commit 736c1c14cfd95495e00abc5a69f2bc31dca5ea09
parent 7ccb4419e8a6a7500130626063ca93810c95223f
Author: Pablo Murad <pablo@pablomurad.com>
Date: Tue, 31 Mar 2026 23:20:18 -0300
fix docker
Diffstat:
20 files changed, 2015 insertions(+), 3351 deletions(-)
diff --git a/.agent/skills b/.agent/skills
@@ -1 +0,0 @@
-Subproject commit 44d6277b698618538f3ae68fa8c803d7e5f07482
diff --git a/lazier/api/main.py b/lazier/api/main.py
@@ -8,7 +8,11 @@ from fastapi import FastAPI
from fastapi.staticfiles import StaticFiles
from fastapi.middleware.cors import CORSMiddleware
from starlette.middleware.sessions import SessionMiddleware
-from dotenv import load_dotenv
+try:
+ from dotenv import load_dotenv
+except ImportError: # pragma: no cover - ambiente sem python-dotenv
+ def load_dotenv():
+ return False
from .routes import router
from .websocket import websocket_router
@@ -76,6 +80,19 @@ def create_app() -> FastAPI:
except Exception as e:
print(f"Aviso: Cache Redis não disponível: {e}")
app.state.cache = None
+
+ # Inicializa persistencia de jobs
+ try:
+ from ..core.jobs import get_job_store
+
+ job_store = get_job_store()
+ recovered = job_store.mark_incomplete_as_interrupted()
+ if recovered:
+ print(f"Aviso: {recovered} job(s) marcados como interrompidos após reinicialização")
+ app.state.job_store = job_store
+ except Exception as e:
+ print(f"ERRO: Falha ao inicializar persistência de jobs: {e}")
+ raise
# Rotas de autenticação (públicas)
app.include_router(auth_router)
diff --git a/lazier/api/routes.py b/lazier/api/routes.py
@@ -1,273 +1,220 @@
"""
-Rotas da API FastAPI
+Rotas da API FastAPI.
"""
+import logging
import os
import uuid
-import asyncio
-import logging
+from datetime import datetime
from pathlib import Path
from typing import List, Optional
-from fastapi import APIRouter, UploadFile, File, HTTPException, BackgroundTasks, Form
-from fastapi.responses import FileResponse, JSONResponse
+
+from fastapi import APIRouter, BackgroundTasks, File, Form, HTTPException, UploadFile
+from fastapi.responses import FileResponse
from pydantic import BaseModel
-from datetime import datetime
-from ..core.cache import get_cache_manager, calculate_file_hash, calculate_url_hash
-from ..core.batch import get_batch_processor
+from ..audio_processor import extract_audio_from_video
from ..core.formats import export
-from ..core.exceptions import (
- YouTubeDownloadError,
- YouTubeVideoUnavailableError,
- YouTubeAccessDeniedError,
- MusicContentError,
-)
+from ..core.jobs import build_job_artifact_path, get_job_store
+from ..core.processing import process_source
from ..core.supported_sites import SUPPORTED_VIDEO_SITES
-from ..utils import validate_input, get_output_filename, is_youtube_url, get_lazier_filename
-from ..downloader import download_youtube_audio, download_video_audio
-from ..audio_processor import prepare_audio_file, extract_audio_from_video
-from ..transcriber import transcribe_audio
-from ..summarizer import summarize_text, summarize_text_file, summarize_web_page, summarize_pdf
-from ..web.extractor import extract_web_content, extract_text_file_content, extract_pdf_content
+from ..core.playlist import is_playlist_url
+from ..utils import AUDIO_EXTENSIONS, TEXT_EXTENSIONS, VIDEO_EXTENSIONS
from .websocket import broadcast_progress
-# Configurar logging
logger = logging.getLogger(__name__)
router = APIRouter()
-# Diretórios
-UPLOAD_DIR = Path(os.getenv('LAZIER_UPLOAD_DIR', '/app/uploads'))
-OUTPUT_DIR = Path(os.getenv('LAZIER_OUTPUT_DIR', '/app/outputs'))
+UPLOAD_DIR = Path(os.getenv("LAZIER_UPLOAD_DIR", "/app/uploads"))
UPLOAD_DIR.mkdir(parents=True, exist_ok=True)
-OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
-
-# Jobs em memória (em produção, usar Redis ou banco de dados)
-jobs: dict = {}
class ProcessRequest(BaseModel):
- """Request para processar URL"""
+ """Request para processar URL."""
+
url: str
format: str = "docx"
- transcribe: bool = True
- summarize: bool = True
+ mode: Optional[str] = None
+ transcribe: Optional[bool] = None
+ summarize: Optional[bool] = None
+
+
+def _resolve_mode(
+ mode: Optional[str],
+ transcribe: Optional[bool],
+ summarize: Optional[bool],
+) -> str:
+ if mode:
+ if mode not in {"transcribe", "summarize"}:
+ raise HTTPException(status_code=400, detail="Modo invalido. Use 'transcribe' ou 'summarize'.")
+ return mode
+
+ if transcribe is None and summarize is None:
+ raise HTTPException(status_code=400, detail="Informe `mode`.")
+
+ if transcribe and not summarize:
+ return "transcribe"
+ if summarize and not transcribe:
+ return "summarize"
+
+ raise HTTPException(
+ status_code=400,
+ detail="A combinacao legada de transcribe/summarize nao aceita mais os dois modos ao mesmo tempo.",
+ )
-class JobStatus(BaseModel):
- """Status de um job"""
- id: str
- status: str
- progress: int
- result_path: Optional[str] = None
- error: Optional[str] = None
+def _job_title(job: dict) -> str:
+ metadata = job.get("metadata", {})
+ if metadata.get("title"):
+ return metadata["title"]
+ if job.get("source_name"):
+ return job["source_name"]
+ if job.get("source_url"):
+ return job["source_url"]
+ if job.get("file_path"):
+ return Path(job["file_path"]).stem
+ return f"Job {job['id']}"
-def process_file_async(
- file_path: str,
- job_id: str,
- output_format: str = "docx",
- should_transcribe: bool = True,
- should_summarize: bool = True
-):
- """Processa arquivo de forma assíncrona"""
+def _progress_updater(job_id: str):
+ store = get_job_store()
+
+ def callback(progress: int, status: str, message: Optional[str] = None) -> None:
+ updates = {"progress": progress, "status": status}
+ if status == "failed":
+ updates["error"] = message
+ store.update_job(job_id, **updates)
+ broadcast_progress(job_id, progress, status, message)
+
+ return callback
+
+
+def _create_job(
+ *,
+ mode: str,
+ output_format: str,
+ source_name: Optional[str] = None,
+ source_url: Optional[str] = None,
+ file_path: Optional[str] = None,
+ input_type: Optional[str] = None,
+) -> dict:
+ store = get_job_store()
+ job_id = str(uuid.uuid4())
+ created_at = datetime.now().isoformat()
+ return store.create_job(
+ {
+ "id": job_id,
+ "mode": mode,
+ "status": "pending",
+ "progress": 0,
+ "input_type": input_type,
+ "source_name": source_name,
+ "source_url": source_url,
+ "file_path": file_path,
+ "format": output_format,
+ "metadata": {},
+ "created_at": created_at,
+ }
+ )
+
+
+def _process_job(job_id: str) -> None:
+ store = get_job_store()
+ job = store.get_job(job_id)
+ if not job:
+ logger.error("Job %s nao encontrado para processamento", job_id)
+ return
+
+ source = job.get("source_url") or job.get("file_path")
+ progress_callback = _progress_updater(job_id)
+
try:
- # Inicializa campos do job se não existirem
- if 'transcription' not in jobs[job_id]:
- jobs[job_id]['transcription'] = None
- jobs[job_id]['summary'] = None
- jobs[job_id]['transcription_path'] = None
- jobs[job_id]['summary_path'] = None
- jobs[job_id]['metadata'] = {}
-
- jobs[job_id]['status'] = 'processing'
- jobs[job_id]['progress'] = 10
- broadcast_progress(job_id, 10, 'processing', 'Iniciando processamento...')
-
- # Valida input
- is_valid, input_type, error_msg = validate_input(file_path)
- if not is_valid:
- raise Exception(error_msg)
-
- jobs[job_id]['progress'] = 20
- broadcast_progress(job_id, 20, 'processing', 'Arquivo validado')
-
- # Determina tipo de processamento
- cache = get_cache_manager()
- file_hash = calculate_file_hash(file_path)
-
- transcription = None
- summary = None
- metadata = {}
- transcription_path = None
- summary_path = None
- transcription_internal = None # Para uso interno quando apenas sumarizar
-
- if input_type in ['audio', 'video']:
- # Processa áudio/vídeo
- # Nota: Vídeos já foram convertidos para áudio no upload
- # Para sumarizar áudio/vídeo, sempre precisa transcrever primeiro
- needs_transcription = should_transcribe or should_summarize
-
- if needs_transcription:
- jobs[job_id]['progress'] = 30
- broadcast_progress(job_id, 30, 'processing', 'Preparando áudio...')
-
- # Verifica cache
- cached = cache.get('transcription', file_hash) if cache else None
- if cached:
- transcription_internal = cached.get('transcription')
- metadata = cached.get('metadata', {})
- broadcast_progress(job_id, 50, 'processing', 'Transcrição encontrada no cache')
- else:
- # Prepara áudio (vídeos já vêm como áudio, então is_video=False)
- # Mantém fallback para vídeo caso algum chegue aqui (não deveria)
- audio_file = prepare_audio_file(file_path, is_video=(input_type == 'video'))
- jobs[job_id]['progress'] = 50
- broadcast_progress(job_id, 50, 'processing', 'Transcrevendo áudio...')
-
- # Transcreve
- transcription_internal = transcribe_audio(audio_file, language='pt', model='whisper-1')
- jobs[job_id]['progress'] = 70
- broadcast_progress(job_id, 70, 'processing', 'Transcrição concluída')
-
- # Salva no cache
- if cache:
- cache.set('transcription', file_hash, {
- 'transcription': transcription_internal,
- 'metadata': metadata,
- 'timestamp': datetime.now().isoformat(),
- })
-
- # Se usuário pediu transcrição, armazena no job
- if should_transcribe:
- transcription = transcription_internal
-
- # Gera arquivo só com transcrição se apenas transcrever
- if transcription and should_transcribe and not should_summarize:
- transcription_path = Path(get_lazier_filename(OUTPUT_DIR, output_format, "_transcription"))
- export(
- transcription=transcription,
- summary=None,
- metadata=metadata,
- output_path=str(transcription_path),
- format_type=output_format
- )
-
- # Sumariza se solicitado
- if should_summarize:
- jobs[job_id]['progress'] = 80
- broadcast_progress(job_id, 80, 'processing', 'Gerando sumário...')
- if transcription_internal:
- text_hash = calculate_url_hash(transcription_internal)
- cached_summary = cache.get('summary', text_hash) if cache else None
-
- if cached_summary:
- summary = cached_summary.get('summary')
- broadcast_progress(job_id, 85, 'processing', 'Sumário encontrado no cache')
- else:
- summary = summarize_text(transcription_internal, model='gpt-4o-mini', language='pt-BR')
- if cache:
- cache.set('summary', text_hash, {
- 'summary': summary,
- 'timestamp': datetime.now().isoformat(),
- })
- jobs[job_id]['progress'] = 90
- broadcast_progress(job_id, 90, 'processing', 'Sumário concluído')
-
- # Gera arquivo só com sumário se apenas sumarizar
- if summary and not should_transcribe:
- summary_path = Path(get_lazier_filename(OUTPUT_DIR, output_format, "_summary"))
- export(
- transcription="",
- summary=summary,
- metadata=metadata,
- output_path=str(summary_path),
- format_type=output_format
- )
-
- elif input_type == 'text' or Path(file_path).suffix.lower() == '.pdf':
- # Processa texto/PDF
- jobs[job_id]['progress'] = 40
- broadcast_progress(job_id, 40, 'processing', 'Extraindo conteúdo do arquivo...')
-
- # Para textos/PDFs, extrai conteúdo (equivalente a transcrição)
- # Mas só armazena se usuário pediu transcrição
- if Path(file_path).suffix.lower() == '.pdf':
- content_data = extract_pdf_content(file_path)
- content_extracted = content_data['content']
- if should_summarize:
- broadcast_progress(job_id, 60, 'processing', 'Sumarizando PDF...')
- summary = summarize_pdf(file_path)
- else:
- content_data = extract_text_file_content(file_path)
- content_extracted = content_data['content']
- if should_summarize:
- broadcast_progress(job_id, 60, 'processing', 'Sumarizando texto...')
- summary = summarize_text_file(file_path)
-
- # Só armazena transcription se usuário pediu
- if should_transcribe:
- transcription = content_extracted
-
- metadata = {'title': content_data.get('title', 'Documento'), 'file_path': file_path}
-
- # Gera arquivo só com sumário se apenas sumarizar
- if should_summarize and summary and not should_transcribe:
- summary_path = Path(get_lazier_filename(OUTPUT_DIR, output_format, "_summary"))
- export(
- transcription="",
- summary=summary,
- metadata=metadata,
- output_path=str(summary_path),
- format_type=output_format
- )
-
- jobs[job_id]['progress'] = 90
- broadcast_progress(job_id, 90, 'processing', 'Conteúdo processado')
-
- # Gera arquivo consolidado apenas se ambos foram solicitados OU se apenas um foi solicitado mas não tem arquivo separado
- # Não gera consolidado quando apenas sumarizar (já tem arquivo separado)
- should_generate_consolidated = False
- if should_transcribe and should_summarize:
- # Ambos solicitados: sempre gera consolidado
- should_generate_consolidated = True
- elif should_transcribe and not should_summarize:
- # Apenas transcrever: gera consolidado se não tem arquivo separado
- should_generate_consolidated = not transcription_path
- elif should_summarize and not should_transcribe:
- # Apenas sumarizar: NÃO gera consolidado (já tem arquivo separado)
- should_generate_consolidated = False
-
- if should_generate_consolidated:
- broadcast_progress(job_id, 95, 'processing', 'Gerando arquivo de saída...')
- output_path = Path(get_lazier_filename(OUTPUT_DIR, output_format))
-
- export(
- transcription=transcription or "",
- summary=summary if should_summarize else None,
- metadata=metadata,
- output_path=str(output_path),
- format_type=output_format
- )
-
- jobs[job_id]['result_path'] = str(output_path)
-
- # Armazena dados separados - só armazena transcription se foi solicitado
- jobs[job_id]['transcription'] = transcription if should_transcribe else None
- jobs[job_id]['summary'] = summary
- jobs[job_id]['transcription_path'] = str(transcription_path) if transcription_path else None
- jobs[job_id]['summary_path'] = str(summary_path) if summary_path else None
- jobs[job_id]['metadata'] = metadata
-
- jobs[job_id]['status'] = 'completed'
- jobs[job_id]['progress'] = 100
- broadcast_progress(job_id, 100, 'completed', 'Processamento concluído')
-
- except Exception as e:
- jobs[job_id]['status'] = 'failed'
- jobs[job_id]['error'] = str(e)
- broadcast_progress(job_id, 0, 'failed', f'Erro: {str(e)}')
+ store.update_job(job_id, status="processing", progress=1, error=None)
+ broadcast_progress(job_id, 1, "processing", "Iniciando processamento...")
+
+ result = process_source(
+ source,
+ mode=job["mode"],
+ output_format=job["format"],
+ run_id=job_id,
+ source_name=job.get("source_name"),
+ created_at=job.get("created_at"),
+ progress_callback=progress_callback,
+ )
+ store.update_job(
+ job_id,
+ status="completed",
+ progress=100,
+ input_type=result.get("input_type", job.get("input_type")),
+ metadata=result.get("metadata", {}),
+ transcription=result.get("transcription"),
+ summary=result.get("summary"),
+ result_path=result.get("result_path"),
+ transcription_path=result.get("transcription_path"),
+ summary_path=result.get("summary_path"),
+ source_name=result.get("source_name") or job.get("source_name"),
+ error=None,
+ )
+ broadcast_progress(job_id, 100, "completed", "Processamento concluido")
+ except Exception as exc:
+ logger.exception("Erro ao processar job %s", job_id)
+ store.update_job(job_id, status="failed", progress=0, error=str(exc))
+ broadcast_progress(job_id, 0, "failed", str(exc))
+
+
+def _ensure_download_file(job: dict, artifact_kind: str) -> Optional[str]:
+ existing_path_key = {
+ "transcription": "transcription_path",
+ "summary": "summary_path",
+ "result": "result_path",
+ }[artifact_kind]
+ existing_path = job.get(existing_path_key)
+ if existing_path and Path(existing_path).exists():
+ return existing_path
+
+ if artifact_kind == "transcription" and job.get("transcription"):
+ output_path = build_job_artifact_path(
+ job_id=job["id"],
+ source_name=job.get("source_name"),
+ format_type=job.get("format", "docx"),
+ artifact_kind="transcription",
+ created_at=job.get("created_at"),
+ )
+ export(
+ transcription=job["transcription"],
+ summary=None,
+ metadata=job.get("metadata", {}),
+ output_path=str(output_path),
+ format_type=job.get("format", "docx"),
+ )
+ get_job_store().update_job(job["id"], transcription_path=str(output_path))
+ return str(output_path)
+
+ if artifact_kind == "summary" and job.get("summary"):
+ output_path = build_job_artifact_path(
+ job_id=job["id"],
+ source_name=job.get("source_name"),
+ format_type=job.get("format", "docx"),
+ artifact_kind="summary",
+ created_at=job.get("created_at"),
+ )
+ export(
+ transcription="",
+ summary=job["summary"],
+ metadata=job.get("metadata", {}),
+ output_path=str(output_path),
+ format_type=job.get("format", "docx"),
+ )
+ get_job_store().update_job(job["id"], summary_path=str(output_path))
+ return str(output_path)
+
+ if artifact_kind == "result":
+ mode = job.get("mode")
+ preferred_key = "transcription" if mode == "transcribe" else "summary"
+ return _ensure_download_file(job, preferred_key)
+
+ return None
@router.post("/upload")
@@ -275,875 +222,197 @@ async def upload_files(
background_tasks: BackgroundTasks,
files: List[UploadFile] = File(...),
format: str = Form("docx"),
- transcribe: bool = Form(True),
- summarize: bool = Form(True)
+ mode: Optional[str] = Form(None),
+ transcribe: Optional[bool] = Form(None),
+ summarize: Optional[bool] = Form(None),
):
- """Upload de arquivos para processamento"""
- from ..utils import VIDEO_EXTENSIONS
-
- job_ids = []
-
+ """Upload de arquivos para processamento."""
+
+ resolved_mode = _resolve_mode(mode, transcribe, summarize)
+ valid_extensions = AUDIO_EXTENSIONS | VIDEO_EXTENSIONS | TEXT_EXTENSIONS | {".pdf"}
+ jobs = []
+
for file in files:
- # Valida tipo de arquivo
ext = Path(file.filename).suffix.lower()
- valid_extensions = {'.mp3', '.wav', '.m4a', '.mp4', '.avi', '.mkv', '.pdf', '.txt', '.md', '.html'}
if ext not in valid_extensions:
- raise HTTPException(status_code=400, detail=f"Tipo de arquivo não suportado: {ext}")
-
- # Salva arquivo
+ raise HTTPException(status_code=400, detail=f"Tipo de arquivo nao suportado: {ext}")
+
file_path = UPLOAD_DIR / f"{uuid.uuid4()}_{file.filename}"
try:
- with open(file_path, 'wb') as f:
- content = await file.read()
- f.write(content)
- except Exception as e:
- logger.error(f"Erro ao salvar arquivo {file.filename}: {e}")
- raise HTTPException(status_code=500, detail=f"Erro ao salvar arquivo: {str(e)}")
-
- # Se for vídeo, extrair áudio imediatamente (sincrono para o upload mas rápido)
+ with open(file_path, "wb") as handle:
+ handle.write(await file.read())
+ except Exception as exc:
+ logger.error("Erro ao salvar arquivo %s: %s", file.filename, exc)
+ raise HTTPException(status_code=500, detail=f"Erro ao salvar arquivo: {exc}") from exc
+
if ext in VIDEO_EXTENSIONS:
try:
- # Extrai áudio do vídeo
- audio_file = extract_audio_from_video(str(file_path))
- # Deleta vídeo original
- file_path.unlink()
- # Usa áudio como arquivo principal
- file_path = Path(audio_file)
- except Exception as e:
- # Se falhar, mantém vídeo original e reporta erro
- logger.error(f"Erro ao extrair áudio do vídeo {file.filename}: {e}")
- raise HTTPException(
- status_code=500,
- detail=f"Erro ao extrair áudio do vídeo: {str(e)}"
- )
-
- # Cria job
- job_id = str(uuid.uuid4())
- jobs[job_id] = {
- 'id': job_id,
- 'status': 'pending',
- 'progress': 0,
- 'file_path': str(file_path),
- 'format': format,
- 'transcribe': transcribe,
- 'summarize': summarize,
- 'transcription': None,
- 'summary': None,
- 'transcription_path': None,
- 'summary_path': None,
- 'metadata': {},
- 'created_at': datetime.now().isoformat(),
- }
-
- # Processa em background REAL (usando BackgroundTasks)
- background_tasks.add_task(process_file_async, str(file_path), job_id, format, transcribe, summarize)
-
- job_ids.append(job_id)
-
- return {"job_ids": job_ids, "message": f"{len(job_ids)} arquivo(s) enviado(s)"}
+ audio_path = extract_audio_from_video(str(file_path))
+ file_path.unlink(missing_ok=True)
+ file_path = Path(audio_path)
+ except Exception as exc:
+ logger.error("Erro ao extrair audio do video %s: %s", file.filename, exc)
+ raise HTTPException(status_code=500, detail=f"Erro ao extrair audio do video: {exc}") from exc
+
+ job = _create_job(
+ mode=resolved_mode,
+ output_format=format,
+ source_name=file.filename,
+ file_path=str(file_path),
+ input_type="file",
+ )
+ background_tasks.add_task(_process_job, job["id"])
+ jobs.append(job["id"])
+
+ return {"job_ids": jobs, "message": f"{len(jobs)} arquivo(s) enviado(s)"}
@router.post("/process")
async def process_url(request: ProcessRequest, background_tasks: BackgroundTasks):
- """Processa URL (YouTube ou página web)"""
- job_id = str(uuid.uuid4())
-
- jobs[job_id] = {
- 'id': job_id,
- 'status': 'pending',
- 'progress': 0,
- 'url': request.url,
- 'format': request.format,
- 'transcribe': request.transcribe,
- 'summarize': request.summarize,
- 'transcription': None,
- 'summary': None,
- 'transcription_path': None,
- 'summary_path': None,
- 'metadata': {},
- 'created_at': datetime.now().isoformat(),
- }
-
- # Processa em background
- if is_youtube_url(request.url):
- background_tasks.add_task(process_youtube_async, request.url, job_id, request.format, request.transcribe, request.summarize)
- else:
- # Tenta processar como vídeo de qualquer site; se falhar, fallback para página web
- background_tasks.add_task(process_video_url_async, request.url, job_id, request.format, request.transcribe, request.summarize)
-
- return {"job_id": job_id, "status": "processing"}
-
-
-def process_youtube_async(url: str, job_id: str, output_format: str, should_transcribe: bool, should_summarize: bool):
- """Processa vídeo do YouTube"""
- try:
- # Inicializa campos do job se não existirem
- if 'transcription' not in jobs[job_id]:
- jobs[job_id]['transcription'] = None
- jobs[job_id]['summary'] = None
- jobs[job_id]['transcription_path'] = None
- jobs[job_id]['summary_path'] = None
- jobs[job_id]['metadata'] = {}
-
- jobs[job_id]['status'] = 'processing'
- jobs[job_id]['progress'] = 10
- broadcast_progress(job_id, 10, 'processing', 'Iniciando processamento do YouTube...')
-
- cache = get_cache_manager()
- url_hash = calculate_url_hash(url)
-
- transcription = None
- summary = None
- transcription_path = None
- summary_path = None
- transcription_internal = None # Para uso interno quando apenas sumarizar
-
- # Para YouTube, sempre precisa transcrever para sumarizar
- needs_transcription = should_transcribe or should_summarize
-
- # Verifica cache
- cached = cache.get('youtube', url_hash) if cache else None
- if cached:
- transcription_internal = cached.get('transcription')
- summary = cached.get('summary')
- metadata = cached.get('metadata', {})
- # Se usuário pediu transcrição, armazena
- if should_transcribe:
- transcription = transcription_internal
- if transcription_internal and (not should_summarize or summary):
- jobs[job_id]['progress'] = 100
- broadcast_progress(job_id, 100, 'completed', 'Dados encontrados no cache')
- else:
- metadata = {}
- if needs_transcription:
- # Download
- broadcast_progress(job_id, 20, 'processing', 'Baixando vídeo do YouTube...')
- try:
- audio_file, metadata = download_youtube_audio(url, str(UPLOAD_DIR))
- except MusicContentError as e:
- logger.error(f"Conteúdo detectado como música (job {job_id}): {str(e)}")
- user_message = "Conteúdo detectado como música não é processado pelo Lazier."
- jobs[job_id]['status'] = 'failed'
- jobs[job_id]['error'] = user_message
- broadcast_progress(job_id, 0, 'failed', user_message)
- return
- except YouTubeVideoUnavailableError as e:
- logger.error(f"Vídeo não disponível (job {job_id}): {str(e)}")
- user_message = (
- "Erro: O vídeo não está disponível. "
- "Pode ser que o vídeo seja privado, tenha sido removido ou esteja bloqueado por região."
- )
- if e.error_code:
- user_message += f" (Código: {e.error_code})"
- jobs[job_id]['status'] = 'failed'
- jobs[job_id]['error'] = user_message
- broadcast_progress(job_id, 0, 'failed', user_message)
- return
- except YouTubeAccessDeniedError as e:
- logger.error(f"Acesso negado ao vídeo (job {job_id}): {str(e)}")
- user_message = (
- "Erro ao baixar vídeo do YouTube: O YouTube está bloqueando o download. "
- "Isso pode ser temporário. Tente novamente em alguns minutos ou verifique se o vídeo está disponível."
- )
- if e.error_code:
- user_message += f" (Código: {e.error_code})"
- jobs[job_id]['status'] = 'failed'
- jobs[job_id]['error'] = user_message
- broadcast_progress(job_id, 0, 'failed', user_message)
- return
- except YouTubeDownloadError as e:
- logger.error(f"Erro ao baixar vídeo do YouTube (job {job_id}): {str(e)}")
- user_message = f"Erro ao baixar vídeo do YouTube: {str(e)}"
- if e.error_code:
- user_message += f" (Código: {e.error_code})"
- jobs[job_id]['status'] = 'failed'
- jobs[job_id]['error'] = user_message
- broadcast_progress(job_id, 0, 'failed', user_message)
- return
- except Exception as e:
- logger.error(f"Erro inesperado ao baixar vídeo (job {job_id}): {str(e)}")
- user_message = f"Erro inesperado ao baixar vídeo: {str(e)}"
- jobs[job_id]['status'] = 'failed'
- jobs[job_id]['error'] = user_message
- broadcast_progress(job_id, 0, 'failed', user_message)
- return
-
- jobs[job_id]['progress'] = 30
- broadcast_progress(job_id, 30, 'processing', 'Transcrevendo áudio...')
-
- # Transcreve
- try:
- transcription_internal = transcribe_audio(audio_file, language='pt', model='whisper-1')
- except Exception as e:
- logger.error(f"Erro ao transcrever áudio (job {job_id}): {str(e)}")
- jobs[job_id]['status'] = 'failed'
- jobs[job_id]['error'] = f"Erro ao transcrever áudio: {str(e)}"
- broadcast_progress(job_id, 0, 'failed', f"Erro ao transcrever áudio: {str(e)}")
- return
-
- jobs[job_id]['progress'] = 60
- broadcast_progress(job_id, 60, 'processing', 'Transcrição concluída')
-
- # Se usuário pediu transcrição, armazena no job
- if should_transcribe:
- transcription = transcription_internal
-
- # Gera arquivo só com transcrição se apenas transcrever
- if transcription and should_transcribe and not should_summarize:
- transcription_path = Path(get_lazier_filename(OUTPUT_DIR, output_format, "_transcription"))
- export(
- transcription=transcription,
- summary=None,
- metadata=metadata,
- output_path=str(transcription_path),
- format_type=output_format
- )
-
- # Sumariza se solicitado
- if should_summarize:
- if transcription_internal:
- broadcast_progress(job_id, 70, 'processing', 'Gerando sumário...')
- summary = summarize_text(transcription_internal, model='gpt-4o-mini', language='pt-BR')
-
- jobs[job_id]['progress'] = 80
- broadcast_progress(job_id, 80, 'processing', 'Sumário concluído')
-
- # Gera arquivo só com sumário se apenas sumarizar
- if summary and not should_transcribe:
- summary_path = Path(get_lazier_filename(OUTPUT_DIR, output_format, "_summary"))
- export(
- transcription="",
- summary=summary,
- metadata=metadata,
- output_path=str(summary_path),
- format_type=output_format
- )
-
- # Salva cache
- if cache and transcription_internal:
- cache.set('youtube', url_hash, {
- 'transcription': transcription_internal,
- 'summary': summary,
- 'metadata': metadata,
- 'timestamp': datetime.now().isoformat(),
- })
-
- # Garantir link do vídeo no cabeçalho do documento
- if metadata is None:
- metadata = {}
- if not metadata.get('webpage_url'):
- metadata = {**metadata, 'webpage_url': url}
-
- # Gera arquivo consolidado apenas se ambos foram solicitados OU se apenas transcrever (sem arquivo separado)
- should_generate_consolidated = False
- if should_transcribe and should_summarize:
- # Ambos solicitados: sempre gera consolidado
- should_generate_consolidated = True
- elif should_transcribe and not should_summarize:
- # Apenas transcrever: gera consolidado se não tem arquivo separado
- should_generate_consolidated = not transcription_path
- elif should_summarize and not should_transcribe:
- # Apenas sumarizar: NÃO gera consolidado (já tem arquivo separado)
- should_generate_consolidated = False
-
- if should_generate_consolidated:
- broadcast_progress(job_id, 90, 'processing', 'Gerando arquivo de saída...')
- output_path = Path(get_lazier_filename(OUTPUT_DIR, output_format))
-
- export(
- transcription=transcription or "",
- summary=summary if should_summarize else None,
- metadata=metadata,
- output_path=str(output_path),
- format_type=output_format
- )
-
- jobs[job_id]['result_path'] = str(output_path)
-
- # Armazena dados separados - só armazena transcription se foi solicitado
- jobs[job_id]['transcription'] = transcription if should_transcribe else None
- jobs[job_id]['summary'] = summary
- jobs[job_id]['transcription_path'] = str(transcription_path) if transcription_path else None
- jobs[job_id]['summary_path'] = str(summary_path) if summary_path else None
- jobs[job_id]['metadata'] = metadata
-
- jobs[job_id]['status'] = 'completed'
- jobs[job_id]['progress'] = 100
- broadcast_progress(job_id, 100, 'completed', 'Processamento concluído')
-
- except (YouTubeVideoUnavailableError, YouTubeAccessDeniedError, YouTubeDownloadError) as e:
- # Erros de download já foram tratados acima, mas pode haver outros casos
- error_msg = str(e)
- logger.error(f"Erro no processamento do YouTube (job {job_id}): {error_msg}")
-
- # Se o erro já foi tratado acima (download/transcrição), não sobrescreve
- if jobs[job_id].get('status') != 'failed':
- user_message = f"Erro no processamento: {error_msg}"
- if hasattr(e, 'error_code') and e.error_code:
- user_message += f" (Código: {e.error_code})"
- jobs[job_id]['status'] = 'failed'
- jobs[job_id]['error'] = user_message
- broadcast_progress(job_id, 0, 'failed', user_message)
- except Exception as e:
- error_msg = str(e)
- logger.error(f"Erro inesperado no processamento do YouTube (job {job_id}): {error_msg}")
-
- # Se o erro já foi tratado acima (download/transcrição), não sobrescreve
- if jobs[job_id].get('status') != 'failed':
- jobs[job_id]['status'] = 'failed'
- jobs[job_id]['error'] = error_msg
- broadcast_progress(job_id, 0, 'failed', f'Erro: {error_msg}')
-
-
-def process_video_url_async(url: str, job_id: str, output_format: str, should_transcribe: bool, should_summarize: bool):
- """Tenta processar URL como vídeo (TED, Reddit, Vimeo, etc.); se falhar, fallback para página web."""
- try:
- broadcast_progress(job_id, 10, 'processing', 'Tentando extrair vídeo/áudio da URL...')
- try:
- audio_file, metadata = download_video_audio(url, str(UPLOAD_DIR))
- except MusicContentError as e:
- logger.error(f"Conteúdo detectado como música (job {job_id}): {str(e)}")
- user_message = "Conteúdo detectado como música não é processado pelo Lazier."
- jobs[job_id]['status'] = 'failed'
- jobs[job_id]['error'] = user_message
- broadcast_progress(job_id, 0, 'failed', user_message)
- return
- except Exception as e:
- logger.info(f"URL não é vídeo ou falha ao baixar (job {job_id}), fallback para página web: {e}")
- process_web_async(url, job_id, output_format, should_transcribe, should_summarize)
- return
- _run_video_pipeline(
- job_id=job_id,
- audio_file=audio_file,
- metadata=metadata,
- output_format=output_format,
- should_transcribe=should_transcribe,
- should_summarize=should_summarize,
- cache_prefix='video',
- url_hash=calculate_url_hash(url),
- url=url,
- )
- except Exception as e:
- if jobs[job_id].get('status') != 'failed':
- jobs[job_id]['status'] = 'failed'
- jobs[job_id]['error'] = str(e)
- broadcast_progress(job_id, 0, 'failed', str(e))
+ """Processa URL."""
+ resolved_mode = _resolve_mode(request.mode, request.transcribe, request.summarize)
+ if is_playlist_url(request.url):
+ raise HTTPException(
+ status_code=400,
+ detail="Playlists continuam disponiveis apenas no fluxo explicito da CLI.",
+ )
-def _run_video_pipeline(
- job_id: str,
- audio_file: str,
- metadata: dict,
- output_format: str,
- should_transcribe: bool,
- should_summarize: bool,
- cache_prefix: str,
- url_hash: str,
- url: str = "",
-):
- """Pipeline comum: transcrever, sumarizar, exportar, cache. Usado por YouTube e vídeo genérico."""
- if 'transcription' not in jobs[job_id]:
- jobs[job_id]['transcription'] = None
- jobs[job_id]['summary'] = None
- jobs[job_id]['transcription_path'] = None
- jobs[job_id]['summary_path'] = None
- jobs[job_id]['metadata'] = {}
- cache = get_cache_manager()
- cached = cache.get(cache_prefix, url_hash) if cache else None
- transcription = None
- summary = None
- transcription_path = None
- summary_path = None
- transcription_internal = None
- needs_transcription = should_transcribe or should_summarize
- if cached:
- transcription_internal = cached.get('transcription')
- summary = cached.get('summary')
- metadata = cached.get('metadata', metadata)
- # Garantir link do vídeo no cabeçalho do documento
- if metadata is None:
- metadata = {}
- if url and not metadata.get('webpage_url'):
- metadata = {**metadata, 'webpage_url': url}
- if should_transcribe:
- transcription = transcription_internal
- if transcription_internal and (not should_summarize or summary):
- jobs[job_id]['progress'] = 100
- broadcast_progress(job_id, 100, 'completed', 'Dados encontrados no cache')
- else:
- if needs_transcription:
- broadcast_progress(job_id, 30, 'processing', 'Transcrevendo áudio...')
- transcription_internal = transcribe_audio(audio_file, language='pt', model='whisper-1')
- broadcast_progress(job_id, 60, 'processing', 'Transcrição concluída')
- if should_transcribe:
- transcription = transcription_internal
- if transcription and should_transcribe and not should_summarize:
- transcription_path = Path(get_lazier_filename(OUTPUT_DIR, output_format, "_transcription"))
- export(transcription=transcription, summary=None, metadata=metadata, output_path=str(transcription_path), format_type=output_format)
- if should_summarize and transcription_internal:
- broadcast_progress(job_id, 70, 'processing', 'Gerando sumário...')
- summary = summarize_text(transcription_internal, model='gpt-4o-mini', language='pt-BR')
- broadcast_progress(job_id, 80, 'processing', 'Sumário concluído')
- if summary and not should_transcribe:
- summary_path = Path(get_lazier_filename(OUTPUT_DIR, output_format, "_summary"))
- export(transcription="", summary=summary, metadata=metadata, output_path=str(summary_path), format_type=output_format)
- if cache and transcription_internal:
- cache.set(cache_prefix, url_hash, {
- 'transcription': transcription_internal,
- 'summary': summary,
- 'metadata': metadata,
- 'timestamp': datetime.now().isoformat(),
- })
- # Garantir link do vídeo no cabeçalho do documento (também quando não veio do cache)
- if metadata is None:
- metadata = {}
- if url and not metadata.get('webpage_url'):
- metadata = {**metadata, 'webpage_url': url}
- should_generate_consolidated = (should_transcribe and should_summarize) or (should_transcribe and not should_summarize and not transcription_path)
- if should_generate_consolidated:
- broadcast_progress(job_id, 90, 'processing', 'Gerando arquivo de saída...')
- output_path = Path(get_lazier_filename(OUTPUT_DIR, output_format))
- export(transcription=transcription or "", summary=summary if should_summarize else None, metadata=metadata, output_path=str(output_path), format_type=output_format)
- jobs[job_id]['result_path'] = str(output_path)
- jobs[job_id]['transcription'] = transcription if should_transcribe else None
- jobs[job_id]['summary'] = summary
- jobs[job_id]['transcription_path'] = str(transcription_path) if transcription_path else None
- jobs[job_id]['summary_path'] = str(summary_path) if summary_path else None
- jobs[job_id]['metadata'] = metadata
- jobs[job_id]['status'] = 'completed'
- jobs[job_id]['progress'] = 100
- broadcast_progress(job_id, 100, 'completed', 'Processamento concluído')
-
-
-def process_web_async(url: str, job_id: str, output_format: str, should_transcribe: bool, should_summarize: bool):
- """Processa página web"""
- try:
- # Inicializa campos do job se não existirem
- if 'transcription' not in jobs[job_id]:
- jobs[job_id]['transcription'] = None
- jobs[job_id]['summary'] = None
- jobs[job_id]['transcription_path'] = None
- jobs[job_id]['summary_path'] = None
- jobs[job_id]['metadata'] = {}
-
- jobs[job_id]['status'] = 'processing'
- jobs[job_id]['progress'] = 20
- broadcast_progress(job_id, 20, 'processing', 'Iniciando processamento da página web...')
-
- cache = get_cache_manager()
- url_hash = calculate_url_hash(url)
-
- content = None
- summary = None
- transcription_path = None
- summary_path = None
- metadata = {}
-
- # Verifica cache
- cached = cache.get('web', url_hash) if cache else None
- needs_processing = True
-
- if cached:
- cached_content = cached.get('content')
- cached_summary = cached.get('summary')
- cached_metadata = cached.get('metadata', {})
-
- # Usa dados do cache se disponíveis e necessários
- if should_transcribe and cached_content:
- content = cached_content
- if not metadata:
- metadata = cached_metadata
- if should_summarize and cached_summary:
- summary = cached_summary
- if not metadata:
- metadata = cached_metadata
-
- # Verifica se tem tudo que precisa no cache
- has_all_needed = True
- if should_transcribe and not content:
- has_all_needed = False
- if should_summarize and not summary:
- has_all_needed = False
-
- if has_all_needed:
- needs_processing = False
- jobs[job_id]['progress'] = 100
- broadcast_progress(job_id, 100, 'completed', 'Dados encontrados no cache')
-
- if needs_processing:
- # Se precisa transcrever e não tem no cache, extrai conteúdo completo
- if should_transcribe and not content:
- broadcast_progress(job_id, 30, 'processing', 'Extraindo conteúdo da página...')
- content_data = extract_web_content(url)
- content = content_data['content']
- metadata = {'title': content_data.get('title', 'Página Web'), 'webpage_url': url}
- jobs[job_id]['progress'] = 50
- broadcast_progress(job_id, 50, 'processing', 'Conteúdo extraído')
-
- # Sumariza se solicitado
- if should_summarize:
- broadcast_progress(job_id, 60, 'processing', 'Gerando sumário...')
- # Se já tem conteúdo extraído, usa ele. Senão, summarize_web_page extrai internamente
- if content:
- summary = summarize_text(content, model='gpt-4o-mini', language='pt-BR')
- else:
- summary = summarize_web_page(url)
- # Se não tinha conteúdo antes, pega metadados da extração interna
- if not metadata:
- content_data = extract_web_content(url)
- metadata = {'title': content_data.get('title', 'Página Web'), 'webpage_url': url}
-
- jobs[job_id]['progress'] = 80
- broadcast_progress(job_id, 80, 'processing', 'Sumário concluído')
-
- # Gera arquivo só com sumário se apenas sumarizar
- if summary and not should_transcribe:
- summary_path = Path(get_lazier_filename(OUTPUT_DIR, output_format, "_summary"))
- export(
- transcription="",
- summary=summary,
- metadata=metadata,
- output_path=str(summary_path),
- format_type=output_format
- )
-
- # Se apenas transcrever, gera arquivo só com transcrição
- if should_transcribe and content and not should_summarize:
- transcription_path = Path(get_lazier_filename(OUTPUT_DIR, output_format, "_transcription"))
- export(
- transcription=content,
- summary=None,
- metadata=metadata,
- output_path=str(transcription_path),
- format_type=output_format
- )
-
- # Salva cache apenas se extraiu conteúdo ou gerou sumário
- if cache and (content or summary):
- cache.set('web', url_hash, {
- 'content': content,
- 'summary': summary,
- 'metadata': metadata,
- 'timestamp': datetime.now().isoformat(),
- })
-
- # Gera arquivo consolidado apenas se ambos foram solicitados OU se apenas transcrever (sem arquivo separado)
- should_generate_consolidated = False
- if should_transcribe and should_summarize:
- # Ambos solicitados: sempre gera consolidado
- should_generate_consolidated = True
- elif should_transcribe and not should_summarize:
- # Apenas transcrever: gera consolidado se não tem arquivo separado
- should_generate_consolidated = not transcription_path
- elif should_summarize and not should_transcribe:
- # Apenas sumarizar: NÃO gera consolidado (já tem arquivo separado)
- should_generate_consolidated = False
-
- if should_generate_consolidated:
- broadcast_progress(job_id, 90, 'processing', 'Gerando arquivo de saída...')
- output_path = Path(get_lazier_filename(OUTPUT_DIR, output_format))
-
- export(
- transcription=content or "",
- summary=summary if should_summarize else None,
- metadata=metadata,
- output_path=str(output_path),
- format_type=output_format
- )
-
- jobs[job_id]['result_path'] = str(output_path)
-
- # Armazena dados separados - só armazena transcription se foi solicitado
- jobs[job_id]['transcription'] = content if should_transcribe else None
- jobs[job_id]['summary'] = summary
- jobs[job_id]['transcription_path'] = str(transcription_path) if transcription_path else None
- jobs[job_id]['summary_path'] = str(summary_path) if summary_path else None
- jobs[job_id]['metadata'] = metadata
-
- jobs[job_id]['status'] = 'completed'
- jobs[job_id]['progress'] = 100
- broadcast_progress(job_id, 100, 'completed', 'Processamento concluído')
-
- except Exception as e:
- jobs[job_id]['status'] = 'failed'
- jobs[job_id]['error'] = str(e)
- broadcast_progress(job_id, 0, 'failed', f'Erro: {str(e)}')
+ job = _create_job(
+ mode=resolved_mode,
+ output_format=request.format,
+ source_name=request.url,
+ source_url=request.url,
+ input_type="url",
+ )
+ background_tasks.add_task(_process_job, job["id"])
+ return {"job_id": job["id"], "status": "processing", "mode": resolved_mode}
@router.get("/jobs/{job_id}")
async def get_job_status(job_id: str):
- """Retorna status de um job"""
- if job_id not in jobs:
- raise HTTPException(status_code=404, detail="Job não encontrado")
-
- job = jobs[job_id]
+ """Retorna status de um job."""
+
+ job = get_job_store().get_job(job_id)
+ if not job:
+ raise HTTPException(status_code=404, detail="Job nao encontrado")
+
return {
- "id": job['id'],
- "status": job['status'],
- "progress": job.get('progress', 0),
- "result_path": job.get('result_path'),
- "transcription_path": job.get('transcription_path'),
- "summary_path": job.get('summary_path'),
- "has_transcription": bool(job.get('transcription')),
- "has_summary": bool(job.get('summary')),
- "error": job.get('error'),
+ "id": job["id"],
+ "mode": job["mode"],
+ "status": job["status"],
+ "progress": job.get("progress", 0),
+ "result_path": job.get("result_path"),
+ "transcription_path": job.get("transcription_path"),
+ "summary_path": job.get("summary_path"),
+ "has_transcription": bool(job.get("transcription")),
+ "has_summary": bool(job.get("summary")),
+ "error": job.get("error"),
}
@router.get("/jobs/{job_id}/details")
async def get_job_details(job_id: str):
- """Retorna detalhes completos de um job (transcrição, sumário, metadados)"""
- if job_id not in jobs:
- raise HTTPException(status_code=404, detail="Job não encontrado")
-
- job = jobs[job_id]
- if job['status'] != 'completed':
- raise HTTPException(status_code=400, detail="Job ainda não concluído")
-
+ """Retorna detalhes completos de um job."""
+
+ job = get_job_store().get_job(job_id)
+ if not job:
+ raise HTTPException(status_code=404, detail="Job nao encontrado")
+
+ if job["status"] not in {"completed", "interrupted"}:
+ raise HTTPException(status_code=400, detail="Job ainda nao concluido")
+
return {
- "id": job['id'],
- "transcription": job.get('transcription'),
- "summary": job.get('summary'),
- "metadata": job.get('metadata', {}),
- "format": job.get('format', 'docx'),
- "result_path": job.get('result_path'),
- "transcription_path": job.get('transcription_path'),
- "summary_path": job.get('summary_path'),
+ "id": job["id"],
+ "mode": job["mode"],
+ "transcription": job.get("transcription"),
+ "summary": job.get("summary"),
+ "metadata": job.get("metadata", {}),
+ "format": job.get("format", "docx"),
+ "result_path": job.get("result_path"),
+ "transcription_path": job.get("transcription_path"),
+ "summary_path": job.get("summary_path"),
}
@router.get("/jobs/{job_id}/transcription")
async def download_transcription(job_id: str):
- """Download apenas da transcrição"""
- if job_id not in jobs:
- logger.error(f"Job {job_id} não encontrado para download de transcrição")
- raise HTTPException(status_code=404, detail="Job não encontrado")
-
- job = jobs[job_id]
- if job['status'] != 'completed':
- raise HTTPException(status_code=400, detail="Job ainda não concluído")
-
- transcription_path = job.get('transcription_path')
- if transcription_path and Path(transcription_path).exists():
- logger.info(f"Enviando transcrição de arquivo: {transcription_path}")
- filename = Path(transcription_path).name
- return FileResponse(
- transcription_path,
- media_type='application/octet-stream',
- filename=filename,
- headers={"Content-Disposition": f'attachment; filename="{filename}"'}
- )
-
- # Se não tem arquivo separado, gera um temporário
- transcription = job.get('transcription')
- if not transcription:
- logger.error(f"Transcrição não disponível para job {job_id}")
- raise HTTPException(status_code=404, detail="Transcrição não disponível")
-
- # Gera arquivo temporário
- output_format = job.get('format', 'txt')
- temp_path = OUTPUT_DIR / f"{job_id}_transcription_temp.{output_format}"
- logger.info(f"Gerando arquivo temporário de transcrição: {temp_path}")
- try:
- export(
- transcription=transcription,
- summary=None,
- metadata=job.get('metadata', {}),
- output_path=str(temp_path),
- format_type=output_format
- )
- logger.info(f"Arquivo de transcrição gerado com sucesso: {temp_path}")
- except Exception as e:
- logger.error(f"Erro ao gerar arquivo de transcrição: {e}")
- raise HTTPException(status_code=500, detail=f"Erro ao gerar arquivo: {str(e)}")
-
- filename = f"transcription.{output_format}"
- return FileResponse(
- str(temp_path),
- media_type='application/octet-stream',
- filename=filename,
- headers={"Content-Disposition": f'attachment; filename="{filename}"'}
- )
+ """Download da transcricao."""
+
+ job = get_job_store().get_job(job_id)
+ if not job:
+ raise HTTPException(status_code=404, detail="Job nao encontrado")
+
+ download_path = _ensure_download_file(job, "transcription")
+ if not download_path:
+ raise HTTPException(status_code=404, detail="Transcricao nao disponivel")
+
+ filename = Path(download_path).name
+ return FileResponse(download_path, media_type="application/octet-stream", filename=filename)
@router.get("/jobs/{job_id}/summary")
async def download_summary(job_id: str):
- """Download apenas do sumário"""
- if job_id not in jobs:
- logger.error(f"Job {job_id} não encontrado para download de sumário")
- raise HTTPException(status_code=404, detail="Job não encontrado")
-
- job = jobs[job_id]
- if job['status'] != 'completed':
- raise HTTPException(status_code=400, detail="Job ainda não concluído")
-
- summary_path = job.get('summary_path')
- if summary_path and Path(summary_path).exists():
- logger.info(f"Enviando sumário de arquivo: {summary_path}")
- filename = Path(summary_path).name
- return FileResponse(
- summary_path,
- media_type='application/octet-stream',
- filename=filename,
- headers={"Content-Disposition": f'attachment; filename="{filename}"'}
- )
-
- # Se não tem arquivo separado, gera um temporário
- summary = job.get('summary')
- if not summary:
- logger.error(f"Sumário não disponível para job {job_id}")
- raise HTTPException(status_code=404, detail="Sumário não disponível")
-
- # Gera arquivo temporário
- output_format = job.get('format', 'txt')
- temp_path = OUTPUT_DIR / f"{job_id}_summary_temp.{output_format}"
- logger.info(f"Gerando arquivo temporário de sumário: {temp_path}")
- try:
- export(
- transcription="",
- summary=summary,
- metadata=job.get('metadata', {}),
- output_path=str(temp_path),
- format_type=output_format
- )
- logger.info(f"Arquivo de sumário gerado com sucesso: {temp_path}")
- except Exception as e:
- logger.error(f"Erro ao gerar arquivo de sumário: {e}")
- raise HTTPException(status_code=500, detail=f"Erro ao gerar arquivo: {str(e)}")
-
- filename = f"summary.{output_format}"
- return FileResponse(
- str(temp_path),
- media_type='application/octet-stream',
- filename=filename,
- headers={"Content-Disposition": f'attachment; filename="{filename}"'}
- )
+ """Download do sumario."""
+
+ job = get_job_store().get_job(job_id)
+ if not job:
+ raise HTTPException(status_code=404, detail="Job nao encontrado")
+
+ download_path = _ensure_download_file(job, "summary")
+ if not download_path:
+ raise HTTPException(status_code=404, detail="Sumario nao disponivel")
+
+ filename = Path(download_path).name
+ return FileResponse(download_path, media_type="application/octet-stream", filename=filename)
@router.get("/jobs/{job_id}/download")
async def download_result(job_id: str):
- """Download do resultado de um job"""
- if job_id not in jobs:
- logger.error(f"Job {job_id} não encontrado")
- raise HTTPException(status_code=404, detail="Job não encontrado")
-
- job = jobs[job_id]
- if job['status'] != 'completed':
- logger.warning(f"Job {job_id} ainda não concluído, status: {job['status']}")
- raise HTTPException(status_code=400, detail="Job ainda não concluído")
-
- result_path = job.get('result_path')
- logger.info(f"Tentando baixar job {job_id}, result_path: {result_path}")
-
- if not result_path:
- logger.error(f"Job {job_id} não tem result_path definido")
- # Tentar gerar arquivo se não existir mas job está completo
- if job.get('transcription') or job.get('summary'):
- logger.info(f"Regenerando arquivo para job {job_id}")
- output_format = job.get('format', 'docx')
- output_path = Path(get_lazier_filename(OUTPUT_DIR, output_format))
- try:
- export(
- transcription=job.get('transcription') or "",
- summary=job.get('summary') if job.get('summarize') else None,
- metadata=job.get('metadata', {}),
- output_path=str(output_path),
- format_type=output_format
- )
- result_path = str(output_path)
- job['result_path'] = result_path
- logger.info(f"Arquivo regenerado: {result_path}")
- except Exception as e:
- logger.error(f"Erro ao regenerar arquivo: {e}")
- raise HTTPException(status_code=500, detail=f"Erro ao gerar arquivo: {str(e)}")
- else:
- raise HTTPException(status_code=404, detail="Arquivo de resultado não encontrado e não há dados para regenerar")
-
- result_path_obj = Path(result_path)
- if not result_path_obj.exists():
- logger.error(f"Arquivo não existe: {result_path}")
- # Tentar caminho absoluto se relativo falhou
- if not result_path_obj.is_absolute():
- result_path_obj = OUTPUT_DIR / result_path_obj.name
- if result_path_obj.exists():
- logger.info(f"Arquivo encontrado em caminho alternativo: {result_path_obj}")
- result_path = str(result_path_obj)
- else:
- raise HTTPException(status_code=404, detail=f"Arquivo de resultado não encontrado: {result_path}")
- else:
- raise HTTPException(status_code=404, detail=f"Arquivo de resultado não encontrado: {result_path}")
-
- logger.info(f"Enviando arquivo: {result_path}")
- filename = result_path_obj.name
- return FileResponse(
- result_path,
- media_type='application/octet-stream',
- filename=filename,
- headers={"Content-Disposition": f'attachment; filename="{filename}"'}
- )
+ """Download do artefato principal do job."""
+
+ job = get_job_store().get_job(job_id)
+ if not job:
+ raise HTTPException(status_code=404, detail="Job nao encontrado")
+ download_path = _ensure_download_file(job, "result")
+ if not download_path:
+ raise HTTPException(status_code=404, detail="Arquivo de resultado nao encontrado")
-def _get_job_title(job: dict) -> str:
- """Extrai título descritivo de um job"""
- metadata = job.get('metadata', {})
-
- # Prioridade 1: Título dos metadados (YouTube, PDF, Web)
- if metadata.get('title'):
- return metadata['title']
-
- # Prioridade 2: Nome do arquivo (sem extensão)
- if job.get('file_path'):
- return Path(job['file_path']).stem
-
- # Prioridade 3: URL (para YouTube/Web sem título)
- if job.get('url'):
- # Para YouTube, pode tentar extrair título da URL
- if 'youtube.com' in job['url'] or 'youtu.be' in job['url']:
- return "Vídeo do YouTube"
- return job['url']
-
- # Fallback
- return f"Job {job.get('id', 'desconhecido')}"
+ filename = Path(download_path).name
+ return FileResponse(download_path, media_type="application/octet-stream", filename=filename)
@router.get("/history")
async def get_history():
- """Retorna histórico de jobs com títulos descritivos"""
- # Formata jobs para incluir campos necessários
- formatted_jobs = []
- for job in jobs.values():
- formatted_job = {
- 'id': job.get('id'),
- 'status': job.get('status'),
- 'progress': job.get('progress', 0),
- 'title': _get_job_title(job), # Título descritivo
- 'url': job.get('url'),
- 'file_path': job.get('file_path'),
- 'format': job.get('format', 'docx'),
- 'result_path': job.get('result_path'),
- 'transcription_path': job.get('transcription_path'),
- 'summary_path': job.get('summary_path'),
- 'has_transcription': bool(job.get('transcription')),
- 'has_summary': bool(job.get('summary')),
- 'error': job.get('error'),
- 'created_at': job.get('created_at'),
- }
- formatted_jobs.append(formatted_job)
-
- return {"jobs": formatted_jobs}
-
-
-@router.delete("/cache")
-async def clear_cache():
- """Limpa cache"""
- cache = get_cache_manager()
- if cache:
- count = cache.clear_all()
- return {"message": f"Cache limpo: {count} chaves removidas"}
- return {"message": "Cache não disponível"}
+ """Retorna historico persistido de jobs."""
+
+ jobs = []
+ for job in get_job_store().list_jobs():
+ jobs.append(
+ {
+ "id": job["id"],
+ "mode": job["mode"],
+ "status": job["status"],
+ "progress": job.get("progress", 0),
+ "title": _job_title(job),
+ "url": job.get("source_url"),
+ "file_path": job.get("file_path"),
+ "format": job.get("format", "docx"),
+ "result_path": job.get("result_path"),
+ "transcription_path": job.get("transcription_path"),
+ "summary_path": job.get("summary_path"),
+ "has_transcription": bool(job.get("transcription")),
+ "has_summary": bool(job.get("summary")),
+ "error": job.get("error"),
+ "created_at": job.get("created_at"),
+ }
+ )
+ return {"jobs": jobs}
@router.get("/supported-sites")
async def get_supported_sites():
- """Retorna lista de sites que podem ser processados (vídeo/áudio via yt-dlp)"""
+ """Retorna lista de sites suportados."""
+
return {"sites": SUPPORTED_VIDEO_SITES}
diff --git a/lazier/cli.py b/lazier/cli.py
@@ -1,426 +1,213 @@
"""
-Interface de linha de comando (CLI) usando Click
+Interface de linha de comando (CLI) usando Click.
"""
import os
import sys
+import uuid
from pathlib import Path
from typing import Optional
-from datetime import datetime
+
import click
-from dotenv import load_dotenv
+try:
+ from dotenv import load_dotenv
+except ImportError: # pragma: no cover - ambiente sem python-dotenv
+ def load_dotenv():
+ return False
from rich.console import Console
-from rich.progress import Progress, SpinnerColumn, BarColumn, TextColumn, TimeElapsedColumn
from rich.panel import Panel
+from rich.progress import BarColumn, Progress, SpinnerColumn, TextColumn, TimeElapsedColumn
-from .utils import (
- validate_input,
- cleanup_files,
- get_output_filename,
- check_ffmpeg
-)
-from .downloader import download_youtube_audio, download_video_audio
-from .audio_processor import prepare_audio_file
-from .transcriber import transcribe_audio
-from .summarizer import summarize_text, summarize_text_file, summarize_web_page, summarize_pdf
-from .core.formats import export
from .core.playlist import is_playlist_url, process_playlist
-from .core.cache import get_cache_manager, calculate_file_hash, calculate_url_hash
+from .core.processing import process_source
+from .core.cache import get_cache_manager
from .core.exceptions import MusicContentError
-from .web.extractor import extract_pdf_content, extract_text_file_content
+from .utils import validate_input
load_dotenv()
console = Console()
-@click.group(invoke_without_command=True)
-@click.pass_context
-@click.version_option(version='0.01', prog_name='lazier')
-@click.argument('input_path', required=False)
-@click.option('--output', '-o', type=str, help='Nome do arquivo de saída')
-@click.option('--format', '-f', type=click.Choice(['docx', 'txt', 'md', 'json', 'pdf']), default='docx', help='Formato de saída (padrão: docx)')
-@click.option('--language', '-l', default='pt', help='Idioma para transcrição (padrão: pt)')
-@click.option('--model', default='whisper-1', help='Modelo Whisper (padrão: whisper-1)')
-@click.option('--gpt-model', default='gpt-4o-mini', help='Modelo GPT para sumarização (padrão: gpt-4o-mini)')
-@click.option('--keep-files', is_flag=True, help='Não deletar arquivos temporários')
-@click.option('--only-audio', is_flag=True, help='Processar apenas áudio (para vídeos)')
-def cli(ctx, input_path, output, format, language, model, gpt_model, keep_files, only_audio):
- """
- Lazier CLI
- """
- if ctx.invoked_subcommand is None:
- if input_path:
- # Comando padrão: transcreve e sumariza
- process_input(
- input_path=input_path,
- output=output,
- format_type=format,
- language=language,
- model=model,
- gpt_model=gpt_model,
- keep_files=keep_files,
- only_audio=only_audio,
- should_summarize=True
- )
- else:
- # Mostra ajuda se não há argumentos
- click.echo(ctx.get_help())
-
-
-@cli.command()
-@click.argument('input_path', type=str)
-@click.option('--output', '-o', type=str, help='Nome do arquivo de saída')
-@click.option('--format', '-f', type=click.Choice(['docx', 'txt', 'md', 'json', 'pdf']), default='docx', help='Formato de saída')
-@click.option('--language', '-l', default='pt', help='Idioma para transcrição (padrão: pt)')
-@click.option('--model', default='whisper-1', help='Modelo Whisper (padrão: whisper-1)')
-@click.option('--keep-files', is_flag=True, help='Não deletar arquivos temporários')
-@click.option('--only-audio', is_flag=True, help='Processar apenas áudio (para vídeos)')
-def transcribe(input_path: str, output: str, format: str, language: str, model: str, keep_files: bool, only_audio: bool):
- """Transcreve áudio/vídeo sem sumarizar"""
- process_input(
- input_path=input_path,
- output=output,
- format_type=format,
- language=language,
- model=model,
- keep_files=keep_files,
- only_audio=only_audio,
- should_summarize=False
- )
-
-
-@cli.command()
-@click.argument('input_path', type=str)
-@click.option('--output', '-o', type=str, help='Nome do arquivo DOCX de saída')
-@click.option('--language', '-l', default='pt', help='Idioma para transcrição (padrão: pt)')
-@click.option('--model', default='whisper-1', help='Modelo Whisper (padrão: whisper-1)')
-@click.option('--gpt-model', default='gpt-4o-mini', help='Modelo GPT para sumarização (padrão: gpt-4o-mini)')
-@click.option('--keep-files', is_flag=True, help='Não deletar arquivos temporários')
-@click.option('--only-audio', is_flag=True, help='Processar apenas áudio (para vídeos)')
-def summarize(input_path: str, output: str, language: str, model: str, gpt_model: str, keep_files: bool, only_audio: bool):
- """Apenas sumariza (requer arquivo de texto ou transcrição prévia)"""
- click.echo("Erro: Comando 'summarize' requer texto pré-transcrito.")
- click.echo("Use 'lazier <input>' para transcrição + sumarização ou 'lazier transcribe' para apenas transcrição.")
- sys.exit(1)
-
+def _progress_notifier(progress_bar, task_id):
+ def callback(progress: int, _status: str, message: Optional[str] = None):
+ if message:
+ progress_bar.update(task_id, description=f"[cyan]{message}")
+ progress_bar.update(task_id, completed=progress)
+ return callback
-def process_input(
+def _run_mode(
+ *,
input_path: str,
- output: Optional[str] = None,
- format_type: str = 'docx',
- language: str = 'pt',
- model: str = 'whisper-1',
- gpt_model: str = 'gpt-4o-mini',
- keep_files: bool = False,
- only_audio: bool = False,
- should_summarize: bool = True
+ mode: str,
+ output: Optional[str],
+ format_type: str,
+ model: str,
+ gpt_model: str,
):
- """Processa o input (arquivo ou URL) com progress bars"""
-
with Progress(
SpinnerColumn(),
TextColumn("[progress.description]{task.description}"),
BarColumn(),
TextColumn("[progress.percentage]{task.percentage:>3.0f}%"),
TimeElapsedColumn(),
- console=console
+ console=console,
) as progress:
-
- # Validação inicial
- task1 = progress.add_task("[cyan]Validando input...", total=100)
+ task = progress.add_task("[cyan]Preparando processamento...", total=100)
+
+ if not os.getenv("OPENAI_API_KEY"):
+ console.print("[red]Erro: OPENAI_API_KEY nao encontrada.[/red]")
+ sys.exit(1)
+
is_valid, input_type, error_msg = validate_input(input_path)
-
if not is_valid:
console.print(f"[red]Erro: {error_msg}[/red]")
sys.exit(1)
-
- # Verifica API key
- if not os.getenv('OPENAI_API_KEY'):
- console.print("[red]Erro: OPENAI_API_KEY não encontrada.[/red]")
- console.print("Configure a variável de ambiente ou crie um arquivo .env")
- sys.exit(1)
-
- progress.update(task1, completed=100)
-
- # Verifica cache
- cache = None
- try:
- cache = get_cache_manager()
- except:
- pass # Cache opcional
-
- files_to_cleanup = []
- metadata = {}
- transcription = None
- summary = None
- web_video_url_hash = None
-
+
+ if input_type == "youtube" and is_playlist_url(input_path):
+ playlist_results = process_playlist(input_path, parallel=False)
+ console.print(
+ f"[green]Playlist processada: {playlist_results['processed']}/{playlist_results['total_videos']} videos[/green]"
+ )
+ return
+
try:
- # Verifica se é playlist
- if input_type == 'youtube' and is_playlist_url(input_path):
- task2 = progress.add_task("[yellow]Processando playlist...", total=100)
- console.print(f"[yellow]Playlist detectada: {input_path}[/yellow]")
- playlist_results = process_playlist(input_path, parallel=False)
- console.print(f"[green]Playlist processada: {playlist_results['processed']}/{playlist_results['total_videos']} vídeos[/green]")
- progress.update(task2, completed=100)
- return
-
- # Determina arquivo a processar
- task3 = progress.add_task("[cyan]Preparando arquivo...", total=100)
- audio_file = None
- content_data = None
-
- if input_type == 'youtube':
- progress.update(task3, description="[cyan]Baixando vídeo do YouTube...")
- url_hash = calculate_url_hash(input_path) if cache else None
-
- # Verifica cache
- if cache:
- cached = cache.get('youtube', url_hash)
- if cached:
- transcription = cached.get('transcription')
- summary = cached.get('summary') if should_summarize else None
- metadata = cached.get('metadata', {})
- console.print("[green]✓[/green] Usando cache")
- progress.update(task3, completed=100)
- else:
- audio_file, metadata = download_youtube_audio(input_path)
- files_to_cleanup.append(audio_file)
- progress.update(task3, completed=50)
- else:
- audio_file, metadata = download_youtube_audio(input_path)
- files_to_cleanup.append(audio_file)
- progress.update(task3, completed=50)
-
- elif input_type == 'video':
- progress.update(task3, description="[cyan]Extraindo áudio do vídeo...")
- audio_file = prepare_audio_file(input_path, is_video=True)
- if audio_file != input_path:
- files_to_cleanup.append(audio_file)
- progress.update(task3, completed=100)
-
- elif input_type == 'audio':
- progress.update(task3, description="[cyan]Preparando áudio...")
- audio_file = prepare_audio_file(input_path, is_video=False)
- if audio_file != input_path:
- files_to_cleanup.append(audio_file)
- progress.update(task3, completed=100)
-
- elif input_type == 'web':
- progress.update(task3, description="[cyan]Tentando extrair vídeo/áudio da URL...")
- web_video_url_hash = calculate_url_hash(input_path) if cache else None
- try:
- audio_file, metadata = download_video_audio(input_path)
- files_to_cleanup.append(audio_file)
- if cache and web_video_url_hash:
- cached = cache.get('video', web_video_url_hash)
- if cached:
- transcription = cached.get('transcription')
- summary = cached.get('summary') if should_summarize else None
- metadata = cached.get('metadata', {})
- console.print("[green]✓[/green] Usando cache")
- audio_file = None
- progress.update(task3, completed=100)
- except MusicContentError:
- console.print("[red]Conteúdo detectado como música não é processado pelo Lazier.[/red]")
- cleanup_files(files_to_cleanup)
- sys.exit(1)
- except Exception:
- progress.update(task3, description="[cyan]Extraindo texto da página web...")
- content_data = extract_web_content(input_path)
- metadata = {'title': content_data.get('title', 'Página Web'), 'file_path': input_path}
- transcription = content_data['content']
- summary = summarize_web_page(input_path, model=gpt_model, language='pt-BR') if should_summarize else None
- progress.update(task3, completed=100)
- audio_file = None
-
- # Processa texto/PDF/web (arquivos locais)
- elif Path(input_path).suffix.lower() == '.pdf':
- progress.update(task3, description="[cyan]Extraindo texto do PDF...")
- content_data = extract_pdf_content(input_path)
- metadata = {'title': content_data.get('title', 'PDF'), 'file_path': input_path}
- progress.update(task3, completed=100)
-
- elif Path(input_path).suffix.lower() in ['.txt', '.md', '.html']:
- progress.update(task3, description="[cyan]Lendo arquivo de texto...")
- content_data = extract_text_file_content(input_path)
- metadata = {'title': content_data.get('title', 'Texto'), 'file_path': input_path}
- progress.update(task3, completed=100)
-
- # Transcrição (para áudio/vídeo)
- if audio_file:
- task4 = progress.add_task("[magenta]Transcrevendo áudio...", total=100)
-
- if not transcription:
- file_hash = calculate_file_hash(audio_file) if cache else None
-
- # Verifica cache
- if cache and file_hash:
- cached = cache.get('transcription', file_hash)
- if cached:
- transcription = cached.get('transcription')
- metadata = cached.get('metadata', metadata)
- console.print("[green]✓[/green] Transcrição do cache")
- progress.update(task4, completed=100)
- else:
- transcription = transcribe_audio(audio_file, language=language, model=model)
- progress.update(task4, completed=100)
-
- # Salva cache
- if cache:
- cache.set('transcription', file_hash, {
- 'transcription': transcription,
- 'metadata': metadata,
- 'timestamp': datetime.now().isoformat(),
- })
- else:
- transcription = transcribe_audio(audio_file, language=language, model=model)
- progress.update(task4, completed=100)
-
- # Sumarização
- if should_summarize:
- task5 = progress.add_task("[green]Gerando sumário...", total=100)
-
- if transcription:
- # Sumariza transcrição
- text_hash = calculate_url_hash(transcription) if cache else None
-
- if cache and text_hash:
- cached = cache.get('summary', text_hash)
- if cached:
- summary = cached.get('summary')
- console.print("[green]✓[/green] Sumário do cache")
- progress.update(task5, completed=100)
- else:
- summary = summarize_text(transcription, model=gpt_model, language='pt-BR')
- progress.update(task5, completed=100)
-
- if cache:
- cache.set('summary', text_hash, {
- 'summary': summary,
- 'timestamp': datetime.now().isoformat(),
- })
- else:
- summary = summarize_text(transcription, model=gpt_model, language='pt-BR')
- progress.update(task5, completed=100)
-
- elif content_data:
- # Sumariza conteúdo extraído
- if Path(input_path).suffix.lower() == '.pdf':
- summary = summarize_pdf(input_path, model=gpt_model, language='pt-BR')
- else:
- summary = summarize_text_file(input_path, model=gpt_model, language='pt-BR')
- transcription = content_data['content']
- progress.update(task5, completed=100)
-
- if web_video_url_hash and cache and transcription:
- cache.set('video', web_video_url_hash, {
- 'transcription': transcription,
- 'summary': summary,
- 'metadata': metadata,
- 'timestamp': datetime.now().isoformat(),
- })
-
- # Gera arquivo de saída
- task6 = progress.add_task(f"[blue]Gerando arquivo {format_type.upper()}...", total=100)
-
- # Garantir link do vídeo no cabeçalho quando o input é URL (YouTube ou vídeo genérico)
- if metadata is None:
- metadata = {}
- if not metadata.get('webpage_url') and input_path.strip().startswith(('http://', 'https://')):
- metadata = {**metadata, 'webpage_url': input_path.strip()}
-
- if not output:
- output = get_output_filename(input_path)
- # Adiciona extensão correta
- output_path_obj = Path(output)
- if output_path_obj.suffix != f'.{format_type}':
- output = str(output_path_obj.with_suffix(f'.{format_type}'))
-
- export(
- transcription=transcription or content_data['content'] if content_data else "",
- summary=summary if should_summarize else None,
- metadata=metadata,
+ result = process_source(
+ input_path,
+ mode=mode,
+ output_format=format_type,
+ model=model,
+ gpt_model=gpt_model,
output_path=output,
- format_type=format_type
+ run_id=str(uuid.uuid4()),
+ source_name=Path(input_path).name if not input_path.startswith(("http://", "https://")) else input_path,
+ progress_callback=_progress_notifier(progress, task),
)
-
- progress.update(task6, completed=100)
-
- console.print(f"\n[bold green]✓ Processamento concluído![/bold green]")
- console.print(f"[cyan]Arquivo gerado:[/cyan] {output}")
-
- # Limpeza
- if not keep_files:
- cleanup_files(files_to_cleanup)
-
except MusicContentError:
- console.print("[red]Conteúdo detectado como música não é processado pelo Lazier.[/red]")
- cleanup_files(files_to_cleanup)
+ console.print("[red]Conteudo detectado como musica nao e processado pelo Lazier.[/red]")
sys.exit(1)
except KeyboardInterrupt:
- console.print("\n[yellow]Operação cancelada pelo usuário.[/yellow]")
- cleanup_files(files_to_cleanup)
+ console.print("\n[yellow]Operacao cancelada pelo usuario.[/yellow]")
sys.exit(1)
- except Exception as e:
- console.print(f"\n[red]Erro durante processamento:[/red] {str(e)}")
- cleanup_files(files_to_cleanup)
+ except Exception as exc:
+ console.print(f"\n[red]Erro durante processamento:[/red] {exc}")
sys.exit(1)
+ console.print(f"\n[bold green]✓ Processamento concluido![/bold green]")
+ console.print(f"[cyan]Arquivo gerado:[/cyan] {result['result_path']}")
+
+
+@click.group(invoke_without_command=True)
+@click.pass_context
+@click.version_option(version="0.01", prog_name="lazier")
+@click.argument("input_path", required=False)
+def cli(ctx, input_path):
+ """
+ Lazier CLI.
+ """
+ if ctx.invoked_subcommand is None:
+ if input_path:
+ console.print(
+ Panel.fit(
+ "Use comandos explicitos:\n\n"
+ "`lazier transcribe <input>` para transcrever em portugues\n"
+ "`lazier summarize <input>` para gerar um sumario em portugues",
+ title="Modo de Uso",
+ )
+ )
+ click.echo(ctx.get_help())
+
@cli.command()
-@click.option('--port', '-p', default=19283, help='Porta do servidor (padrão: 19283)')
-@click.option('--host', default='0.0.0.0', help='Host do servidor (padrão: 0.0.0.0)')
+@click.argument("input_path", type=str)
+@click.option("--output", "-o", type=str, help="Nome do arquivo de saida")
+@click.option("--format", "-f", "format_type", type=click.Choice(["docx", "txt", "md", "json", "pdf"]), default="docx", help="Formato de saida")
+@click.option("--model", default="whisper-1", help="Modelo Whisper (padrao: whisper-1)")
+@click.option("--gpt-model", default="gpt-4o-mini", help="Modelo GPT para conversao ao portugues (padrao: gpt-4o-mini)")
+def transcribe(input_path: str, output: Optional[str], format_type: str, model: str, gpt_model: str):
+ """Transcreve ou converte o conteudo para portugues."""
+ _run_mode(
+ input_path=input_path,
+ mode="transcribe",
+ output=output,
+ format_type=format_type,
+ model=model,
+ gpt_model=gpt_model,
+ )
+
+
+@cli.command()
+@click.argument("input_path", type=str)
+@click.option("--output", "-o", type=str, help="Nome do arquivo de saida")
+@click.option("--format", "-f", "format_type", type=click.Choice(["docx", "txt", "md", "json", "pdf"]), default="docx", help="Formato de saida")
+@click.option("--model", default="whisper-1", help="Modelo Whisper (padrao: whisper-1)")
+@click.option("--gpt-model", default="gpt-4o-mini", help="Modelo GPT para sumario (padrao: gpt-4o-mini)")
+def summarize(input_path: str, output: Optional[str], format_type: str, model: str, gpt_model: str):
+ """Gera um sumario em portugues do conteudo informado."""
+ _run_mode(
+ input_path=input_path,
+ mode="summarize",
+ output=output,
+ format_type=format_type,
+ model=model,
+ gpt_model=gpt_model,
+ )
+
+
+@cli.command()
+@click.option("--port", "-p", default=19283, help="Porta do servidor (padrao: 19283)")
+@click.option("--host", default="0.0.0.0", help="Host do servidor (padrao: 0.0.0.0)")
def web(port: int, host: str):
- """Inicia servidor web FastAPI"""
+ """Inicia servidor web FastAPI."""
import uvicorn
from .api.main import app
-
- console.print(Panel.fit(
- f"[bold green]Lazier WebGUI[/bold green]\n\n"
- f"Servidor iniciando em [cyan]http://{host}:{port}[/cyan]\n"
- f"Pressione Ctrl+C para parar",
- title="🚀 Servidor Web"
- ))
-
+
+ console.print(
+ Panel.fit(
+ f"[bold green]Lazier WebGUI[/bold green]\n\n"
+ f"Servidor iniciando em [cyan]http://{host}:{port}[/cyan]\n"
+ "Pressione Ctrl+C para parar",
+ title="Servidor Web",
+ )
+ )
+
uvicorn.run(app, host=host, port=port)
@cli.command()
def cache():
- """Comandos de cache"""
+ """Comandos de cache."""
pass
@cache.command()
def clear():
- """Limpa todo o cache"""
+ """Limpa todo o cache."""
try:
- from .core.cache import get_cache_manager
- cache = get_cache_manager()
- count = cache.clear_all()
+ cache_manager = get_cache_manager()
+ count = cache_manager.clear_all()
console.print(f"[green]Cache limpo: {count} chaves removidas[/green]")
- except Exception as e:
- console.print(f"[red]Erro ao limpar cache: {e}[/red]")
+ except Exception as exc:
+ console.print(f"[red]Erro ao limpar cache: {exc}[/red]")
@cache.command()
def stats():
- """Mostra estatísticas do cache"""
+ """Mostra estatisticas do cache."""
try:
- from .core.cache import get_cache_manager
- cache = get_cache_manager()
- stats = cache.stats()
- console.print(Panel.fit(
- f"[bold]Estatísticas do Cache[/bold]\n\n"
- f"Total de chaves: [cyan]{stats.get('total_keys', 0)}[/cyan]\n"
- f"Memória usada: [cyan]{stats.get('memory_used', 'N/A')}[/cyan]\n"
- f"Memória pico: [cyan]{stats.get('memory_peak', 'N/A')}[/cyan]\n"
- f"Clientes conectados: [cyan]{stats.get('connected_clients', 0)}[/cyan]",
- title="📊 Cache Redis"
- ))
- except Exception as e:
- console.print(f"[red]Erro ao obter estatísticas: {e}[/red]")
+ cache_manager = get_cache_manager()
+ stats_data = cache_manager.stats()
+ console.print(
+ Panel.fit(
+ f"[bold]Estatisticas do Cache[/bold]\n\n"
+ f"Total de chaves: [cyan]{stats_data.get('total_keys', 0)}[/cyan]\n"
+ f"Memoria usada: [cyan]{stats_data.get('memory_used', 'N/A')}[/cyan]\n"
+ f"Memoria pico: [cyan]{stats_data.get('memory_peak', 'N/A')}[/cyan]\n"
+ f"Clientes conectados: [cyan]{stats_data.get('connected_clients', 0)}[/cyan]",
+ title="Cache Redis",
+ )
+ )
+ except Exception as exc:
+ console.print(f"[red]Erro ao obter estatisticas: {exc}[/red]")
diff --git a/lazier/core/auth.py b/lazier/core/auth.py
@@ -8,7 +8,11 @@ from pathlib import Path
from typing import Optional
from datetime import datetime
from passlib.context import CryptContext
-from dotenv import load_dotenv
+try:
+ from dotenv import load_dotenv
+except ImportError: # pragma: no cover - ambiente sem python-dotenv
+ def load_dotenv():
+ return False
# Monkey patch para evitar erro durante detect_wrap_bug do passlib
# O passlib tenta detectar um bug do bcrypt usando uma senha de teste que pode exceder 72 bytes
diff --git a/lazier/core/cache.py b/lazier/core/cache.py
@@ -7,9 +7,22 @@ import json
import hashlib
from typing import Optional, Dict, Any, Union
from datetime import timedelta
-import redis
-from redis.exceptions import ConnectionError, RedisError
-from dotenv import load_dotenv
+try:
+ import redis
+ from redis.exceptions import ConnectionError, RedisError
+except ImportError: # pragma: no cover - ambiente sem redis instalado
+ redis = None
+
+ class ConnectionError(Exception):
+ pass
+
+ class RedisError(Exception):
+ pass
+try:
+ from dotenv import load_dotenv
+except ImportError: # pragma: no cover - ambiente sem python-dotenv
+ def load_dotenv():
+ return False
load_dotenv()
@@ -19,6 +32,8 @@ class CacheManager:
def __init__(self):
"""Inicializa conexão com Redis"""
+ if redis is None:
+ raise Exception("Redis não está instalado neste ambiente.")
self.redis_host = os.getenv('REDIS_HOST', 'localhost')
self.redis_port = int(os.getenv('REDIS_PORT', 6379))
self.redis_db = int(os.getenv('REDIS_DB', 0))
diff --git a/lazier/core/formats.py b/lazier/core/formats.py
@@ -6,9 +6,13 @@ import json
from datetime import datetime
from pathlib import Path
from typing import Optional, Dict, Any
-from docx import Document
-from docx.shared import Pt, Inches, RGBColor
-from docx.enum.text import WD_ALIGN_PARAGRAPH
+try:
+ from docx import Document # noqa: F401
+ from docx.shared import Pt, Inches, RGBColor # noqa: F401
+ from docx.enum.text import WD_ALIGN_PARAGRAPH # noqa: F401
+except ImportError: # pragma: no cover - ambiente sem python-docx
+ Document = None
+ Pt = Inches = RGBColor = WD_ALIGN_PARAGRAPH = None
from ..docx_generator import _format_duration
from ..utils import sanitize_xml_string
@@ -82,18 +86,19 @@ def export_txt(
lines.append("")
# Transcrição
- lines.append("TRANSCRIÇÃO COMPLETA")
- lines.append("-" * 80)
-
- # Divide transcrição em parágrafos e preserva estrutura
- transcription_paragraphs = transcription.split('\n\n')
- if len(transcription_paragraphs) == 1:
- transcription_paragraphs = transcription.split('\n')
-
- for para in transcription_paragraphs:
- if para.strip():
- lines.append(para.strip())
- lines.append("") # Linha em branco entre parágrafos
+ if transcription and transcription.strip():
+ lines.append("TRANSCRIÇÃO COMPLETA")
+ lines.append("-" * 80)
+
+ # Divide transcrição em parágrafos e preserva estrutura
+ transcription_paragraphs = transcription.split('\n\n')
+ if len(transcription_paragraphs) == 1:
+ transcription_paragraphs = transcription.split('\n')
+
+ for para in transcription_paragraphs:
+ if para.strip():
+ lines.append(para.strip())
+ lines.append("") # Linha em branco entre parágrafos
# Salva arquivo
with open(output_path_obj, 'w', encoding='utf-8') as f:
@@ -168,18 +173,19 @@ def export_markdown(
lines.append("")
# Transcrição
- lines.append("## Transcrição Completa")
- lines.append("")
-
- # Divide transcrição em parágrafos e formata corretamente
- transcription_paragraphs = transcription.split('\n\n')
- if len(transcription_paragraphs) == 1:
- transcription_paragraphs = transcription.split('\n')
-
- for para in transcription_paragraphs:
- if para.strip():
- lines.append(para.strip())
- lines.append("") # Linha em branco entre parágrafos (Markdown requer)
+ if transcription and transcription.strip():
+ lines.append("## Transcrição Completa")
+ lines.append("")
+
+ # Divide transcrição em parágrafos e formata corretamente
+ transcription_paragraphs = transcription.split('\n\n')
+ if len(transcription_paragraphs) == 1:
+ transcription_paragraphs = transcription.split('\n')
+
+ for para in transcription_paragraphs:
+ if para.strip():
+ lines.append(para.strip())
+ lines.append("") # Linha em branco entre parágrafos (Markdown requer)
# Salva arquivo
with open(output_path_obj, 'w', encoding='utf-8') as f:
@@ -217,7 +223,7 @@ def export_json(
'uploader': metadata.get('uploader') if metadata else None,
'webpage_url': metadata.get('webpage_url') if metadata else None,
},
- 'transcription': transcription,
+ 'transcription': transcription if transcription and transcription.strip() else None,
'summary': summary,
}
@@ -325,14 +331,15 @@ def export_pdf(
story.append(Paragraph(text, body_style))
story.append(Spacer(1, 16))
- story.append(Paragraph("Transcrição Completa", heading_style))
- story.append(Spacer(1, 8))
transcription_safe = sanitize_xml_string(transcription or "")
- for para in (transcription_safe.split('\n\n') if transcription_safe else []):
- if not para.strip():
- continue
- text = para.strip().replace('&', '&').replace('<', '<').replace('>', '>').replace('\n', '<br/>').replace('**', '')
- story.append(Paragraph(text, body_style))
+ if transcription_safe.strip():
+ story.append(Paragraph("Transcrição Completa", heading_style))
+ story.append(Spacer(1, 8))
+ for para in transcription_safe.split('\n\n'):
+ if not para.strip():
+ continue
+ text = para.strip().replace('&', '&').replace('<', '<').replace('>', '>').replace('\n', '<br/>').replace('**', '')
+ story.append(Paragraph(text, body_style))
doc.build(story)
return str(output_path_obj)
diff --git a/lazier/core/jobs.py b/lazier/core/jobs.py
@@ -0,0 +1,274 @@
+"""
+Persistencia de jobs e organizacao de arquivos de saida.
+"""
+
+import json
+import os
+import re
+import sqlite3
+import threading
+import unicodedata
+from datetime import datetime
+from pathlib import Path
+from typing import Any, Dict, List, Optional, Union
+
+
+TERMINAL_STATUSES = {"completed", "failed", "interrupted"}
+ARTIFACT_FILENAMES = {
+ "transcription": "transcricao",
+ "summary": "sumario",
+ "result": "resultado",
+}
+
+
+def _coerce_datetime(value: Optional[Union[str, datetime]]) -> datetime:
+ if isinstance(value, datetime):
+ return value
+ if isinstance(value, str) and value:
+ return datetime.fromisoformat(value)
+ return datetime.now()
+
+
+def get_jobs_db_path() -> Path:
+ data_dir = Path(os.getenv("LAZIER_DATA_DIR", "/app/data"))
+ data_dir.mkdir(parents=True, exist_ok=True)
+ return data_dir / "jobs.db"
+
+
+def get_outputs_root() -> Path:
+ output_dir = Path(os.getenv("LAZIER_OUTPUT_DIR", "/app/outputs"))
+ output_dir.mkdir(parents=True, exist_ok=True)
+ return output_dir
+
+
+def slugify_source_name(source_name: Optional[str], fallback: str = "job") -> str:
+ base = source_name or fallback
+ base = Path(base).stem
+ normalized = unicodedata.normalize("NFKD", base)
+ ascii_only = normalized.encode("ascii", "ignore").decode("ascii")
+ ascii_only = ascii_only.lower()
+ ascii_only = re.sub(r"[^a-z0-9]+", "-", ascii_only).strip("-")
+ return ascii_only[:40] or fallback
+
+
+def build_job_output_dir(
+ job_id: str,
+ source_name: Optional[str] = None,
+ created_at: Optional[Union[str, datetime]] = None,
+ output_root: Optional[Path] = None,
+) -> Path:
+ timestamp = _coerce_datetime(created_at)
+ root = output_root or get_outputs_root()
+ slug = slugify_source_name(source_name, fallback="job")
+ folder_name = f"{slug}-{job_id[:8]}"
+ return root / timestamp.strftime("%Y") / timestamp.strftime("%m") / timestamp.strftime("%d") / folder_name
+
+
+def build_job_artifact_path(
+ job_id: str,
+ source_name: Optional[str],
+ format_type: str,
+ artifact_kind: str,
+ created_at: Optional[Union[str, datetime]] = None,
+ output_root: Optional[Path] = None,
+) -> Path:
+ if artifact_kind not in ARTIFACT_FILENAMES:
+ raise ValueError(f"Artefato nao suportado: {artifact_kind}")
+
+ output_dir = build_job_output_dir(
+ job_id=job_id,
+ source_name=source_name,
+ created_at=created_at,
+ output_root=output_root,
+ )
+ output_dir.mkdir(parents=True, exist_ok=True)
+ filename = ARTIFACT_FILENAMES[artifact_kind]
+ return output_dir / f"{filename}.{format_type}"
+
+
+class JobStore:
+ """Repositorio SQLite para jobs da WebGUI."""
+
+ def __init__(self, db_path: Optional[Path] = None):
+ self.db_path = Path(db_path or get_jobs_db_path())
+ self.db_path.parent.mkdir(parents=True, exist_ok=True)
+ self._lock = threading.Lock()
+ self.init_db()
+
+ def _connect(self) -> sqlite3.Connection:
+ connection = sqlite3.connect(self.db_path)
+ connection.row_factory = sqlite3.Row
+ return connection
+
+ def init_db(self) -> None:
+ with self._connect() as conn:
+ conn.execute(
+ """
+ CREATE TABLE IF NOT EXISTS jobs (
+ id TEXT PRIMARY KEY,
+ mode TEXT NOT NULL,
+ status TEXT NOT NULL,
+ progress INTEGER NOT NULL DEFAULT 0,
+ input_type TEXT,
+ source_name TEXT,
+ source_url TEXT,
+ file_path TEXT,
+ format TEXT NOT NULL,
+ metadata_json TEXT NOT NULL DEFAULT '{}',
+ transcription TEXT,
+ summary TEXT,
+ result_path TEXT,
+ transcription_path TEXT,
+ summary_path TEXT,
+ error TEXT,
+ created_at TEXT NOT NULL,
+ updated_at TEXT NOT NULL,
+ completed_at TEXT
+ )
+ """
+ )
+ conn.commit()
+
+ def reset(self) -> None:
+ with self._connect() as conn:
+ conn.execute("DELETE FROM jobs")
+ conn.commit()
+
+ def create_job(self, job_data: Dict[str, Any]) -> Dict[str, Any]:
+ now = datetime.now().isoformat()
+ payload = {
+ "id": job_data["id"],
+ "mode": job_data["mode"],
+ "status": job_data.get("status", "pending"),
+ "progress": int(job_data.get("progress", 0)),
+ "input_type": job_data.get("input_type"),
+ "source_name": job_data.get("source_name"),
+ "source_url": job_data.get("source_url"),
+ "file_path": job_data.get("file_path"),
+ "format": job_data.get("format", "docx"),
+ "metadata_json": json.dumps(job_data.get("metadata", {}), ensure_ascii=False),
+ "transcription": job_data.get("transcription"),
+ "summary": job_data.get("summary"),
+ "result_path": job_data.get("result_path"),
+ "transcription_path": job_data.get("transcription_path"),
+ "summary_path": job_data.get("summary_path"),
+ "error": job_data.get("error"),
+ "created_at": job_data.get("created_at", now),
+ "updated_at": job_data.get("updated_at", job_data.get("created_at", now)),
+ "completed_at": job_data.get("completed_at"),
+ }
+
+ with self._lock, self._connect() as conn:
+ conn.execute(
+ """
+ INSERT INTO jobs (
+ id, mode, status, progress, input_type, source_name, source_url, file_path,
+ format, metadata_json, transcription, summary, result_path,
+ transcription_path, summary_path, error, created_at, updated_at, completed_at
+ ) VALUES (
+ :id, :mode, :status, :progress, :input_type, :source_name, :source_url, :file_path,
+ :format, :metadata_json, :transcription, :summary, :result_path,
+ :transcription_path, :summary_path, :error, :created_at, :updated_at, :completed_at
+ )
+ """,
+ payload,
+ )
+ conn.commit()
+ return self.get_job(payload["id"])
+
+ def update_job(self, job_id: str, **updates: Any) -> Dict[str, Any]:
+ if not updates:
+ job = self.get_job(job_id)
+ if job is None:
+ raise KeyError(f"Job nao encontrado: {job_id}")
+ return job
+
+ assignments = []
+ values: Dict[str, Any] = {"id": job_id}
+
+ if "metadata" in updates:
+ updates["metadata_json"] = json.dumps(updates.pop("metadata") or {}, ensure_ascii=False)
+
+ if "status" in updates and updates["status"] in TERMINAL_STATUSES and "completed_at" not in updates:
+ updates["completed_at"] = datetime.now().isoformat()
+
+ updates["updated_at"] = datetime.now().isoformat()
+
+ for key, value in updates.items():
+ assignments.append(f"{key} = :{key}")
+ values[key] = value
+
+ with self._lock, self._connect() as conn:
+ cursor = conn.execute(
+ f"UPDATE jobs SET {', '.join(assignments)} WHERE id = :id",
+ values,
+ )
+ conn.commit()
+ if cursor.rowcount == 0:
+ raise KeyError(f"Job nao encontrado: {job_id}")
+
+ return self.get_job(job_id)
+
+ def get_job(self, job_id: str) -> Optional[Dict[str, Any]]:
+ with self._connect() as conn:
+ row = conn.execute("SELECT * FROM jobs WHERE id = ?", (job_id,)).fetchone()
+ return self._row_to_job(row) if row else None
+
+ def list_jobs(self) -> List[Dict[str, Any]]:
+ with self._connect() as conn:
+ rows = conn.execute("SELECT * FROM jobs ORDER BY created_at DESC").fetchall()
+ return [self._row_to_job(row) for row in rows]
+
+ def mark_incomplete_as_interrupted(self) -> int:
+ interrupted_at = datetime.now().isoformat()
+ with self._lock, self._connect() as conn:
+ cursor = conn.execute(
+ """
+ UPDATE jobs
+ SET status = 'interrupted',
+ progress = CASE WHEN progress > 0 THEN progress ELSE 0 END,
+ error = COALESCE(error, 'Processamento interrompido por reinicializacao do servidor.'),
+ updated_at = ?,
+ completed_at = ?
+ WHERE status IN ('pending', 'processing')
+ """,
+ (interrupted_at, interrupted_at),
+ )
+ conn.commit()
+ return cursor.rowcount
+
+ def _row_to_job(self, row: sqlite3.Row) -> Dict[str, Any]:
+ return {
+ "id": row["id"],
+ "mode": row["mode"],
+ "status": row["status"],
+ "progress": row["progress"],
+ "input_type": row["input_type"],
+ "source_name": row["source_name"],
+ "source_url": row["source_url"],
+ "file_path": row["file_path"],
+ "format": row["format"],
+ "metadata": json.loads(row["metadata_json"] or "{}"),
+ "transcription": row["transcription"],
+ "summary": row["summary"],
+ "result_path": row["result_path"],
+ "transcription_path": row["transcription_path"],
+ "summary_path": row["summary_path"],
+ "error": row["error"],
+ "created_at": row["created_at"],
+ "updated_at": row["updated_at"],
+ "completed_at": row["completed_at"],
+ }
+
+
+_job_store: Optional[JobStore] = None
+_job_store_path: Optional[Path] = None
+
+
+def get_job_store() -> JobStore:
+ global _job_store, _job_store_path
+ db_path = get_jobs_db_path()
+ if _job_store is None or _job_store_path != db_path:
+ _job_store = JobStore(db_path)
+ _job_store_path = db_path
+ return _job_store
diff --git a/lazier/core/processing.py b/lazier/core/processing.py
@@ -0,0 +1,400 @@
+"""
+Pipeline compartilhado de processamento para CLI e API.
+"""
+
+import os
+from datetime import datetime
+from pathlib import Path
+from typing import Any, Callable, Dict, Optional
+
+from .cache import calculate_file_hash, calculate_url_hash, get_cache_manager
+from .exceptions import MusicContentError
+from .jobs import build_job_artifact_path, get_outputs_root
+from ..audio_processor import prepare_audio_file
+from ..downloader import download_video_audio, download_youtube_audio
+from ..summarizer import render_text_in_portuguese, summarize_text
+from ..transcriber import transcribe_audio
+from ..utils import cleanup_files, validate_input
+from ..web.extractor import extract_pdf_content, extract_text_file_content, extract_web_content
+from .formats import export
+
+ProgressCallback = Optional[Callable[[int, str, Optional[str]], None]]
+
+
+def _notify(callback: ProgressCallback, progress: int, status: str, message: Optional[str] = None) -> None:
+ if callback:
+ callback(progress, status, message)
+
+
+def _get_cache_manager_safe():
+ try:
+ return get_cache_manager()
+ except Exception:
+ return None
+
+
+def _ensure_api_key() -> None:
+ if not os.getenv("OPENAI_API_KEY"):
+ raise Exception(
+ "OPENAI_API_KEY nao encontrada. Configure a variavel de ambiente OPENAI_API_KEY "
+ "ou crie um arquivo .env"
+ )
+
+
+def _ensure_mode(mode: str) -> str:
+ if mode not in {"transcribe", "summarize"}:
+ raise ValueError("Modo invalido. Use 'transcribe' ou 'summarize'.")
+ return mode
+
+
+def _export_selected_artifact(
+ mode: str,
+ format_type: str,
+ output_path: str,
+ transcription: str,
+ summary: Optional[str],
+ metadata: Dict[str, Any],
+) -> str:
+ if mode == "transcribe":
+ return export(
+ transcription=transcription,
+ summary=None,
+ metadata=metadata,
+ output_path=output_path,
+ format_type=format_type,
+ )
+ return export(
+ transcription="",
+ summary=summary,
+ metadata=metadata,
+ output_path=output_path,
+ format_type=format_type,
+ )
+
+
+def _transcribe_media_to_portuguese(
+ audio_file: str,
+ cache_prefix: str,
+ cache_identifier: str,
+ model: str,
+ gpt_model: str,
+ metadata: Dict[str, Any],
+ progress_callback: ProgressCallback,
+ progress_start: int = 30,
+ progress_end: int = 70,
+) -> str:
+ cache = _get_cache_manager_safe()
+ cached = cache.get(cache_prefix, cache_identifier) if cache else None
+ if cached and cached.get("transcription"):
+ _notify(progress_callback, progress_end, "processing", "Transcricao encontrada no cache")
+ return cached["transcription"]
+
+ midpoint = progress_start + ((progress_end - progress_start) // 2)
+ _notify(progress_callback, progress_start, "processing", "Transcrevendo audio...")
+ raw_transcription = transcribe_audio(audio_file, language=None, model=model)
+ _notify(progress_callback, midpoint, "processing", "Convertendo conteudo para portugues...")
+ portuguese_text = render_text_in_portuguese(raw_transcription, model=gpt_model)
+
+ if cache:
+ cache.set(
+ cache_prefix,
+ cache_identifier,
+ {
+ "transcription": portuguese_text,
+ "metadata": metadata,
+ "timestamp": datetime.now().isoformat(),
+ },
+ )
+
+ _notify(progress_callback, progress_end, "processing", "Transcricao concluida")
+ return portuguese_text
+
+
+def _summarize_portuguese_text(
+ text: str,
+ model: str,
+ progress_callback: ProgressCallback,
+ progress_start: int = 75,
+ progress_end: int = 88,
+) -> str:
+ cache = _get_cache_manager_safe()
+ text_hash = calculate_url_hash(text)
+ cached = cache.get("summary", text_hash) if cache else None
+ if cached and cached.get("summary"):
+ _notify(progress_callback, progress_end, "processing", "Sumario encontrado no cache")
+ return cached["summary"]
+
+ _notify(progress_callback, progress_start, "processing", "Gerando sumario em portugues...")
+ summary = summarize_text(text, model=model, language="pt-BR")
+ if cache:
+ cache.set(
+ "summary",
+ text_hash,
+ {
+ "summary": summary,
+ "timestamp": datetime.now().isoformat(),
+ },
+ )
+ _notify(progress_callback, progress_end, "processing", "Sumario concluido")
+ return summary
+
+
+def process_source(
+ source: str,
+ *,
+ mode: str,
+ output_format: str = "docx",
+ model: str = "whisper-1",
+ gpt_model: str = "gpt-4o-mini",
+ output_path: Optional[str] = None,
+ output_root: Optional[Path] = None,
+ run_id: Optional[str] = None,
+ source_name: Optional[str] = None,
+ created_at: Optional[str] = None,
+ progress_callback: ProgressCallback = None,
+ keep_files: bool = False,
+) -> Dict[str, Any]:
+ """
+ Processa um input e retorna texto em portugues, sumario e caminhos de saida.
+ """
+
+ _ensure_api_key()
+ mode = _ensure_mode(mode)
+ _notify(progress_callback, 5, "processing", "Validando entrada...")
+ is_valid, input_type, error_msg = validate_input(source)
+ if not is_valid:
+ raise Exception(error_msg)
+
+ cache = _get_cache_manager_safe()
+ files_to_cleanup = []
+ metadata: Dict[str, Any] = {}
+ portuguese_text: Optional[str] = None
+ summary: Optional[str] = None
+
+ try:
+ output_root = output_root or get_outputs_root()
+
+ if input_type == "youtube":
+ url_hash = calculate_url_hash(source) if cache else ""
+ cached = cache.get("youtube", url_hash) if cache else None
+ if cached and cached.get("transcription"):
+ metadata = cached.get("metadata", {})
+ portuguese_text = cached.get("transcription")
+ summary = cached.get("summary")
+ _notify(progress_callback, 70, "processing", "Conteudo do YouTube encontrado no cache")
+ if mode == "summarize" and not summary:
+ summary = _summarize_portuguese_text(portuguese_text, gpt_model, progress_callback)
+ if cache:
+ cache.set(
+ "youtube",
+ url_hash,
+ {
+ "transcription": portuguese_text,
+ "summary": summary,
+ "metadata": metadata,
+ "timestamp": datetime.now().isoformat(),
+ },
+ )
+ else:
+ _notify(progress_callback, 15, "processing", "Baixando video do YouTube...")
+ audio_file, metadata = download_youtube_audio(source)
+ files_to_cleanup.append(audio_file)
+ portuguese_text = _transcribe_media_to_portuguese(
+ audio_file=audio_file,
+ cache_prefix="transcription",
+ cache_identifier=calculate_file_hash(audio_file),
+ model=model,
+ gpt_model=gpt_model,
+ metadata=metadata,
+ progress_callback=progress_callback,
+ )
+ if mode == "summarize":
+ summary = _summarize_portuguese_text(portuguese_text, gpt_model, progress_callback)
+ if cache:
+ cache.set(
+ "youtube",
+ url_hash,
+ {
+ "transcription": portuguese_text,
+ "summary": summary,
+ "metadata": metadata,
+ "timestamp": datetime.now().isoformat(),
+ },
+ )
+
+ elif input_type == "web":
+ url_hash = calculate_url_hash(source) if cache else ""
+ try:
+ _notify(progress_callback, 15, "processing", "Tentando extrair audio da URL...")
+ audio_file, metadata = download_video_audio(source)
+ files_to_cleanup.append(audio_file)
+ cached = cache.get("video", url_hash) if cache else None
+ if cached and cached.get("transcription"):
+ metadata = cached.get("metadata", metadata)
+ portuguese_text = cached.get("transcription")
+ summary = cached.get("summary")
+ _notify(progress_callback, 70, "processing", "Conteudo de video encontrado no cache")
+ if mode == "summarize" and not summary:
+ summary = _summarize_portuguese_text(portuguese_text, gpt_model, progress_callback)
+ if cache:
+ cache.set(
+ "video",
+ url_hash,
+ {
+ "transcription": portuguese_text,
+ "summary": summary,
+ "metadata": metadata,
+ "timestamp": datetime.now().isoformat(),
+ },
+ )
+ else:
+ portuguese_text = _transcribe_media_to_portuguese(
+ audio_file=audio_file,
+ cache_prefix="transcription",
+ cache_identifier=calculate_file_hash(audio_file),
+ model=model,
+ gpt_model=gpt_model,
+ metadata=metadata,
+ progress_callback=progress_callback,
+ )
+ if mode == "summarize":
+ summary = _summarize_portuguese_text(portuguese_text, gpt_model, progress_callback)
+ if cache:
+ cache.set(
+ "video",
+ url_hash,
+ {
+ "transcription": portuguese_text,
+ "summary": summary,
+ "metadata": metadata,
+ "timestamp": datetime.now().isoformat(),
+ },
+ )
+ except MusicContentError:
+ raise
+ except Exception:
+ cached = cache.get("web", url_hash) if cache else None
+ if cached and cached.get("content"):
+ metadata = cached.get("metadata", {})
+ portuguese_text = cached.get("content")
+ summary = cached.get("summary")
+ _notify(progress_callback, 70, "processing", "Conteudo web encontrado no cache")
+ if mode == "summarize" and not summary:
+ summary = _summarize_portuguese_text(portuguese_text, gpt_model, progress_callback)
+ if cache:
+ cache.set(
+ "web",
+ url_hash,
+ {
+ "content": portuguese_text,
+ "summary": summary,
+ "metadata": metadata,
+ "timestamp": datetime.now().isoformat(),
+ },
+ )
+ else:
+ _notify(progress_callback, 20, "processing", "Extraindo texto da pagina web...")
+ content_data = extract_web_content(source)
+ metadata = {
+ "title": content_data.get("title", "Pagina Web"),
+ "webpage_url": source,
+ }
+ portuguese_text = render_text_in_portuguese(content_data["content"], model=gpt_model)
+ _notify(progress_callback, 70, "processing", "Texto convertido para portugues")
+ if mode == "summarize":
+ summary = _summarize_portuguese_text(portuguese_text, gpt_model, progress_callback)
+ if cache:
+ cache.set(
+ "web",
+ url_hash,
+ {
+ "content": portuguese_text,
+ "summary": summary,
+ "metadata": metadata,
+ "timestamp": datetime.now().isoformat(),
+ },
+ )
+
+ elif input_type in {"audio", "video"}:
+ _notify(progress_callback, 15, "processing", "Preparando audio...")
+ audio_file = prepare_audio_file(source, is_video=(input_type == "video"))
+ if audio_file != source:
+ files_to_cleanup.append(audio_file)
+ portuguese_text = _transcribe_media_to_portuguese(
+ audio_file=audio_file,
+ cache_prefix="transcription",
+ cache_identifier=calculate_file_hash(audio_file),
+ model=model,
+ gpt_model=gpt_model,
+ metadata=metadata,
+ progress_callback=progress_callback,
+ )
+ if mode == "summarize":
+ summary = _summarize_portuguese_text(portuguese_text, gpt_model, progress_callback)
+
+ elif input_type in {"pdf", "text"}:
+ _notify(progress_callback, 20, "processing", "Extraindo conteudo do arquivo...")
+ if input_type == "pdf":
+ content_data = extract_pdf_content(source)
+ else:
+ content_data = extract_text_file_content(source)
+ metadata = {
+ "title": content_data.get("title", "Documento"),
+ "file_path": source,
+ }
+ portuguese_text = render_text_in_portuguese(content_data["content"], model=gpt_model)
+ _notify(progress_callback, 70, "processing", "Conteudo convertido para portugues")
+ if mode == "summarize":
+ summary = _summarize_portuguese_text(portuguese_text, gpt_model, progress_callback)
+
+ if metadata is None:
+ metadata = {}
+ if source.startswith(("http://", "https://")) and not metadata.get("webpage_url"):
+ metadata = {**metadata, "webpage_url": source}
+
+ run_id = run_id or datetime.now().strftime("%Y%m%d%H%M%S%f")
+ resolved_source_name = source_name or metadata.get("title") or source
+
+ if output_path:
+ final_output_path = Path(output_path)
+ if final_output_path.suffix != f".{output_format}":
+ final_output_path = final_output_path.with_suffix(f".{output_format}")
+ else:
+ artifact_kind = "transcription" if mode == "transcribe" else "summary"
+ final_output_path = build_job_artifact_path(
+ job_id=run_id,
+ source_name=resolved_source_name,
+ format_type=output_format,
+ artifact_kind=artifact_kind,
+ created_at=created_at,
+ output_root=output_root,
+ )
+
+ _notify(progress_callback, 92, "processing", f"Gerando arquivo {output_format.upper()}...")
+ exported_path = _export_selected_artifact(
+ mode=mode,
+ format_type=output_format,
+ output_path=str(final_output_path),
+ transcription=portuguese_text or "",
+ summary=summary,
+ metadata=metadata,
+ )
+
+ output_dir = str(Path(exported_path).parent)
+ result = {
+ "mode": mode,
+ "input_type": input_type,
+ "source_name": resolved_source_name,
+ "metadata": metadata,
+ "transcription": portuguese_text,
+ "summary": summary,
+ "result_path": exported_path,
+ "transcription_path": exported_path if mode == "transcribe" else None,
+ "summary_path": exported_path if mode == "summarize" else None,
+ "output_dir": output_dir,
+ }
+ _notify(progress_callback, 100, "completed", "Processamento concluido")
+ return result
+ finally:
+ if not keep_files:
+ cleanup_files(files_to_cleanup)
diff --git a/lazier/docx_generator.py b/lazier/docx_generator.py
@@ -5,9 +5,13 @@ Módulo para geração de arquivos DOCX com transcrição e sumário
import re
from datetime import datetime
from pathlib import Path
-from docx import Document
-from docx.shared import Pt, Inches, RGBColor
-from docx.enum.text import WD_ALIGN_PARAGRAPH
+try:
+ from docx import Document
+ from docx.shared import Pt, Inches, RGBColor
+ from docx.enum.text import WD_ALIGN_PARAGRAPH
+except ImportError: # pragma: no cover - ambiente sem python-docx
+ Document = None
+ Pt = Inches = RGBColor = WD_ALIGN_PARAGRAPH = None
from typing import Optional, Dict, Any
from .utils import sanitize_xml_string
@@ -31,6 +35,8 @@ def create_document(
Returns:
Caminho do arquivo criado
"""
+ if Document is None:
+ raise Exception("python-docx não está instalado neste ambiente.")
doc = Document()
# Configuração de estilo padrão
@@ -90,20 +96,19 @@ def create_document(
doc.add_paragraph()
# Seção de Transcrição
- doc.add_heading('Transcrição Completa', level=2)
-
- # Sanitizar transcrição antes de processar
transcription = sanitize_xml_string(transcription)
-
- # Divide transcrição em parágrafos
- transcription_paragraphs = transcription.split('\n\n')
- if len(transcription_paragraphs) == 1:
- # Se não tem \n\n, tenta dividir por \n
- transcription_paragraphs = transcription.split('\n')
-
- for para_text in transcription_paragraphs:
- if para_text.strip():
- _add_markdown_paragraph(doc, para_text.strip(), is_summary=False)
+ if transcription.strip():
+ doc.add_heading('Transcrição Completa', level=2)
+
+ # Divide transcrição em parágrafos
+ transcription_paragraphs = transcription.split('\n\n')
+ if len(transcription_paragraphs) == 1:
+ # Se não tem \n\n, tenta dividir por \n
+ transcription_paragraphs = transcription.split('\n')
+
+ for para_text in transcription_paragraphs:
+ if para_text.strip():
+ _add_markdown_paragraph(doc, para_text.strip(), is_summary=False)
# Rodapé
doc.add_paragraph()
diff --git a/lazier/downloader.py b/lazier/downloader.py
@@ -10,7 +10,11 @@ import re
from pathlib import Path
from typing import Optional, Dict, Any, Tuple, Type
from urllib.parse import urlparse
-import yt_dlp
+
+try:
+ import yt_dlp
+except ImportError: # pragma: no cover - ambiente sem yt-dlp
+ yt_dlp = None
from .core.exceptions import (
YouTubeDownloadError,
@@ -21,6 +25,11 @@ from .core.exceptions import (
logger = logging.getLogger(__name__)
+
+def _require_yt_dlp() -> None:
+ if yt_dlp is None:
+ raise Exception("yt-dlp não está instalado neste ambiente.")
+
# Domínios exclusivamente de música: não processar em hipótese alguma
MUSIC_ONLY_DOMAINS = frozenset({
'spotify.com', 'open.spotify.com', 'music.apple.com', 'itunes.apple.com',
@@ -244,6 +253,7 @@ def download_youtube_audio(url: str, output_dir: Optional[str] = None) -> tuple[
MusicContentError: Se o conteúdo for detectado como música
Exception: Se o download falhar após todas as tentativas
"""
+ _require_yt_dlp()
if is_music_domain(url):
raise MusicContentError(
"Conteúdo detectado como música não é processado pelo Lazier."
@@ -462,6 +472,7 @@ def download_video_audio(url: str, output_dir: Optional[str] = None) -> tuple[st
MusicContentError: Se o conteúdo for detectado como música
Exception: Se a URL não for vídeo ou o download falhar
"""
+ _require_yt_dlp()
if is_music_domain(url):
raise MusicContentError(
"Conteúdo detectado como música não é processado pelo Lazier."
diff --git a/lazier/summarizer.py b/lazier/summarizer.py
@@ -5,8 +5,15 @@ Suporta textos, páginas web e PDFs
import os
from typing import Optional
-from openai import OpenAI
-from dotenv import load_dotenv
+try:
+ from openai import OpenAI
+except ImportError: # pragma: no cover - ambiente sem openai
+ OpenAI = None
+try:
+ from dotenv import load_dotenv
+except ImportError: # pragma: no cover - ambiente sem python-dotenv
+ def load_dotenv():
+ return False
from .web.extractor import extract_web_content, extract_pdf_content, extract_text_file_content
@@ -31,7 +38,9 @@ def summarize_text(text: str, model: str = 'gpt-4o-mini', language: str = 'pt-BR
"OPENAI_API_KEY não encontrada. "
"Configure a variável de ambiente OPENAI_API_KEY ou crie um arquivo .env"
)
-
+ if OpenAI is None:
+ raise Exception("openai não está instalado neste ambiente.")
+
if not text or not text.strip():
return "Texto vazio - não é possível gerar sumário."
@@ -73,6 +82,71 @@ def summarize_text(text: str, model: str = 'gpt-4o-mini', language: str = 'pt-BR
return valid_summaries[0]
+def render_text_in_portuguese(text: str, model: str = 'gpt-4o-mini') -> str:
+ """
+ Converte qualquer texto para portugues do Brasil preservando detalhes.
+ Se o texto ja estiver em portugues, apenas normaliza a redacao.
+ """
+ api_key = os.getenv('OPENAI_API_KEY')
+ if not api_key:
+ raise Exception(
+ "OPENAI_API_KEY não encontrada. "
+ "Configure a variável de ambiente OPENAI_API_KEY ou crie um arquivo .env"
+ )
+ if OpenAI is None:
+ raise Exception("openai não está instalado neste ambiente.")
+
+ if not text or not text.strip():
+ return ""
+
+ max_chars = 300000
+ if len(text) <= max_chars:
+ return _render_portuguese_chunk(text, model)
+
+ chunks = _split_text_into_chunks(text, max_chars)
+ rendered_chunks = []
+ print(f"Texto longo detectado ({len(text)} caracteres). Convertendo {len(chunks)} partes para portugues...")
+ for i, chunk in enumerate(chunks):
+ print(f"Convertendo parte {i+1}/{len(chunks)}...")
+ rendered_chunks.append(_render_portuguese_chunk(chunk, model))
+ return "\n\n".join(chunk.strip() for chunk in rendered_chunks if chunk.strip())
+
+
+def _render_portuguese_chunk(text: str, model: str) -> str:
+ api_key = os.getenv('OPENAI_API_KEY')
+ client = OpenAI(api_key=api_key)
+ prompt = """Converta o texto a seguir para portugues do Brasil.
+
+Regras:
+- Se o texto ja estiver em portugues, mantenha em portugues do Brasil natural.
+- Nao resuma, nao explique, nao comente o texto.
+- Preserve nomes proprios, numeros, datas, links, listas e estrutura.
+- Mantenha o maximo de fidelidade possivel ao conteudo original.
+
+Texto:
+
+"""
+
+ try:
+ response = client.chat.completions.create(
+ model=model,
+ messages=[
+ {
+ "role": "system",
+ "content": "Voce e um tradutor tecnico e editorial que preserva fielmente o conteudo."
+ },
+ {"role": "user", "content": prompt + text}
+ ],
+ temperature=0.1,
+ )
+ return response.choices[0].message.content.strip()
+ except Exception as e:
+ error_msg = str(e)
+ if 'api_key' in error_msg.lower() or 'authentication' in error_msg.lower():
+ raise Exception("Erro de autenticação com OpenAI API. Verifique sua OPENAI_API_KEY.")
+ raise Exception(f"Erro ao converter texto para português: {error_msg}")
+
+
def _summarize_chunk(text: str, model: str, language: str, is_final: bool = False) -> str:
"""Sumariza um chunk de texto"""
api_key = os.getenv('OPENAI_API_KEY')
diff --git a/lazier/transcriber.py b/lazier/transcriber.py
@@ -5,8 +5,15 @@ Módulo para transcrição de áudio usando OpenAI Whisper API
import os
from pathlib import Path
from typing import Optional
-from openai import OpenAI
-from dotenv import load_dotenv
+try:
+ from openai import OpenAI
+except ImportError: # pragma: no cover - ambiente sem openai
+ OpenAI = None
+try:
+ from dotenv import load_dotenv
+except ImportError: # pragma: no cover - ambiente sem python-dotenv
+ def load_dotenv():
+ return False
load_dotenv()
@@ -14,14 +21,14 @@ load_dotenv()
from .audio_processor import split_audio
-def transcribe_audio(audio_path: str, language: str = 'pt', model: str = 'whisper-1') -> str:
+def transcribe_audio(audio_path: str, language: Optional[str] = None, model: str = 'whisper-1') -> str:
"""
Transcreve um arquivo de áudio usando OpenAI Whisper API,
com suporte a divisão automática de arquivos grandes.
Args:
audio_path: Caminho do arquivo de áudio
- language: Código do idioma (pt para português)
+ language: Codigo do idioma. Quando None, a API detecta automaticamente.
model: Modelo Whisper a usar (padrão: whisper-1)
Returns:
@@ -33,7 +40,9 @@ def transcribe_audio(audio_path: str, language: str = 'pt', model: str = 'whispe
"OPENAI_API_KEY não encontrada. "
"Configure a variável de ambiente OPENAI_API_KEY ou crie um arquivo .env"
)
-
+ if OpenAI is None:
+ raise Exception("openai não está instalado neste ambiente.")
+
if not os.path.exists(audio_path):
raise FileNotFoundError(f"Arquivo de áudio não encontrado: {audio_path}")
@@ -52,12 +61,14 @@ def transcribe_audio(audio_path: str, language: str = 'pt', model: str = 'whispe
for i, chunk_path in enumerate(chunks):
print(f"Processando chunk {i+1}/{len(chunks)}...")
with open(chunk_path, 'rb') as audio_file:
- transcript = client.audio.transcriptions.create(
- model=model,
- file=audio_file,
- language=language,
- response_format='text'
- )
+ request_kwargs = {
+ 'model': model,
+ 'file': audio_file,
+ 'response_format': 'text'
+ }
+ if language:
+ request_kwargs['language'] = language
+ transcript = client.audio.transcriptions.create(**request_kwargs)
if hasattr(transcript, 'text'):
text = transcript.text
@@ -70,12 +81,14 @@ def transcribe_audio(audio_path: str, language: str = 'pt', model: str = 'whispe
else:
# Caso contrário, transcreve direto
with open(audio_path, 'rb') as audio_file:
- transcript = client.audio.transcriptions.create(
- model=model,
- file=audio_file,
- language=language,
- response_format='text'
- )
+ request_kwargs = {
+ 'model': model,
+ 'file': audio_file,
+ 'response_format': 'text'
+ }
+ if language:
+ request_kwargs['language'] = language
+ transcript = client.audio.transcriptions.create(**request_kwargs)
# Se retornou como objeto, pega o texto
if hasattr(transcript, 'text'):
diff --git a/lazier/web/extractor.py b/lazier/web/extractor.py
@@ -7,16 +7,33 @@ import re
import random
from pathlib import Path
from typing import Optional, Dict, Any
-import requests
-from bs4 import BeautifulSoup
-import pypdf
-import pdfplumber
+try:
+ import requests
+except ImportError: # pragma: no cover - ambiente sem requests
+ requests = None
+
+try:
+ from bs4 import BeautifulSoup
+except ImportError: # pragma: no cover - ambiente sem beautifulsoup4
+ BeautifulSoup = None
+
+try:
+ import pypdf
+except ImportError: # pragma: no cover - ambiente sem pypdf
+ pypdf = None
+
+try:
+ import pdfplumber
+except ImportError: # pragma: no cover - ambiente sem pdfplumber
+ pdfplumber = None
from ..utils import sanitize_xml_string
def _extract_with_bs4(url: str, timeout: int) -> Dict[str, Any]:
"""Extrai conteúdo usando BeautifulSoup (método rápido, sem JavaScript)"""
+ if requests is None or BeautifulSoup is None:
+ raise Exception("requests e beautifulsoup4 são necessários para extrair conteúdo web.")
# User-Agents rotativos para evitar bloqueios
user_agents = [
'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36',
@@ -259,15 +276,14 @@ def extract_web_content(url: str, timeout: int = 30, use_js: bool = True) -> Dic
return content
- except requests.exceptions.RequestException as e:
- # Se requests falhar, tentar com Playwright se use_js estiver habilitado
- if use_js:
- try:
- return _extract_with_playwright(url, timeout)
- except Exception:
- pass
- raise Exception(f"Erro ao acessar URL: {str(e)}")
except Exception as e:
+ if requests is not None and isinstance(e, requests.exceptions.RequestException):
+ if use_js:
+ try:
+ return _extract_with_playwright(url, timeout)
+ except Exception:
+ pass
+ raise Exception(f"Erro ao acessar URL: {str(e)}")
raise Exception(f"Erro ao extrair conteúdo web: {str(e)}")
@@ -281,6 +297,8 @@ def extract_pdf_content(file_path: str) -> Dict[str, Any]:
Returns:
Dicionário com conteúdo extraído e metadados
"""
+ if pdfplumber is None and pypdf is None:
+ raise Exception("pdfplumber ou pypdf são necessários para processar PDFs.")
import warnings
import logging
diff --git a/lazier/web/templates/index.html b/lazier/web/templates/index.html
@@ -3,1670 +3,385 @@
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
- <title>Lazier - Transcrição e Sumarização</title>
- <link rel="icon" href="data:image/svg+xml,<svg xmlns=%22http://www.w3.org/2000/svg%22 viewBox=%220 0 100 100%22><text y=%22.9em%22 font-size=%2290%22>📢</text></svg>">
+ <title>Lazier</title>
<link rel="preconnect" href="https://fonts.googleapis.com">
<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
- <link href="https://fonts.googleapis.com/css2?family=Playfair+Display:wght@400;600;700&family=Work+Sans:wght@300;400;500;600;700&display=swap" rel="stylesheet">
+ <link href="https://fonts.googleapis.com/css2?family=Fraunces:wght@500;700&family=Manrope:wght@400;500;600;700&display=swap" rel="stylesheet">
<script src="https://cdn.jsdelivr.net/npm/marked/marked.min.js"></script>
<style>
- /* ===== VARIÁVEIS CSS ===== */
- :root {
- --color-primary: #283593;
- --color-primary-dark: #1a237e;
- --color-primary-light: #3949ab;
- --color-secondary: #ffb300;
- --color-accent: #ffa000;
- --color-accent-light: #ffc107;
- --color-bg: #fafafa;
- --color-surface: #ffffff;
- --color-text: #263238;
- --color-text-light: #546e7a;
- --color-text-lighter: #78909c;
- --color-border: #e0e0e0;
- --color-border-light: #f5f5f5;
- --font-display: 'Playfair Display', serif;
- --font-body: 'Work Sans', sans-serif;
- --spacing-xs: 2px;
- --spacing-sm: 4px;
- --spacing-md: 8px;
- --spacing-lg: 12px;
- --spacing-xl: 16px;
- --spacing-2xl: 24px;
- --spacing-3xl: 32px;
- --border-radius-sm: 8px;
- --border-radius-md: 12px;
- --border-radius-lg: 16px;
- --border-radius-xl: 24px;
- --shadow-sm: 0 2px 4px rgba(0,0,0,0.08);
- --shadow-md: 0 4px 12px rgba(0,0,0,0.12);
- --shadow-lg: 0 8px 24px rgba(0,0,0,0.16);
- --shadow-xl: 0 12px 40px rgba(0,0,0,0.2);
- --transition-fast: 0.15s ease;
- --transition-base: 0.3s ease;
- --transition-slow: 0.5s ease;
- }
-
- /* ===== RESET E BASE ===== */
- * {
- margin: 0;
- padding: 0;
- box-sizing: border-box;
- /* Firefox scrollbar */
- scrollbar-width: thin;
- scrollbar-color: var(--color-secondary) rgba(255, 255, 255, 0.1);
- }
-
- html {
- scroll-behavior: smooth;
- }
-
- /* ===== SCROLLBARS CUSTOMIZADAS ===== */
- /* Webkit (Chrome, Safari, Edge) */
- ::-webkit-scrollbar {
- width: 12px;
- height: 12px;
- }
-
- ::-webkit-scrollbar-track {
- background: rgba(255, 255, 255, 0.1);
- border-radius: 10px;
- margin: 4px;
- }
-
- ::-webkit-scrollbar-thumb {
- background: linear-gradient(135deg, var(--color-primary) 0%, var(--color-secondary) 100%);
- border-radius: 10px;
- border: 2px solid transparent;
- background-clip: padding-box;
- transition: all var(--transition-base);
- }
-
- ::-webkit-scrollbar-thumb:hover {
- background: linear-gradient(135deg, var(--color-primary-light) 0%, var(--color-accent) 100%);
- transform: scaleY(1.1);
- }
-
- ::-webkit-scrollbar-corner {
- background: rgba(255, 255, 255, 0.1);
- border-radius: 10px;
- }
-
- body {
- font-family: var(--font-body);
- background: linear-gradient(135deg, #1a237e 0%, #283593 50%, #3949ab 100%);
- background-attachment: fixed;
- min-height: 100vh;
- color: var(--color-text);
- line-height: 1.5;
- padding-top: 60px;
- padding-bottom: 0;
- display: flex;
- flex-direction: column;
- position: relative;
- overflow-x: hidden;
- }
-
- /* Background texture overlay */
- body::before {
- content: '';
- position: fixed;
- top: 0;
- left: 0;
- width: 100%;
- height: 100%;
- background-image:
- radial-gradient(circle at 20% 50%, rgba(255, 179, 0, 0.1) 0%, transparent 50%),
- radial-gradient(circle at 80% 80%, rgba(255, 160, 0, 0.1) 0%, transparent 50%);
- pointer-events: none;
- z-index: 0;
- }
-
- /* ===== NAVBAR ===== */
- .navbar {
- position: fixed;
- top: 0;
- left: 0;
- right: 0;
- height: 60px;
- background: rgba(26, 35, 126, 0.85);
- backdrop-filter: blur(20px);
- -webkit-backdrop-filter: blur(20px);
- padding: var(--spacing-sm) var(--spacing-md);
- box-shadow: var(--shadow-sm);
- z-index: 1000;
- display: flex;
- justify-content: space-between;
- align-items: center;
- border-bottom: 1px solid rgba(255, 255, 255, 0.1);
- transition: var(--transition-base);
- }
-
- .navbar-brand {
- font-family: var(--font-display);
- color: #ffffff;
- font-size: clamp(1.125rem, 3vw, 1.5rem);
- font-weight: 700;
- text-decoration: none;
- letter-spacing: -0.5px;
- transition: var(--transition-base);
- display: flex;
- align-items: center;
- gap: var(--spacing-xs);
- }
-
- .navbar-brand:hover {
- transform: translateY(-2px);
- text-shadow: 0 4px 12px rgba(255, 179, 0, 0.3);
- }
-
- .navbar-nav {
- display: flex;
- gap: var(--spacing-md);
- list-style: none;
- }
-
- .navbar-nav a {
- color: rgba(255, 255, 255, 0.9);
- text-decoration: none;
- font-weight: 500;
- font-size: 0.9rem;
- padding: var(--spacing-xs) 0;
- position: relative;
- transition: var(--transition-base);
- }
-
- .navbar-nav a::after {
- content: '';
- position: absolute;
- bottom: 0;
- left: 0;
- width: 0;
- height: 2px;
- background: var(--color-secondary);
- transition: var(--transition-base);
- }
-
- .navbar-nav a:hover,
- .navbar-nav a.active {
- color: #ffffff;
- }
-
- .navbar-nav a:hover::after,
- .navbar-nav a.active::after {
- width: 100%;
- }
-
- .mobile-menu-toggle {
- display: none;
- background: none;
- border: none;
- color: #ffffff;
- font-size: 1.5rem;
- cursor: pointer;
- padding: var(--spacing-sm);
- }
-
- /* ===== CONTAINER ===== */
- .container {
- flex: 1;
- max-width: 1200px;
- margin: 0 auto;
- padding: var(--spacing-md) var(--spacing-sm);
- width: 100%;
- position: relative;
- z-index: 1;
- }
-
- .page {
- display: none;
- background: var(--color-surface);
- border-radius: var(--border-radius-md);
- box-shadow: var(--shadow-sm);
- padding: var(--spacing-md) var(--spacing-sm);
- height: calc(100vh - 120px);
- animation: fadeIn 0.5s ease;
- position: relative;
- overflow: hidden;
- }
-
- .page::before {
- content: '';
- position: absolute;
- top: 0;
- left: 0;
- right: 0;
- height: 2px;
- background: linear-gradient(90deg, var(--color-primary), var(--color-secondary), var(--color-accent));
- }
-
- .page.active {
- display: flex;
- flex-direction: column;
- overflow: hidden;
- }
-
- .page-content {
- flex: 1;
- overflow-y: auto;
- overflow-x: hidden;
- padding: var(--spacing-sm);
- display: flex;
- flex-direction: column;
- }
-
- .page-content::-webkit-scrollbar {
- width: 10px;
- }
-
- .page-content::-webkit-scrollbar-thumb {
- background: linear-gradient(135deg, rgba(40, 53, 147, 0.3) 0%, rgba(255, 179, 0, 0.3) 100%);
- }
-
- @keyframes fadeIn {
- from {
- opacity: 0;
- transform: translateY(20px);
- }
- to {
- opacity: 1;
- transform: translateY(0);
- }
- }
-
- /* ===== TIPOGRAFIA ===== */
- body {
- font-size: 0.875rem;
- line-height: 1.5;
- }
-
- h1 {
- font-family: var(--font-display);
- color: var(--color-primary);
- margin-bottom: var(--spacing-sm);
- font-size: clamp(1.5rem, 3vw, 2rem);
- font-weight: 700;
- line-height: 1.2;
- letter-spacing: -1px;
- }
-
- h2 {
- font-family: var(--font-display);
- color: var(--color-primary);
- font-size: clamp(1.25rem, 2.5vw, 1.75rem);
- font-weight: 600;
- margin-bottom: var(--spacing-sm);
- }
-
- h3 {
- font-family: var(--font-display);
- color: var(--color-text);
- font-size: clamp(1rem, 2vw, 1.25rem);
- font-weight: 600;
- margin-bottom: var(--spacing-sm);
- }
-
- .subtitle {
- color: var(--color-text-light);
- margin-bottom: var(--spacing-lg);
- font-size: clamp(0.75rem, 1.5vw, 0.9rem);
- font-weight: 400;
- }
-
- /* ===== UPLOAD AREA ===== */
- .upload-area {
- border: 2px dashed var(--color-border);
- border-radius: var(--border-radius-md);
- padding: var(--spacing-lg) var(--spacing-md);
- text-align: center;
- background: linear-gradient(135deg, rgba(255, 179, 0, 0.03) 0%, rgba(255, 160, 0, 0.05) 100%);
- transition: all var(--transition-base);
- cursor: pointer;
- margin-bottom: var(--spacing-md);
- position: relative;
- overflow: hidden;
- }
-
- .upload-area::before {
- content: '';
- position: absolute;
- top: -50%;
- left: -50%;
- width: 200%;
- height: 200%;
- background: linear-gradient(45deg, transparent, rgba(255, 179, 0, 0.1), transparent);
- transform: rotate(45deg);
- transition: var(--transition-slow);
- opacity: 0;
- }
-
- .upload-area:hover::before {
- animation: shimmer 2s infinite;
- opacity: 1;
- }
-
- @keyframes shimmer {
- 0% { transform: translateX(-100%) translateY(-100%) rotate(45deg); }
- 100% { transform: translateX(100%) translateY(100%) rotate(45deg); }
- }
-
- .upload-area:hover {
- border-color: var(--color-secondary);
- background: linear-gradient(135deg, rgba(255, 179, 0, 0.08) 0%, rgba(255, 160, 0, 0.12) 100%);
- transform: translateY(-2px);
- box-shadow: var(--shadow-md);
- }
-
- .upload-area.dragover {
- border-color: var(--color-secondary);
- background: linear-gradient(135deg, rgba(255, 179, 0, 0.15) 0%, rgba(255, 160, 0, 0.2) 100%);
- transform: scale(1.01);
- box-shadow: var(--shadow-md);
- }
-
- .upload-icon {
- font-size: clamp(2rem, 5vw, 3rem);
- margin-bottom: var(--spacing-md);
- display: block;
- animation: float 3s ease-in-out infinite;
- }
-
- @keyframes float {
- 0%, 100% { transform: translateY(0); }
- 50% { transform: translateY(-10px); }
- }
-
- .upload-area h3 {
- font-size: clamp(0.9rem, 2.5vw, 1.125rem);
- color: var(--color-text);
- margin-bottom: var(--spacing-xs);
- }
-
- .upload-area p {
- color: var(--color-text-light);
- font-size: clamp(0.75rem, 1.5vw, 0.875rem);
- }
-
- .file-input {
- display: none;
- }
-
- /* ===== URL INPUT ===== */
- .url-input {
- width: 100%;
- padding: var(--spacing-sm) var(--spacing-md);
- border: 1px solid var(--color-border);
- border-radius: var(--border-radius-sm);
- font-size: 0.875rem;
- font-family: var(--font-body);
- margin-bottom: var(--spacing-md);
- transition: all var(--transition-base);
- background: var(--color-surface);
- color: var(--color-text);
- }
-
- .url-input:focus {
- outline: none;
- border-color: var(--color-secondary);
- box-shadow: 0 0 0 3px rgba(255, 179, 0, 0.1);
- }
-
- /* ===== PROCESSING OPTIONS ===== */
- .processing-options {
- margin: var(--spacing-lg) 0;
- padding: var(--spacing-md);
- background: linear-gradient(135deg, rgba(40, 53, 147, 0.03) 0%, rgba(26, 35, 126, 0.05) 100%);
- border-radius: var(--border-radius-md);
- border: 1px solid var(--color-border-light);
- }
-
- .processing-options h3 {
- margin-bottom: var(--spacing-md);
- color: var(--color-primary);
- }
-
- .option-cards {
- display: grid;
- grid-template-columns: repeat(auto-fit, minmax(150px, 1fr));
- gap: var(--spacing-md);
- margin-top: var(--spacing-md);
- }
-
- .option-card {
- padding: var(--spacing-md) var(--spacing-sm);
- border: 1px solid var(--color-border);
- border-radius: var(--border-radius-sm);
- cursor: pointer;
- transition: all var(--transition-base);
- text-align: center;
- background: var(--color-surface);
- position: relative;
- overflow: hidden;
- }
-
- .option-card::before {
- content: '';
- position: absolute;
- top: 0;
- left: 0;
- right: 0;
- height: 2px;
- background: linear-gradient(90deg, var(--color-primary), var(--color-secondary));
- transform: scaleX(0);
- transition: var(--transition-base);
- }
-
- .option-card:hover {
- border-color: var(--color-secondary);
- transform: translateY(-2px);
- box-shadow: var(--shadow-sm);
- }
-
- .option-card:hover::before {
- transform: scaleX(1);
- }
-
- .option-card.selected {
- border-color: var(--color-secondary);
- background: linear-gradient(135deg, rgba(255, 179, 0, 0.08) 0%, rgba(255, 160, 0, 0.12) 100%);
- box-shadow: var(--shadow-sm);
- }
-
- .option-card.selected::before {
- transform: scaleX(1);
- }
-
- .option-card input[type="radio"] {
- margin-right: var(--spacing-sm);
- accent-color: var(--color-secondary);
- }
-
- .option-card label {
- cursor: pointer;
- font-weight: 600;
- color: var(--color-text);
- display: flex;
- align-items: center;
- justify-content: center;
- font-size: 0.9rem;
- }
-
- .option-card .description {
- margin-top: var(--spacing-xs);
- font-size: 0.75rem;
- color: var(--color-text-light);
- font-weight: 400;
- }
-
- /* ===== OPTIONS GRID ===== */
- .options {
- margin-top: var(--spacing-md);
- display: grid;
- grid-template-columns: repeat(auto-fit, minmax(150px, 1fr));
- gap: var(--spacing-md);
- }
-
- .option-group {
- display: flex;
- flex-direction: column;
- }
-
- .option-group label {
- margin-bottom: var(--spacing-xs);
- font-weight: 600;
- color: var(--color-text);
- font-size: 0.8rem;
- }
-
- .option-group select {
- padding: var(--spacing-sm) var(--spacing-md);
- border: 1px solid var(--color-border);
- border-radius: var(--border-radius-sm);
- font-size: 0.875rem;
- font-family: var(--font-body);
- background: var(--color-surface);
- color: var(--color-text);
- cursor: pointer;
- transition: all var(--transition-base);
- }
-
- .option-group select:focus {
- outline: none;
- border-color: var(--color-secondary);
- box-shadow: 0 0 0 3px rgba(255, 179, 0, 0.1);
- }
-
- /* ===== BUTTONS ===== */
- .btn {
- background: linear-gradient(135deg, var(--color-primary) 0%, var(--color-primary-light) 100%);
- color: white;
- border: none;
- padding: var(--spacing-sm) var(--spacing-lg);
- border-radius: var(--border-radius-sm);
- font-size: 0.9rem;
- font-weight: 600;
- font-family: var(--font-body);
- cursor: pointer;
- margin-top: var(--spacing-md);
- width: 100%;
- transition: all var(--transition-base);
- position: relative;
- overflow: hidden;
- box-shadow: var(--shadow-sm);
- }
-
- .btn::before {
- content: '';
- position: absolute;
- top: 50%;
- left: 50%;
- width: 0;
- height: 0;
- border-radius: 50%;
- background: rgba(255, 255, 255, 0.3);
- transform: translate(-50%, -50%);
- transition: width 0.6s, height 0.6s;
- }
-
- .btn:hover::before {
- width: 300px;
- height: 300px;
- }
-
- .btn:hover {
- transform: translateY(-2px);
- box-shadow: var(--shadow-md);
- }
-
- .btn:active {
- transform: translateY(0);
- box-shadow: var(--shadow-sm);
- }
-
- .btn:disabled {
- opacity: 0.6;
- cursor: not-allowed;
- transform: none;
- }
-
- .btn-secondary {
- background: linear-gradient(135deg, var(--color-text-light) 0%, var(--color-text) 100%);
- margin-top: var(--spacing-sm);
- }
-
- .btn-small {
- padding: var(--spacing-xs) var(--spacing-md);
- font-size: 0.8rem;
- width: auto;
- margin: 0;
- }
-
- /* ===== JOBS LIST ===== */
- .jobs-list {
- margin-top: var(--spacing-md);
- }
-
- .job-card {
- background: var(--color-surface);
- border-radius: var(--border-radius-sm);
- padding: var(--spacing-md) var(--spacing-sm);
- margin-bottom: var(--spacing-sm);
- border-left: 2px solid var(--color-primary);
- box-shadow: var(--shadow-sm);
- transition: all var(--transition-base);
- animation: slideIn 0.4s ease;
- }
-
- @keyframes slideIn {
- from {
- opacity: 0;
- transform: translateX(-20px);
- }
- to {
- opacity: 1;
- transform: translateX(0);
- }
- }
-
- .job-card:hover {
- box-shadow: var(--shadow-sm);
- transform: translateX(2px);
- }
-
- .job-header {
- display: flex;
- justify-content: space-between;
- align-items: center;
- margin-bottom: var(--spacing-xs);
- flex-wrap: wrap;
- gap: var(--spacing-xs);
- }
-
- .job-title {
- font-weight: 600;
- color: var(--color-text);
- font-size: 0.875rem;
- }
-
- .job-status {
- padding: var(--spacing-xs) var(--spacing-sm);
- border-radius: 12px;
- font-size: 0.7rem;
- font-weight: 600;
- text-transform: uppercase;
- letter-spacing: 0.3px;
- }
-
- .status-pending {
- background: rgba(255, 193, 7, 0.2);
- color: #f57c00;
- }
- .status-processing {
- background: rgba(33, 150, 243, 0.2);
- color: #1976d2;
- }
- .status-completed {
- background: rgba(76, 175, 80, 0.2);
- color: #388e3c;
- }
- .status-failed {
- background: rgba(244, 67, 54, 0.2);
- color: #d32f2f;
- }
-
- .progress-bar {
- width: 100%;
- height: 6px;
- background: var(--color-border-light);
- border-radius: 3px;
- overflow: hidden;
- margin: var(--spacing-xs) 0;
- position: relative;
- }
-
- .progress-fill {
- height: 100%;
- background: linear-gradient(90deg, var(--color-primary) 0%, var(--color-secondary) 50%, var(--color-accent) 100%);
- background-size: 200% 100%;
- transition: width var(--transition-base);
- animation: progressShimmer 2s infinite;
- border-radius: 4px;
- }
-
- @keyframes progressShimmer {
- 0% { background-position: 200% 0; }
- 100% { background-position: -200% 0; }
- }
-
- .job-actions {
- margin-top: var(--spacing-sm);
- display: flex;
- gap: var(--spacing-xs);
- flex-wrap: wrap;
- }
-
- /* ===== FILE LIST ===== */
- .file-list {
- margin-top: var(--spacing-lg);
- }
-
- .file-item {
- background: var(--color-surface);
- padding: var(--spacing-md);
- border-radius: var(--border-radius-sm);
- margin-bottom: var(--spacing-sm);
- display: flex;
- justify-content: space-between;
- align-items: center;
- border: 1px solid var(--color-border-light);
- transition: var(--transition-base);
- }
-
- .file-item:hover {
- background: rgba(255, 179, 0, 0.05);
- border-color: var(--color-secondary);
- }
-
- .btn-remove {
- background: #d32f2f;
- color: white;
- border: none;
- padding: var(--spacing-xs) var(--spacing-md);
- border-radius: var(--border-radius-sm);
- cursor: pointer;
- font-size: 0.875rem;
- margin-left: var(--spacing-md);
- transition: var(--transition-base);
- font-weight: 600;
- }
-
- .btn-remove:hover {
- background: #b71c1c;
- transform: scale(1.05);
- }
-
- /* ===== HISTORY FILTERS ===== */
- .history-filters {
- margin-bottom: var(--spacing-md);
- display: flex;
- gap: var(--spacing-xs);
- flex-wrap: wrap;
- }
-
- .filter-btn {
- padding: var(--spacing-xs) var(--spacing-md);
- border: 1px solid var(--color-border);
- background: var(--color-surface);
- border-radius: var(--border-radius-sm);
- cursor: pointer;
- transition: all var(--transition-base);
- font-family: var(--font-body);
- font-weight: 500;
- color: var(--color-text);
- font-size: 0.8rem;
- }
-
- .filter-btn:hover {
- border-color: var(--color-secondary);
- transform: translateY(-1px);
- box-shadow: var(--shadow-sm);
- }
-
- .filter-btn.active {
- border-color: var(--color-secondary);
- background: linear-gradient(135deg, rgba(255, 179, 0, 0.1) 0%, rgba(255, 160, 0, 0.15) 100%);
- color: var(--color-primary);
- font-weight: 600;
- }
-
- /* ===== PREVIEW CONTENT ===== */
- .preview-content {
- background: linear-gradient(135deg, rgba(255, 255, 255, 0.98) 0%, rgba(250, 250, 250, 0.95) 100%);
- padding: var(--spacing-md);
- border-radius: var(--border-radius-md);
- border: 1px solid rgba(40, 53, 147, 0.1);
- margin-top: var(--spacing-md);
- line-height: 1.6;
- box-shadow:
- 0 2px 8px rgba(0, 0, 0, 0.06),
- inset 0 1px 0 rgba(255, 255, 255, 0.9);
- position: relative;
- transition: all var(--transition-base);
- overflow: visible;
- }
-
- .preview-content::before {
- content: '';
- position: absolute;
- top: 0;
- left: 0;
- right: 0;
- height: 2px;
- background: linear-gradient(90deg, var(--color-primary), var(--color-secondary), var(--color-accent));
- border-radius: var(--border-radius-md) var(--border-radius-md) 0 0;
- z-index: 1;
- }
-
- .preview-content:hover {
- box-shadow:
- 0 4px 12px rgba(0, 0, 0, 0.1),
- inset 0 1px 0 rgba(255, 255, 255, 0.9);
- transform: translateY(-1px);
- }
-
- .preview-content h1, .preview-content h2, .preview-content h3, .preview-content h4 {
- margin-top: var(--spacing-md);
- margin-bottom: var(--spacing-sm);
- color: var(--color-primary);
- font-family: var(--font-display);
- font-weight: 600;
- letter-spacing: -0.02em;
- }
-
- .preview-content h1:first-child,
- .preview-content h2:first-child,
- .preview-content h3:first-child {
- margin-top: 0;
- }
-
- .preview-content h1 {
- font-size: 1.25rem;
- border-bottom: 2px solid var(--color-secondary);
- padding-bottom: var(--spacing-sm);
- }
-
- .preview-content h2 {
- font-size: 1.125rem;
- color: var(--color-primary);
- }
-
- .preview-content h3 {
- font-size: 1rem;
- }
-
- .preview-content p {
- margin-bottom: var(--spacing-sm);
- line-height: 1.6;
- letter-spacing: 0.01em;
- }
-
- .preview-content pre {
- background: linear-gradient(135deg, rgba(40, 53, 147, 0.08) 0%, rgba(40, 53, 147, 0.05) 100%);
- padding: var(--spacing-md);
- border-radius: var(--border-radius-sm);
- overflow-x: auto;
- border-left: 2px solid var(--color-secondary);
- margin: var(--spacing-sm) 0;
- position: relative;
- box-shadow: inset 0 1px 4px rgba(0, 0, 0, 0.04);
- }
-
- .preview-content pre::-webkit-scrollbar {
- height: 8px;
- }
-
- .preview-content pre::-webkit-scrollbar-track {
- background: rgba(40, 53, 147, 0.05);
- border-radius: 4px;
- }
-
- .preview-content pre::-webkit-scrollbar-thumb {
- background: var(--color-secondary);
- border-radius: 4px;
- border: 1px solid transparent;
- background-clip: padding-box;
- }
-
- .preview-content pre::-webkit-scrollbar-thumb:hover {
- background: var(--color-accent);
- }
-
- .preview-content code {
- background: rgba(40, 53, 147, 0.1);
- padding: 2px 6px;
- border-radius: 3px;
- font-family: 'Courier New', monospace;
- font-size: 0.9em;
- }
-
- .preview-content pre code {
- background: transparent;
- padding: 0;
- }
-
- .preview-content ul, .preview-content ol {
- margin: var(--spacing-sm) 0;
- padding-left: var(--spacing-lg);
- }
-
- .preview-content li {
- margin-bottom: var(--spacing-xs);
- line-height: 1.6;
- }
-
- .preview-content blockquote {
- border-left: 2px solid var(--color-secondary);
- padding-left: var(--spacing-md);
- margin: var(--spacing-sm) 0;
- color: var(--color-text-light);
- font-style: italic;
- }
-
- .preview-content hr {
- border: none;
- border-top: 1px solid var(--color-border);
- margin: var(--spacing-md) 0;
- position: relative;
- }
-
- .preview-content hr::after {
- content: '';
- position: absolute;
- top: -1px;
- left: 0;
- width: 60px;
- height: 2px;
- background: linear-gradient(90deg, var(--color-secondary), transparent);
- }
-
- .preview-content strong {
- font-weight: 600;
- color: var(--color-text);
- }
-
- .preview-content a {
- color: var(--color-primary);
- text-decoration: none;
- border-bottom: 1px solid transparent;
- transition: var(--transition-base);
- }
-
- .preview-content a:hover {
- border-bottom-color: var(--color-primary);
- }
-
- .preview-text-plain {
- white-space: pre-wrap;
- word-wrap: break-word;
- word-break: break-word;
- font-family: var(--font-body);
- line-height: 1.9;
- letter-spacing: 0.01em;
- color: var(--color-text);
- padding: var(--spacing-md) 0;
- }
-
- /* ===== FOOTER ===== */
- .main-footer {
- background: linear-gradient(135deg, var(--color-primary-dark) 0%, var(--color-primary) 100%);
- color: rgba(255, 255, 255, 0.9);
- padding: var(--spacing-md) var(--spacing-lg);
- margin-top: var(--spacing-md);
- position: relative;
- overflow: hidden;
- }
-
- .main-footer::before {
- content: '';
- position: absolute;
- top: 0;
- left: 0;
- right: 0;
- height: 1px;
- background: linear-gradient(90deg, transparent, rgba(255, 179, 0, 0.5), transparent);
- }
-
- .footer-content {
- max-width: 1200px;
- margin: 0 auto;
- display: grid;
- grid-template-columns: repeat(auto-fit, minmax(150px, 1fr));
- gap: var(--spacing-md);
- }
-
- .footer-section h4 {
- font-family: var(--font-display);
- color: var(--color-secondary);
- font-size: 0.9rem;
- margin-bottom: var(--spacing-xs);
- font-weight: 600;
- }
-
- .footer-section p,
- .footer-section li {
- color: rgba(255, 255, 255, 0.8);
- font-size: 0.75rem;
- line-height: 1.5;
- margin-bottom: var(--spacing-xs);
- }
-
- .footer-section ul {
- list-style: none;
- padding: 0;
- }
-
- .footer-section a {
- color: rgba(255, 255, 255, 0.9);
- text-decoration: none;
- transition: var(--transition-base);
- display: inline-block;
- }
-
- .footer-section a:hover {
- color: var(--color-secondary);
- transform: translateX(4px);
- }
-
- .footer-bottom {
- max-width: 1200px;
- margin: var(--spacing-md) auto 0;
- padding-top: var(--spacing-sm);
- border-top: 1px solid rgba(255, 255, 255, 0.1);
- text-align: center;
- color: rgba(255, 255, 255, 0.7);
- font-size: 0.75rem;
- }
-
- /* ===== RESPONSIVIDADE ===== */
- @media (max-width: 768px) {
- .navbar {
- padding: var(--spacing-xs) var(--spacing-sm);
- height: 50px;
- }
-
- .navbar-nav {
- position: fixed;
- top: 50px;
- left: 0;
- right: 0;
- background: rgba(26, 35, 126, 0.98);
- backdrop-filter: blur(20px);
- flex-direction: column;
- padding: var(--spacing-md);
- gap: var(--spacing-sm);
- transform: translateX(-100%);
- transition: var(--transition-base);
- box-shadow: var(--shadow-md);
- }
-
- .navbar-nav.active {
- transform: translateX(0);
- }
-
- .mobile-menu-toggle {
- display: block;
- }
-
- .container {
- padding: var(--spacing-sm);
- }
-
- .page {
- padding: var(--spacing-sm);
- border-radius: var(--border-radius-sm);
- height: calc(100vh - 90px);
- }
-
- .option-cards {
- grid-template-columns: 1fr;
- gap: var(--spacing-sm);
- }
-
- .options {
- grid-template-columns: 1fr;
- gap: var(--spacing-sm);
- }
-
- .footer-content {
- grid-template-columns: 1fr;
- gap: var(--spacing-md);
- }
-
- .job-header {
- flex-direction: column;
- align-items: flex-start;
- }
- }
-
- @media (min-width: 769px) and (max-width: 1024px) {
- .footer-content {
- grid-template-columns: repeat(2, 1fr);
- gap: var(--spacing-md);
- }
- }
-
- /* ===== ACESSIBILIDADE ===== */
- *:focus-visible {
- outline: 3px solid var(--color-secondary);
- outline-offset: 2px;
- }
-
- @media (prefers-reduced-motion: reduce) {
- *,
- *::before,
- *::after {
- animation-duration: 0.01ms !important;
- animation-iteration-count: 1 !important;
- transition-duration: 0.01ms !important;
- }
- }
+ :root { --bg:#f5f1ea; --card:rgba(255,255,255,.9); --ink:#223437; --muted:#66787b; --line:rgba(34,52,55,.12); --accent:#2f7a71; --accent-soft:rgba(47,122,113,.1); --warn:#b85f49; }
+ * { box-sizing:border-box; margin:0; padding:0; }
+ body { font-family:'Manrope',sans-serif; color:var(--ink); background:radial-gradient(circle at top left, rgba(47,122,113,.08), transparent 26%), linear-gradient(180deg,#faf7f2 0%,#f2ede6 100%); min-height:100vh; padding:20px; }
+ a { color:inherit; text-decoration:none; }
+ button,input,select { font:inherit; }
+ .shell { max-width:1180px; margin:0 auto; }
+ .topbar,.card,.job,.modal-card { background:var(--card); border:1px solid rgba(255,255,255,.9); box-shadow:0 18px 42px rgba(40,56,60,.08); backdrop-filter:blur(14px); }
+ .topbar { border-radius:999px; padding:16px 20px; display:flex; justify-content:space-between; gap:16px; align-items:center; margin-bottom:20px; flex-wrap:wrap; }
+ .brand { display:flex; gap:12px; align-items:center; }
+ .mark { width:42px; height:42px; border-radius:14px; display:grid; place-items:center; background:linear-gradient(135deg, rgba(47,122,113,.12), rgba(198,125,69,.12)); }
+ .brand h1,.title { font-family:'Fraunces',serif; letter-spacing:-.03em; }
+ .brand p,.subtle,.hero p,.field label,.meta,.empty,.error { color:var(--muted); }
+ .nav { display:flex; gap:8px; flex-wrap:wrap; }
+ .nav a,.nav button,.filter,.action { border:0; background:transparent; border-radius:999px; padding:10px 14px; cursor:pointer; }
+ .nav a.active,.nav a:hover,.nav button:hover,.filter.active { background:var(--accent-soft); color:var(--accent); }
+ .page { display:none; }
+ .page.active { display:block; }
+ .hero { display:grid; grid-template-columns:1.1fr .9fr; gap:18px; }
+ .card { border-radius:28px; padding:28px; }
+ .eyebrow { display:inline-block; background:var(--accent-soft); color:var(--accent); padding:8px 12px; border-radius:999px; font-size:.85rem; font-weight:700; }
+ .hero h2 { font-family:'Fraunces',serif; font-size:clamp(2rem,4vw,3.2rem); line-height:1.04; margin:16px 0; letter-spacing:-.04em; }
+ .hero p { line-height:1.7; max-width:56ch; }
+ .notes { display:grid; grid-template-columns:repeat(3,1fr); gap:10px; margin-top:22px; }
+ .note { border:1px solid var(--line); border-radius:18px; padding:14px; background:rgba(255,255,255,.6); }
+ .note strong { display:block; margin-bottom:6px; }
+ .stack { display:flex; flex-direction:column; gap:16px; }
+ .field { display:flex; flex-direction:column; gap:8px; }
+ .field label { font-size:.88rem; font-weight:700; }
+ .field input,.field select { width:100%; padding:14px 16px; border-radius:16px; border:1px solid var(--line); background:rgba(255,255,255,.95); }
+ .upload { padding:24px; border-radius:24px; border:1.5px dashed rgba(47,122,113,.28); background:linear-gradient(180deg, rgba(223,240,236,.55), rgba(255,255,255,.75)); cursor:pointer; }
+ .upload.dragover { transform:translateY(-1px); }
+ .file-list { display:grid; gap:10px; }
+ .file { display:grid; grid-template-columns:1fr auto auto; gap:10px; align-items:center; padding:12px 14px; border-radius:16px; background:#fff; border:1px solid var(--line); }
+ .remove { width:34px; height:34px; border-radius:999px; border:0; background:rgba(184,95,73,.1); color:var(--warn); cursor:pointer; }
+ .mode-grid { display:grid; grid-template-columns:repeat(2,1fr); gap:10px; }
+ .mode { border:1px solid var(--line); border-radius:20px; padding:16px; cursor:pointer; background:rgba(255,255,255,.7); }
+ .mode.selected { border-color:rgba(47,122,113,.35); background:linear-gradient(180deg, rgba(223,240,236,.8), rgba(255,255,255,.9)); }
+ .mode input { display:none; }
+ .mode strong { display:block; margin-bottom:6px; }
+ .row { display:grid; grid-template-columns:1fr 220px; gap:12px; align-items:end; }
+ .btn { border:0; border-radius:16px; padding:14px 16px; background:linear-gradient(135deg,var(--accent),#225b55); color:#fff; font-weight:700; cursor:pointer; }
+ .btn:disabled { opacity:.65; cursor:not-allowed; }
+ .section { margin-top:20px; }
+ .header { display:flex; justify-content:space-between; gap:14px; align-items:end; margin-bottom:14px; }
+ .list { display:grid; gap:12px; }
+ .job { border-radius:22px; padding:20px; }
+ .job-head { display:flex; justify-content:space-between; gap:12px; align-items:flex-start; }
+ .job-title { font-weight:700; line-height:1.45; }
+ .chips,.actions,.filters { display:flex; gap:8px; flex-wrap:wrap; }
+ .chip,.status { border-radius:999px; padding:8px 12px; font-size:.82rem; }
+ .chip { background:rgba(34,52,55,.06); color:var(--muted); }
+ .status.pending,.status.processing { background:rgba(198,125,69,.14); color:#8f5a2d; }
+ .status.completed { background:rgba(47,122,113,.14); color:var(--accent); }
+ .status.failed,.status.interrupted { background:rgba(184,95,73,.12); color:var(--warn); }
+ .bar { height:8px; background:rgba(34,52,55,.08); border-radius:999px; overflow:hidden; margin-top:14px; }
+ .bar span { display:block; height:100%; background:linear-gradient(90deg,var(--accent),#c67d45); }
+ .actions,.filters { margin-top:14px; }
+ .action,.filter { border:1px solid var(--line); background:rgba(255,255,255,.9); }
+ .empty { padding:28px; border:1px dashed var(--line); border-radius:22px; text-align:center; background:rgba(255,255,255,.45); }
+ .error { margin-top:12px; color:var(--warn); }
+ .footer { text-align:center; font-size:.84rem; padding:22px 0 10px; color:var(--muted); }
+ .modal { position:fixed; inset:0; background:rgba(26,34,37,.48); display:none; align-items:center; justify-content:center; padding:24px; }
+ .modal.open { display:flex; }
+ .modal-card { width:min(900px,100%); max-height:85vh; overflow:auto; border-radius:24px; padding:24px; }
+ .modal-top { display:flex; justify-content:space-between; gap:12px; margin-bottom:16px; }
+ .close { width:40px; height:40px; border-radius:999px; border:0; background:rgba(34,52,55,.08); cursor:pointer; }
+ .preview { white-space:pre-wrap; line-height:1.7; }
+ .preview h1,.preview h2,.preview h3 { font-family:'Fraunces',serif; margin:16px 0 8px; }
+ .preview hr { border:0; border-top:1px solid var(--line); margin:18px 0; }
+ @media (max-width:980px) { .hero,.row { grid-template-columns:1fr; } .notes { grid-template-columns:1fr; } }
+ @media (max-width:640px) { body { padding:14px; } .mode-grid { grid-template-columns:1fr; } .job-head,.header { flex-direction:column; align-items:flex-start; } }
</style>
</head>
<body>
- <!-- Menu de Navegação -->
- <nav class="navbar">
- <a href="#" class="navbar-brand" onclick="showPage('process'); return false;">🎧 Lazier</a>
- <button class="mobile-menu-toggle" onclick="toggleMobileMenu()" aria-label="Toggle menu">☰</button>
- <ul class="navbar-nav" id="navbarNav">
- <li><a href="#" class="active" onclick="showPage('process'); return false;">Processar</a></li>
- <li><a href="#" onclick="showPage('history'); return false;">Histórico</a></li>
- <li><a href="#" onclick="showPage('downloads'); return false;">Downloads</a></li>
- <li><a href="#" onclick="logout(); return false;" style="color: var(--color-secondary);">Sair</a></li>
- </ul>
- </nav>
-
- <div class="container">
- <!-- Página: Processar -->
- <div id="page-process" class="page active">
- <div class="page-content">
- <h1>🎧 Lazier</h1>
- <p class="subtitle">Transcrição e Sumarização de Áudios, Vídeos, Textos e PDFs usando OpenAI API</p>
-
- <div class="upload-area" id="uploadArea" style="flex-shrink: 0;">
- <div class="upload-icon">📁</div>
- <h3>Arraste arquivos aqui ou clique para selecionar</h3>
- <p>Suporta: Áudio (mp3, wav, m4a...), Vídeo (mp4, avi, mkv...), PDF, Texto (txt, md, html)</p>
- <input type="file" id="fileInput" class="file-input" multiple accept=".mp3,.wav,.m4a,.mp4,.avi,.mkv,.pdf,.txt,.md,.html">
+ <div class="shell">
+ <header class="topbar">
+ <div class="brand">
+ <div class="mark">🎧</div>
+ <div>
+ <h1>Lazier</h1>
+ <p>Transcrição ou sumário, sempre em português do Brasil.</p>
</div>
-
- <input type="text" id="urlInput" class="url-input" placeholder="Ou cole uma URL do YouTube ou página web aqui...">
-
- <!-- Opções de Processamento -->
- <div class="processing-options" style="flex-shrink: 0;">
- <h3>Modo de Processamento</h3>
- <div class="option-cards">
- <div class="option-card selected" onclick="selectProcessingMode('both', this)">
- <label>
- <input type="radio" name="processingMode" value="both" checked>
- Transcrever + Sumarizar
- </label>
- <div class="description">Transcreve e gera sumário completo</div>
- </div>
- <div class="option-card" onclick="selectProcessingMode('transcribe', this)">
- <label>
- <input type="radio" name="processingMode" value="transcribe">
- Apenas Transcrever
- </label>
- <div class="description">Apenas transcrição do conteúdo</div>
+ </div>
+ <nav class="nav">
+ <a href="#" class="active" onclick="showPage('process');return false;">Processar</a>
+ <a href="#" onclick="showPage('history');return false;">Histórico</a>
+ <a href="#" onclick="showPage('downloads');return false;">Downloads</a>
+ <button onclick="logout();return false;">Sair</button>
+ </nav>
+ </header>
+
+ <section id="page-process" class="page active">
+ <div class="hero">
+ <div class="card">
+ <div class="eyebrow">Fluxo simplificado</div>
+ <h2>Escolha um modo e receba o resultado final em português.</h2>
+ <p>Envie arquivos, vídeos, links, páginas, textos ou PDFs. O Lazier detecta o idioma, converte o conteúdo e salva o artefato em uma estrutura de saída mais organizada.</p>
+ <div class="notes">
+ <div class="note"><strong>Um objetivo por vez</strong><span class="meta">Agora o fluxo é transcrever ou resumir.</span></div>
+ <div class="note"><strong>Saídas mais limpas</strong><span class="meta">Pastas por data e job com nomes previsíveis.</span></div>
+ <div class="note"><strong>Histórico persistente</strong><span class="meta">Jobs continuam visíveis após reiniciar o servidor.</span></div>
+ </div>
+ </div>
+
+ <div class="card stack">
+ <div>
+ <div class="title">Novo processamento</div>
+ <p class="subtle">A saída final sempre será em português do Brasil.</p>
+ </div>
+ <div class="upload" id="uploadArea"><input type="file" id="fileInput" hidden multiple accept=".mp3,.wav,.m4a,.aac,.flac,.ogg,.opus,.wma,.3gp,.3g2,.amr,.au,.caf,.mka,.ra,.rm,.spx,.tta,.wv,.mp4,.avi,.mkv,.mov,.wmv,.flv,.webm,.m4v,.asf,.f4v,.m2v,.mts,.m2ts,.ogv,.rmvb,.ts,.vob,.pdf,.txt,.md,.html,.htm"></div>
+ <div class="field"><label for="urlInput">Ou cole uma URL</label><input type="text" id="urlInput" placeholder="YouTube, TED, Vimeo, página web, artigo, etc."></div>
+ <div class="field">
+ <label>Modo</label>
+ <div class="mode-grid">
+ <div class="mode selected" onclick="selectMode('transcribe',this)"><input type="radio" checked value="transcribe"><strong>Apenas transcrever</strong><span class="meta">Entrega o conteúdo completo em português.</span></div>
+ <div class="mode" onclick="selectMode('summarize',this)"><input type="radio" value="summarize"><strong>Apenas resumir</strong><span class="meta">Entrega um sumário em português.</span></div>
</div>
- <div class="option-card" onclick="selectProcessingMode('summarize', this)">
- <label>
- <input type="radio" name="processingMode" value="summarize">
- Apenas Sumarizar
- </label>
- <div class="description">Apenas sumário (textos/PDFs)</div>
+ </div>
+ <div class="row">
+ <div class="field">
+ <label for="formatSelect">Formato de saída</label>
+ <select id="formatSelect"><option value="docx">DOCX</option><option value="txt">TXT</option><option value="md">Markdown</option><option value="json">JSON</option><option value="pdf">PDF</option></select>
</div>
+ <button class="btn" id="processBtn" onclick="processFiles()">Processar</button>
</div>
</div>
-
- <div class="options" style="flex-shrink: 0;">
- <div class="option-group">
- <label>Formato de Saída</label>
- <select id="formatSelect">
- <option value="docx">DOCX</option>
- <option value="txt">TXT</option>
- <option value="md">Markdown</option>
- <option value="json">JSON</option>
- <option value="pdf">PDF</option>
- </select>
- </div>
- <div class="option-group">
- <label>Idioma</label>
- <select id="languageSelect">
- <option value="pt">Português (Brasil)</option>
- <option value="en">English</option>
- </select>
+ </div>
+
+ <div class="card section">
+ <div class="header">
+ <div>
+ <div class="title">Jobs em andamento</div>
+ <p class="subtle">Acompanhe progresso, visualize conteúdo e faça download dos artefatos.</p>
</div>
</div>
-
- <button class="btn" id="processBtn" onclick="processFiles()" style="flex-shrink: 0;">Processar</button>
-
- <div class="jobs-list" id="jobsList" style="flex: 1; overflow-y: auto; min-height: 0;"></div>
+ <div class="list" id="jobsList"></div>
</div>
- </div>
-
- <!-- Página: Histórico -->
- <div id="page-history" class="page">
- <div class="page-content">
- <h1>Histórico de Processamentos</h1>
- <p class="subtitle">Visualize todos os seus jobs processados</p>
-
- <div class="history-filters">
- <button class="filter-btn active" onclick="filterHistory('all')">Todos</button>
- <button class="filter-btn" onclick="filterHistory('completed')">Concluídos</button>
- <button class="filter-btn" onclick="filterHistory('processing')">Processando</button>
- <button class="filter-btn" onclick="filterHistory('failed')">Falhados</button>
+ </section>
+
+ <section id="page-history" class="page">
+ <div class="card">
+ <div class="header">
+ <div><div class="title">Histórico</div><p class="subtle">Jobs persistidos, inclusive após reinício do servidor.</p></div>
+ </div>
+ <div class="filters">
+ <button class="filter active" onclick="filterHistory('all',this)">Todos</button>
+ <button class="filter" onclick="filterHistory('completed',this)">Concluídos</button>
+ <button class="filter" onclick="filterHistory('processing',this)">Processando</button>
+ <button class="filter" onclick="filterHistory('failed',this)">Falhados</button>
+ <button class="filter" onclick="filterHistory('interrupted',this)">Interrompidos</button>
</div>
-
- <div class="jobs-list" id="historyList"></div>
+ <div class="list" id="historyList"></div>
</div>
- </div>
-
- <!-- Página: Downloads -->
- <div id="page-downloads" class="page">
- <div class="page-content">
- <h1>Downloads Disponíveis</h1>
- <p class="subtitle">Acesse rapidamente seus arquivos processados</p>
-
- <div class="jobs-list" id="downloadsList"></div>
+ </section>
+
+ <section id="page-downloads" class="page">
+ <div class="card">
+ <div class="header">
+ <div><div class="title">Downloads</div><p class="subtle">Acesso rápido aos arquivos prontos.</p></div>
+ </div>
+ <div class="list" id="downloadsList"></div>
</div>
- </div>
- </div>
+ </section>
- <!-- Footer Principal -->
- <footer class="main-footer">
+ <footer class="footer">Desenvolvido por Pablo Murad · <span id="currentYear"></span></footer>
+ </div>
-
- <div class="footer-bottom">
- <p>Desenvolvido com ❤️ por Pablo Murad - <span id="currentYear"></span></p>
+ <div class="modal" id="previewModal" onclick="closePreview(event)">
+ <div class="modal-card" onclick="event.stopPropagation()">
+ <div class="modal-top">
+ <div><div class="title" id="previewTitle">Pré-visualização</div><p class="subtle">Conteúdo persistido do job selecionado.</p></div>
+ <button class="close" onclick="closePreview()">✕</button>
+ </div>
+ <div class="preview" id="previewContent"></div>
</div>
- </footer>
-
+ </div>
+
<script>
- // Estado da aplicação
- let currentPage = 'process';
let selectedFiles = [];
- let processingMode = 'both';
+ let processingMode = 'transcribe';
let allJobs = [];
let currentFilter = 'all';
-
- // Inicialização
+
document.addEventListener('DOMContentLoaded', () => {
document.getElementById('currentYear').textContent = new Date().getFullYear();
+ renderUpload();
+ bindUpload();
loadHistory();
-
- // Animações de entrada
- const elements = document.querySelectorAll('.page-content > *');
- elements.forEach((el, index) => {
- el.style.opacity = '0';
- el.style.transform = 'translateY(20px)';
- setTimeout(() => {
- el.style.transition = 'all 0.5s ease';
- el.style.opacity = '1';
- el.style.transform = 'translateY(0)';
- }, index * 100);
- });
});
-
- // Toggle mobile menu
- function toggleMobileMenu() {
- const nav = document.getElementById('navbarNav');
- nav.classList.toggle('active');
- }
-
- // Logout
- async function logout() {
- try {
- const response = await fetch('/logout', {
- method: 'POST',
- credentials: 'include'
- });
- if (response.ok || response.redirected) {
- window.location.href = '/login';
- }
- } catch (error) {
- console.error('Erro ao fazer logout:', error);
- // Mesmo com erro, redireciona para login
- window.location.href = '/login';
+
+ function renderUpload() {
+ const area = document.getElementById('uploadArea');
+ if (!selectedFiles.length) {
+ area.innerHTML = '<strong>Arraste arquivos aqui ou clique para selecionar</strong><p class="subtle" style="margin-top:10px;">Suporta áudio, vídeo, PDF, texto e HTML. O resultado final será salvo em português do Brasil.</p>';
+ return;
}
+ area.innerHTML = `<div class="file-list">${selectedFiles.map((file, index) => `<div class="file"><div><strong>${escapeHtml(file.name)}</strong><div class="meta">${(file.size / 1024 / 1024).toFixed(2)} MB</div></div><div class="chip">${file.type || 'arquivo'}</div><button class="remove" onclick="removeFile(${index})">✕</button></div>`).join('')}</div>`;
+ }
+
+ function bindUpload() {
+ const area = document.getElementById('uploadArea');
+ const input = document.getElementById('fileInput');
+ area.addEventListener('click', (event) => { if (!event.target.classList.contains('remove')) input.click(); });
+ area.addEventListener('dragover', (event) => { event.preventDefault(); area.classList.add('dragover'); });
+ area.addEventListener('dragleave', () => area.classList.remove('dragover'));
+ area.addEventListener('drop', (event) => { event.preventDefault(); area.classList.remove('dragover'); selectedFiles = Array.from(event.dataTransfer.files); renderUpload(); });
+ input.addEventListener('change', (event) => { selectedFiles = Array.from(event.target.files); renderUpload(); });
}
-
- // Navegação
+
+ function removeFile(index) { selectedFiles.splice(index, 1); document.getElementById('fileInput').value = ''; renderUpload(); }
+ function selectMode(mode, element) { processingMode = mode; document.querySelectorAll('.mode').forEach((node) => node.classList.remove('selected')); element.classList.add('selected'); }
+ async function logout() { try { await fetch('/logout', { method:'POST', credentials:'include' }); } finally { window.location.href = '/login'; } }
+
function showPage(page) {
- document.querySelectorAll('.page').forEach(p => p.classList.remove('active'));
- document.querySelectorAll('.navbar-nav a').forEach(a => a.classList.remove('active'));
-
+ document.querySelectorAll('.page').forEach((node) => node.classList.remove('active'));
+ document.querySelectorAll('.nav a').forEach((node) => node.classList.remove('active'));
document.getElementById(`page-${page}`).classList.add('active');
- const navLinks = document.querySelectorAll('.navbar-nav a');
- const pageIndex = ['process', 'history', 'downloads'].indexOf(page);
- if (pageIndex >= 0 && navLinks[pageIndex]) {
- navLinks[pageIndex].classList.add('active');
- }
-
- // Fecha menu mobile se estiver aberto
- document.getElementById('navbarNav').classList.remove('active');
-
- currentPage = page;
-
- if (page === 'history') {
- loadHistory();
- } else if (page === 'downloads') {
- loadDownloads();
- }
- }
-
- // Seleção de modo de processamento
- function selectProcessingMode(mode, element) {
- processingMode = mode;
- document.querySelectorAll('.option-card').forEach(card => card.classList.remove('selected'));
- if (element) {
- element.classList.add('selected');
- } else {
- event.currentTarget.classList.add('selected');
- }
- document.querySelector(`input[value="${mode}"]`).checked = true;
- }
-
- // Upload e Drag & Drop
- const uploadArea = document.getElementById('uploadArea');
- const fileInput = document.getElementById('fileInput');
-
- uploadArea.addEventListener('click', () => fileInput.click());
- uploadArea.addEventListener('dragover', (e) => {
- e.preventDefault();
- uploadArea.classList.add('dragover');
- });
- uploadArea.addEventListener('dragleave', () => {
- uploadArea.classList.remove('dragover');
- });
- uploadArea.addEventListener('drop', (e) => {
- e.preventDefault();
- uploadArea.classList.remove('dragover');
- selectedFiles = Array.from(e.dataTransfer.files);
- updateFileList();
- });
-
- fileInput.addEventListener('change', (e) => {
- selectedFiles = Array.from(e.target.files);
- updateFileList();
- });
-
- function updateFileList() {
- if (selectedFiles.length > 0) {
- const fileListHTML = `
- <div class="file-list">
- ${selectedFiles.map((f, index) => `
- <div class="file-item">
- <span>${f.name}</span>
- <span>${(f.size / 1024 / 1024).toFixed(2)} MB</span>
- <button onclick="removeFile(${index})" class="btn-remove" title="Remover arquivo">✕</button>
- </div>
- `).join('')}
- </div>
- <p style="margin-top: var(--spacing-md); color: var(--color-text-light);">Clique novamente para selecionar outros arquivos</p>
- `;
- uploadArea.innerHTML = fileListHTML;
- // Re-adiciona event listener após atualizar HTML
- uploadArea.addEventListener('click', (e) => {
- if (!e.target.classList.contains('btn-remove')) {
- fileInput.click();
- }
- });
- } else {
- uploadArea.innerHTML = `
- <div class="upload-icon">📁</div>
- <h3>Arraste arquivos aqui ou clique para selecionar</h3>
- <p>Suporta: Áudio (mp3, wav, m4a...), Vídeo (mp4, avi, mkv...), PDF, Texto (txt, md, html)</p>
- `;
- uploadArea.addEventListener('click', () => fileInput.click());
- }
- }
-
- function removeFile(index) {
- selectedFiles.splice(index, 1);
- updateFileList();
- fileInput.value = '';
+ const index = ['process','history','downloads'].indexOf(page);
+ const links = Array.from(document.querySelectorAll('.nav a'));
+ if (links[index]) links[index].classList.add('active');
+ if (page === 'history') loadHistory();
+ if (page === 'downloads') loadDownloads();
}
-
- // Processamento
+
async function processFiles() {
+ const button = document.getElementById('processBtn');
const format = document.getElementById('formatSelect').value;
const url = document.getElementById('urlInput').value.trim();
-
- const transcribe = processingMode === 'both' || processingMode === 'transcribe';
- const summarize = processingMode === 'both' || processingMode === 'summarize';
-
- const processBtn = document.getElementById('processBtn');
- processBtn.disabled = true;
- processBtn.textContent = 'Processando...';
-
+ button.disabled = true;
+ button.textContent = 'Processando...';
try {
if (url) {
- const response = await fetch('/api/process', {
- method: 'POST',
- headers: {'Content-Type': 'application/json'},
- body: JSON.stringify({url, format, transcribe, summarize}),
- credentials: 'include'
- });
+ const response = await fetch('/api/process', { method:'POST', headers:{'Content-Type':'application/json'}, body:JSON.stringify({ url, format, mode: processingMode }), credentials:'include' });
const data = await response.json();
- addJob(data.job_id, url, 'URL');
+ if (!response.ok) throw new Error(data.detail || 'Falha ao processar URL');
+ addJob(data.job_id, url, processingMode);
startPolling(data.job_id);
- } else if (selectedFiles.length > 0) {
+ } else if (selectedFiles.length) {
const formData = new FormData();
- selectedFiles.forEach(f => formData.append('files', f));
+ selectedFiles.forEach((file) => formData.append('files', file));
formData.append('format', format);
- formData.append('transcribe', transcribe);
- formData.append('summarize', summarize);
-
- const response = await fetch('/api/upload', {
- method: 'POST',
- body: formData,
- credentials: 'include'
- });
+ formData.append('mode', processingMode);
+ const response = await fetch('/api/upload', { method:'POST', body:formData, credentials:'include' });
const data = await response.json();
-
- data.job_ids.forEach(jobId => {
- addJob(jobId, selectedFiles[0].name, 'Arquivo');
- startPolling(jobId);
- });
+ if (!response.ok) throw new Error(data.detail || 'Falha ao enviar arquivos');
+ data.job_ids.forEach((jobId, index) => { addJob(jobId, selectedFiles[index] ? selectedFiles[index].name : 'Arquivo', processingMode); startPolling(jobId); });
} else {
- alert('Selecione arquivos ou informe uma URL');
+ throw new Error('Selecione arquivos ou informe uma URL.');
}
} catch (error) {
- console.error('Erro:', error);
- alert('Erro ao processar: ' + error.message);
+ alert(error.message);
} finally {
- processBtn.disabled = false;
- processBtn.textContent = 'Processar';
+ button.disabled = false;
+ button.textContent = 'Processar';
+ loadHistory();
}
}
-
- // Gerenciamento de Jobs
- function addJob(jobId, name, type) {
- const jobCard = document.createElement('div');
- jobCard.className = 'job-card';
- jobCard.id = `job-${jobId}`;
- jobCard.innerHTML = `
- <div class="job-header">
- <div class="job-title">
- <strong>${type}:</strong> ${name}
- </div>
- <span class="job-status status-pending" id="status-${jobId}">Pendente</span>
- </div>
- <div class="progress-bar">
- <div class="progress-fill" id="progress-${jobId}" style="width: 0%"></div>
- </div>
- <div id="result-${jobId}"></div>
- `;
- document.getElementById('jobsList').appendChild(jobCard);
+
+ function renderJob(job) {
+ const title = job.title || job.url || job.file_path || `Job ${job.id}`;
+ const statusLabel = { pending:'Pendente', processing:'Processando', completed:'Concluído', failed:'Falhou', interrupted:'Interrompido' }[job.status] || job.status;
+ return `<div class="job-head"><div><div class="job-title">${escapeHtml(title)}</div><div class="chips" style="margin-top:10px;">${job.mode ? `<span class="chip">${job.mode === 'transcribe' ? 'Transcrição' : 'Sumário'}</span>` : ''}${job.format ? `<span class="chip">${job.format.toUpperCase()}</span>` : ''}${job.created_at ? `<span class="chip">${new Date(job.created_at).toLocaleString('pt-BR')}</span>` : ''}</div></div><span class="status ${job.status}">${statusLabel}</span></div><div class="bar"><span style="width:${job.progress || 0}%"></span></div>${job.error ? `<div class="error">${escapeHtml(job.error)}</div>` : ''}${renderActions(job)}`;
+ }
+
+ function renderActions(job) {
+ if (job.status !== 'completed' && job.status !== 'interrupted') return '';
+ let html = '<div class="actions">';
+ if (job.result_path) html += `<a class="action" href="/api/jobs/${job.id}/download">Baixar principal</a>`;
+ if (job.has_transcription) html += `<a class="action" href="/api/jobs/${job.id}/transcription">Baixar transcrição</a>`;
+ if (job.has_summary) html += `<a class="action" href="/api/jobs/${job.id}/summary">Baixar sumário</a>`;
+ html += `<button class="action" onclick="viewJobDetails('${job.id}')">Visualizar</button></div>`;
+ return html;
+ }
+
+ function addJob(jobId, title, mode) {
+ const node = document.createElement('div');
+ node.className = 'job';
+ node.id = `job-${jobId}`;
+ node.dataset.title = title;
+ node.innerHTML = renderJob({ id: jobId, title, mode, status: 'pending', progress: 0, has_transcription: false, has_summary: false });
+ document.getElementById('jobsList').prepend(node);
}
-
+
+ function updateJob(jobId, data) {
+ const node = document.getElementById(`job-${jobId}`);
+ if (!node) return;
+ node.innerHTML = renderJob({ id: jobId, title: node.dataset.title || undefined, ...data });
+ }
+
async function startPolling(jobId) {
- const ws = new WebSocket(`ws://${window.location.host}/ws/progress/${jobId}`);
-
- ws.onmessage = (event) => {
- const data = JSON.parse(event.data);
- updateJob(jobId, data);
- };
-
+ const protocol = window.location.protocol === 'https:' ? 'wss' : 'ws';
+ let socket = null;
+ try {
+ socket = new WebSocket(`${protocol}://${window.location.host}/ws/progress/${jobId}`);
+ socket.onmessage = (event) => { const data = JSON.parse(event.data); if (data.type !== 'ping') updateJob(jobId, data); };
+ } catch (_error) {}
const interval = setInterval(async () => {
try {
- const response = await fetch(`/api/jobs/${jobId}`, {
- credentials: 'include'
- });
+ const response = await fetch(`/api/jobs/${jobId}`, { credentials:'include' });
const data = await response.json();
+ if (!response.ok) throw new Error();
updateJob(jobId, data);
-
- if (data.status === 'completed' || data.status === 'failed') {
+ if (['completed','failed','interrupted'].includes(data.status)) {
clearInterval(interval);
- ws.close();
+ if (socket) socket.close();
loadHistory();
+ loadDownloads();
}
- } catch (error) {
- console.error('Erro ao verificar status:', error);
+ } catch (_error) {
+ clearInterval(interval);
+ if (socket) socket.close();
}
}, 2000);
}
-
- function updateJob(jobId, data) {
- const statusEl = document.getElementById(`status-${jobId}`);
- const progressEl = document.getElementById(`progress-${jobId}`);
- const resultEl = document.getElementById(`result-${jobId}`);
-
- if (!statusEl) return;
-
- statusEl.textContent = data.status === 'completed' ? 'Concluído' :
- data.status === 'failed' ? 'Falhou' :
- data.status === 'processing' ? 'Processando' : 'Pendente';
- statusEl.className = `job-status status-${data.status}`;
- progressEl.style.width = `${data.progress || 0}%`;
-
- if (data.status === 'completed') {
- let actionsHTML = '<div class="job-actions">';
-
- if (data.result_path) {
- actionsHTML += `<a href="/api/jobs/${jobId}/download" class="btn btn-small">📥 Download Completo</a>`;
- }
-
- if (data.has_transcription) {
- actionsHTML += `<a href="/api/jobs/${jobId}/transcription" class="btn btn-small btn-secondary">📄 Download Transcrição</a>`;
- }
-
- if (data.has_summary) {
- actionsHTML += `<a href="/api/jobs/${jobId}/summary" class="btn btn-small btn-secondary">📝 Download Sumário</a>`;
- }
-
- actionsHTML += `<button class="btn btn-small btn-secondary" onclick="viewJobDetails('${jobId}')">👁️ Visualizar</button>`;
- actionsHTML += '</div>';
-
- resultEl.innerHTML = actionsHTML;
- } else if (data.status === 'failed') {
- resultEl.innerHTML = `<p style="color:#d32f2f; margin-top:var(--spacing-md);">Erro: ${data.error || 'Erro desconhecido'}</p>`;
- }
- }
-
- async function viewJobDetails(jobId) {
- try {
- const response = await fetch(`/api/jobs/${jobId}/details`, {
- credentials: 'include'
- });
- const data = await response.json();
-
- const format = data.format || 'docx';
-
- let content = '<div class="preview-content">';
-
- if (data.summary) {
- content += '<h3>Sumário</h3>';
- const shouldRenderMarkdown = (format === 'md' || format === 'markdown') || isMarkdownContent(data.summary);
-
- if (shouldRenderMarkdown && typeof marked !== 'undefined') {
- content += marked.parse(data.summary);
- } else {
- content += '<div class="preview-text-plain">' + escapeHtml(data.summary) + '</div>';
- }
- content += '<hr>';
- }
-
- if (data.transcription) {
- content += '<h3>Transcrição</h3>';
- const shouldRenderMarkdown = (format === 'md' || format === 'markdown') || isMarkdownContent(data.transcription);
-
- if (shouldRenderMarkdown && typeof marked !== 'undefined') {
- content += marked.parse(data.transcription);
- } else {
- content += '<div class="preview-text-plain">' + escapeHtml(data.transcription) + '</div>';
- }
- }
-
- content += '</div>';
-
- document.getElementById(`result-${jobId}`).innerHTML += content;
- } catch (error) {
- alert('Erro ao carregar detalhes: ' + error.message);
- }
- }
-
- function escapeHtml(text) {
- if (!text) return '';
- const div = document.createElement('div');
- div.textContent = text;
- return div.innerHTML;
- }
-
- function isMarkdownContent(text) {
- if (!text) return false;
- const markdownPatterns = [
- /^\s*#{1,6}\s+.+$/m,
- /\*\*[^*]+\*\*/,
- /\*[^*]+\*/,
- /^\s*[-*+]\s+.+$/m,
- /^\s*\d+\.\s+.+$/m,
- /\[.+\]\(.+\)/,
- /```[\s\S]*?```/,
- /`[^`]+`/,
- /^>\s+.+$/m,
- /^\|.+\|$/m,
- ];
-
- return markdownPatterns.some(pattern => pattern.test(text));
- }
-
- // Histórico
+
async function loadHistory() {
try {
- const response = await fetch('/api/history', {
- credentials: 'include'
- });
+ const response = await fetch('/api/history', { credentials:'include' });
const data = await response.json();
allJobs = data.jobs || [];
renderHistory();
- } catch (error) {
- console.error('Erro ao carregar histórico:', error);
+ loadDownloads();
+ } catch (_error) {
+ document.getElementById('historyList').innerHTML = '<div class="empty">Não foi possível carregar o histórico.</div>';
}
}
-
- function filterHistory(filter) {
+
+ function filterHistory(filter, button) {
currentFilter = filter;
- document.querySelectorAll('.filter-btn').forEach(btn => btn.classList.remove('active'));
- event.target.classList.add('active');
+ document.querySelectorAll('.filter').forEach((node) => node.classList.remove('active'));
+ button.classList.add('active');
renderHistory();
}
-
+
function renderHistory() {
- const historyList = document.getElementById('historyList');
- historyList.innerHTML = '';
-
- let filteredJobs = allJobs;
- if (currentFilter !== 'all') {
- filteredJobs = allJobs.filter(job => job.status === currentFilter);
- }
-
- if (filteredJobs.length === 0) {
- historyList.innerHTML = '<p style="text-align:center; color:var(--color-text-light); padding:var(--spacing-3xl);">Nenhum job encontrado</p>';
- return;
- }
-
- filteredJobs.reverse().forEach(job => {
- const jobCard = document.createElement('div');
- jobCard.className = 'job-card';
- jobCard.innerHTML = `
- <div class="job-header">
- <div class="job-title">
- ${job.title || job.url || job.file_path || 'Job ' + job.id}
- </div>
- <span class="job-status status-${job.status}">${job.status === 'completed' ? 'Concluído' : job.status === 'failed' ? 'Falhado' : job.status === 'processing' ? 'Processando' : job.status}</span>
- </div>
- ${job.status === 'completed' ? `
- <div class="job-actions">
- ${job.result_path ? `<a href="/api/jobs/${job.id}/download" class="btn btn-small">📥 Download Completo</a>` : ''}
- ${job.has_transcription ? `<a href="/api/jobs/${job.id}/transcription" class="btn btn-small btn-secondary">📄 Transcrição</a>` : ''}
- ${job.has_summary ? `<a href="/api/jobs/${job.id}/summary" class="btn btn-small btn-secondary">📝 Sumário</a>` : ''}
- </div>
- ` : ''}
- `;
- historyList.appendChild(jobCard);
- });
+ const container = document.getElementById('historyList');
+ const jobs = currentFilter === 'all' ? allJobs : allJobs.filter((job) => job.status === currentFilter);
+ container.innerHTML = jobs.length ? jobs.map((job) => `<div class="job">${renderJob(job)}</div>`).join('') : '<div class="empty">Nenhum job encontrado para este filtro.</div>';
}
-
- // Downloads
+
function loadDownloads() {
- const downloadsList = document.getElementById('downloadsList');
- downloadsList.innerHTML = '';
-
- const completedJobs = allJobs.filter(job => job.status === 'completed');
-
- if (completedJobs.length === 0) {
- downloadsList.innerHTML = '<p style="text-align:center; color:var(--color-text-light); padding:var(--spacing-3xl);">Nenhum arquivo disponível para download</p>';
- return;
+ const container = document.getElementById('downloadsList');
+ const jobs = allJobs.filter((job) => job.status === 'completed' && job.result_path);
+ container.innerHTML = jobs.length ? jobs.map((job) => `<div class="job">${renderJob(job)}</div>`).join('') : '<div class="empty">Ainda não há arquivos prontos para download.</div>';
+ }
+
+ async function viewJobDetails(jobId) {
+ try {
+ const response = await fetch(`/api/jobs/${jobId}/details`, { credentials:'include' });
+ const data = await response.json();
+ if (!response.ok) throw new Error(data.detail || 'Erro ao carregar detalhes');
+ document.getElementById('previewTitle').textContent = data.metadata?.title || `Job ${jobId}`;
+ let content = '';
+ if (data.summary) { content += '<h3>Sumário</h3>' + renderPreview(data.summary, data.format) + '<hr>'; }
+ if (data.transcription) { content += '<h3>Transcrição</h3>' + renderPreview(data.transcription, data.format); }
+ document.getElementById('previewContent').innerHTML = content || '<div class="empty">Nenhum conteúdo persistido para este job.</div>';
+ document.getElementById('previewModal').classList.add('open');
+ } catch (error) {
+ alert(error.message);
}
-
- completedJobs.reverse().forEach(job => {
- const jobCard = document.createElement('div');
- jobCard.className = 'job-card';
- jobCard.innerHTML = `
- <div class="job-header">
- <div class="job-title">${job.url || job.file_path || 'Job ' + job.id}</div>
- </div>
- <div class="job-actions">
- ${job.result_path ? `<a href="/api/jobs/${job.id}/download" class="btn btn-small">📥 Download Completo</a>` : ''}
- ${job.has_transcription ? `<a href="/api/jobs/${job.id}/transcription" class="btn btn-small btn-secondary">📄 Download Transcrição</a>` : ''}
- ${job.has_summary ? `<a href="/api/jobs/${job.id}/summary" class="btn btn-small btn-secondary">📝 Download Sumário</a>` : ''}
- </div>
- `;
- downloadsList.appendChild(jobCard);
- });
}
+
+ function renderPreview(text, format) {
+ const maybeMarkdown = (format === 'md' || format === 'markdown') || [/^\s*#{1,6}\s+.+$/m, /\*\*[^*]+\*\*/, /^\s*[-*+]\s+.+$/m, /^\s*\d+\.\s+.+$/m, /\[.+\]\(.+\)/, /```[\s\S]*?```/, /`[^`]+`/].some((pattern) => pattern.test(text || ''));
+ return maybeMarkdown && typeof marked !== 'undefined' ? marked.parse(text) : `<div>${escapeHtml(text)}</div>`;
+ }
+
+ function closePreview(event) {
+ if (event && event.target && event.target.id !== 'previewModal') return;
+ document.getElementById('previewModal').classList.remove('open');
+ }
+
+ function escapeHtml(text) { const div = document.createElement('div'); div.textContent = text || ''; return div.innerHTML; }
</script>
</body>
</html>
diff --git a/lazier/web/templates/login.html b/lazier/web/templates/login.html
@@ -3,260 +3,68 @@
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
- <title>Login - Lazier</title>
- <link rel="icon" href="data:image/svg+xml,<svg xmlns=%22http://www.w3.org/2000/svg%22 viewBox=%220 0 100 100%22><text y=%22.9em%22 font-size=%2290%22>📢</text></svg>">
+ <title>Login · Lazier</title>
<link rel="preconnect" href="https://fonts.googleapis.com">
<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
- <link href="https://fonts.googleapis.com/css2?family=Playfair+Display:wght@400;600;700&family=Work+Sans:wght@300;400;500;600;700&display=swap" rel="stylesheet">
+ <link href="https://fonts.googleapis.com/css2?family=Fraunces:wght@500;700&family=Manrope:wght@400;500;600;700&display=swap" rel="stylesheet">
<style>
- * {
- margin: 0;
- padding: 0;
- box-sizing: border-box;
- }
-
- body {
- font-family: 'Work Sans', sans-serif;
- background: linear-gradient(135deg, #1a237e 0%, #283593 50%, #3949ab 100%);
- background-attachment: fixed;
- display: flex;
- flex-direction: column;
- min-height: 100vh;
- padding: 0;
- position: relative;
- overflow-x: hidden;
- }
-
- .login-wrapper {
- flex: 1;
- display: flex;
- justify-content: center;
- align-items: center;
- padding: 20px;
- position: relative;
- z-index: 1;
- }
-
- body::before {
- content: '';
- position: fixed;
- top: 0;
- left: 0;
- width: 100%;
- height: 100%;
- background-image:
- radial-gradient(circle at 20% 50%, rgba(255, 179, 0, 0.1) 0%, transparent 50%),
- radial-gradient(circle at 80% 80%, rgba(255, 160, 0, 0.1) 0%, transparent 50%);
- pointer-events: none;
- z-index: 0;
- }
-
- .login-container {
- background: white;
- padding: 48px;
- border-radius: 24px;
- box-shadow: 0 12px 40px rgba(0,0,0,0.3);
- width: 100%;
- max-width: 420px;
- position: relative;
- z-index: 1;
- animation: fadeIn 0.5s ease;
- }
-
- @keyframes fadeIn {
- from {
- opacity: 0;
- transform: translateY(20px);
- }
- to {
- opacity: 1;
- transform: translateY(0);
- }
- }
-
- .login-container::before {
- content: '';
- position: absolute;
- top: 0;
- left: 0;
- right: 0;
- height: 4px;
- background: linear-gradient(90deg, #283593, #ffb300, #ffa000);
- border-radius: 24px 24px 0 0;
- }
-
- h1 {
- font-family: 'Playfair Display', serif;
- color: #283593;
- margin-bottom: 8px;
- text-align: center;
- font-size: 2.5rem;
- font-weight: 700;
- letter-spacing: -1px;
- }
-
- .subtitle {
- text-align: center;
- color: #546e7a;
- margin-bottom: 32px;
- font-size: 0.95rem;
- }
-
- .form-group {
- margin-bottom: 24px;
- }
-
- label {
- display: block;
- margin-bottom: 8px;
- color: #263238;
- font-weight: 600;
- font-size: 0.95rem;
- }
-
- input {
- width: 100%;
- padding: 14px 16px;
- border: 2px solid #e0e0e0;
- border-radius: 12px;
- font-size: 1rem;
- font-family: 'Work Sans', sans-serif;
- box-sizing: border-box;
- transition: all 0.3s ease;
- }
-
- input:focus {
- outline: none;
- border-color: #ffb300;
- box-shadow: 0 0 0 3px rgba(255, 179, 0, 0.1);
- }
-
- button {
- width: 100%;
- padding: 14px;
- background: linear-gradient(135deg, #283593 0%, #3949ab 100%);
- color: white;
- border: none;
- border-radius: 12px;
- font-size: 1.125rem;
- font-weight: 600;
- font-family: 'Work Sans', sans-serif;
- cursor: pointer;
- margin-top: 8px;
- transition: all 0.3s ease;
- box-shadow: 0 4px 12px rgba(40, 53, 147, 0.2);
- }
-
- button:hover {
- transform: translateY(-2px);
- box-shadow: 0 6px 20px rgba(40, 53, 147, 0.3);
- }
-
- button:active {
- transform: translateY(0);
- }
-
- .error {
- color: #d32f2f;
- margin-top: 16px;
- text-align: center;
- font-size: 0.9rem;
- padding: 12px;
- background: rgba(211, 47, 47, 0.1);
- border-radius: 8px;
- border: 1px solid rgba(211, 47, 47, 0.2);
- }
-
- /* ===== FOOTER ===== */
- .main-footer {
- background: linear-gradient(135deg, #1a237e 0%, #283593 100%);
- color: rgba(255, 255, 255, 0.9);
- padding: 16px 24px;
- margin-top: auto;
- width: 100%;
- position: relative;
- overflow: hidden;
- }
-
- .main-footer::before {
- content: '';
- position: absolute;
- top: 0;
- left: 0;
- right: 0;
- height: 1px;
- background: linear-gradient(90deg, transparent, rgba(255, 179, 0, 0.5), transparent);
- }
-
- .footer-bottom {
- max-width: 1200px;
- margin: 0 auto;
- padding-top: 8px;
- border-top: 1px solid rgba(255, 255, 255, 0.1);
- text-align: center;
- color: rgba(255, 255, 255, 0.7);
- font-size: 0.75rem;
- }
-
- @media (max-width: 480px) {
- .login-container {
- padding: 32px 24px;
- }
-
- h1 {
- font-size: 2rem;
- }
-
- .main-footer {
- padding: 12px 16px;
- }
- }
+ :root { --bg:#f5f1ea; --card:rgba(255,255,255,.9); --ink:#223437; --muted:#66787b; --line:rgba(34,52,55,.12); --accent:#2f7a71; --warn:#b85f49; }
+ * { box-sizing:border-box; margin:0; padding:0; }
+ body { min-height:100vh; display:grid; place-items:center; padding:20px; font-family:'Manrope',sans-serif; color:var(--ink); background:radial-gradient(circle at top left, rgba(47,122,113,.08), transparent 26%), linear-gradient(180deg,#faf7f2 0%,#f2ede6 100%); }
+ .shell { width:min(980px,100%); display:grid; grid-template-columns:1.05fr .95fr; gap:18px; }
+ .hero,.card { background:var(--card); border:1px solid rgba(255,255,255,.9); box-shadow:0 18px 42px rgba(40,56,60,.08); backdrop-filter:blur(14px); border-radius:28px; }
+ .hero,.card { padding:28px; }
+ .hero { display:flex; flex-direction:column; justify-content:space-between; min-height:430px; }
+ .eyebrow { display:inline-block; background:rgba(47,122,113,.1); color:var(--accent); padding:8px 12px; border-radius:999px; font-size:.85rem; font-weight:700; }
+ h1,h2 { font-family:'Fraunces',serif; letter-spacing:-.04em; line-height:1.05; }
+ h1 { font-size:clamp(2.2rem,4vw,3.4rem); margin:18px 0; }
+ p,.point span,label,.footer { color:var(--muted); }
+ .point { border:1px solid var(--line); border-radius:18px; padding:14px; background:rgba(255,255,255,.6); margin-top:10px; }
+ .point strong { display:block; margin-bottom:6px; color:var(--ink); }
+ .card h2 { font-size:2rem; margin-bottom:8px; }
+ .card p { margin-bottom:22px; }
+ .field { display:flex; flex-direction:column; gap:8px; margin-bottom:16px; }
+ label { font-size:.88rem; font-weight:700; }
+ input { width:100%; padding:14px 16px; border-radius:16px; border:1px solid var(--line); background:rgba(255,255,255,.95); color:var(--ink); }
+ button { width:100%; border:0; border-radius:16px; padding:14px 16px; background:linear-gradient(135deg,var(--accent),#225b55); color:#fff; font-weight:700; cursor:pointer; margin-top:6px; }
+ .error { margin-top:14px; color:var(--warn); background:rgba(184,95,73,.1); border:1px solid rgba(184,95,73,.16); border-radius:14px; padding:12px 14px; }
+ .footer { margin-top:16px; text-align:center; font-size:.84rem; }
+ @media (max-width:880px) { .shell { grid-template-columns:1fr; } .hero,.card { min-height:auto; padding:22px; } }
</style>
</head>
<body>
- <div class="login-wrapper">
- <div class="login-container">
- <h1>🎧 Lazier</h1>
- <form method="POST" action="/login">
- <div class="form-group">
- <label for="username">Usuário</label>
- <input
- type="text"
- id="username"
- name="username"
- required
- autofocus
- autocomplete="username"
- placeholder="Digite seu usuário"
- >
- </div>
- <div class="form-group">
- <label for="password">Senha</label>
- <input
- type="password"
- id="password"
- name="password"
- required
- autocomplete="current-password"
- placeholder="Digite sua senha"
- >
- </div>
- <button type="submit">Entrar</button>
- </form>
+ <div>
+ <div class="shell">
+ <section class="hero">
+ <div>
+ <div class="eyebrow">Lazier</div>
+ <h1>Entrada simples, saída organizada e sempre em português.</h1>
+ <p>O Lazier recebe mídias, páginas, textos e PDFs, persiste o histórico e mantém o fluxo mais claro: transcrever ou resumir.</p>
+ </div>
+ <div>
+ <div class="point"><strong>Transcrição ou sumário</strong><span>Um objetivo por vez, com resultado mais consistente.</span></div>
+ <div class="point"><strong>Histórico persistente</strong><span>Seus jobs continuam visíveis após reiniciar o servidor.</span></div>
+ <div class="point"><strong>Arquivos mais fáceis de achar</strong><span>As saídas ficam agrupadas por data e job.</span></div>
+ </div>
+ </section>
+ <section class="card">
+ <h2>Entrar</h2>
+ <p>Use sua conta para acessar a WebGUI.</p>
+ <form method="POST" action="/login">
+ <div class="field">
+ <label for="username">Usuário</label>
+ <input type="text" id="username" name="username" required autofocus autocomplete="username" placeholder="Digite seu usuário">
+ </div>
+ <div class="field">
+ <label for="password">Senha</label>
+ <input type="password" id="password" name="password" required autocomplete="current-password" placeholder="Digite sua senha">
+ </div>
+ <button type="submit">Entrar</button>
+ </form>
+ </section>
</div>
+ <div class="footer">Desenvolvido por Pablo Murad · <span id="currentYear"></span></div>
</div>
-
- <!-- Footer Principal -->
- <footer class="main-footer">
- <div class="footer-bottom">
- <p>Desenvolvido com ❤️ por Pablo Murad - <span id="currentYear"></span></p>
- </div>
- </footer>
-
- <script>
- document.addEventListener('DOMContentLoaded', () => {
- document.getElementById('currentYear').textContent = new Date().getFullYear();
- });
- </script>
+ <script>document.getElementById('currentYear').textContent = new Date().getFullYear();</script>
</body>
</html>
diff --git a/tests/__init__.py b/tests/__init__.py
@@ -1,2 +1 @@
-# Diretório de testes
-# Testes serão implementados em versões futuras
+"""Testes do Lazier."""
diff --git a/tests/test_api.py b/tests/test_api.py
@@ -0,0 +1,120 @@
+import importlib
+import os
+import shutil
+import unittest
+import uuid
+from pathlib import Path
+from unittest.mock import patch
+
+try:
+ from fastapi.testclient import TestClient
+except ImportError: # pragma: no cover - ambiente sem stack web
+ TestClient = None
+
+
+@unittest.skipIf(TestClient is None, "fastapi/testclient não está instalado neste ambiente")
+class ApiTests(unittest.TestCase):
+ def setUp(self):
+ self.temp_dir = Path(os.getcwd()) / ".tmp-tests" / f"api-{uuid.uuid4().hex[:8]}"
+ self.temp_dir.mkdir(parents=True, exist_ok=True)
+ os.environ["OPENAI_API_KEY"] = "test-key"
+ os.environ["SESSION_SECRET_KEY"] = "test-session-secret"
+ os.environ["ADMIN_USER"] = "admin"
+ os.environ["ADMIN_PASSWORD"] = "secret"
+ os.environ["LAZIER_DATA_DIR"] = str(self.temp_dir / "data")
+ os.environ["LAZIER_UPLOAD_DIR"] = str(self.temp_dir / "uploads")
+ os.environ["LAZIER_OUTPUT_DIR"] = str(self.temp_dir / "outputs")
+
+ import lazier.api.main as main_module
+
+ self.main_module = importlib.reload(main_module)
+ self.client = TestClient(self.main_module.create_app())
+ self.client.post("/login", data={"username": "admin", "password": "secret"})
+
+ def tearDown(self):
+ shutil.rmtree(self.temp_dir, ignore_errors=True)
+
+ def test_process_rejects_legacy_double_mode(self):
+ response = self.client.post(
+ "/api/process",
+ json={"url": "https://example.com/page", "format": "txt", "transcribe": True, "summarize": True},
+ )
+
+ self.assertEqual(response.status_code, 400)
+ self.assertIn("nao aceita mais os dois modos", response.json()["detail"])
+
+ def test_upload_persists_job_and_downloads_from_store(self):
+ output_dir = Path(os.environ["LAZIER_OUTPUT_DIR"]) / "2026" / "03" / "31" / "sample-job"
+ output_dir.mkdir(parents=True, exist_ok=True)
+ output_path = output_dir / "transcricao.txt"
+ output_path.write_text("Transcrição persistida", encoding="utf-8")
+
+ with patch(
+ "lazier.api.routes.process_source",
+ return_value={
+ "mode": "transcribe",
+ "input_type": "audio",
+ "source_name": "sample.mp3",
+ "metadata": {"title": "Sample"},
+ "transcription": "Transcrição persistida",
+ "summary": None,
+ "result_path": str(output_path),
+ "transcription_path": str(output_path),
+ "summary_path": None,
+ },
+ ):
+ response = self.client.post(
+ "/api/upload",
+ files={"files": ("sample.mp3", b"fake-audio", "audio/mpeg")},
+ data={"format": "txt", "mode": "transcribe"},
+ )
+
+ self.assertEqual(response.status_code, 200)
+ job_id = response.json()["job_ids"][0]
+
+ history = self.client.get("/api/history")
+ jobs = history.json()["jobs"]
+ self.assertTrue(any(job["id"] == job_id for job in jobs))
+
+ download = self.client.get(f"/api/jobs/{job_id}/download")
+ self.assertEqual(download.status_code, 200)
+
+ def test_history_survives_app_recreation(self):
+ output_dir = Path(os.environ["LAZIER_OUTPUT_DIR"]) / "2026" / "03" / "31" / "sample-job"
+ output_dir.mkdir(parents=True, exist_ok=True)
+ output_path = output_dir / "sumario.txt"
+ output_path.write_text("Resumo persistido", encoding="utf-8")
+
+ with patch(
+ "lazier.api.routes.process_source",
+ return_value={
+ "mode": "summarize",
+ "input_type": "text",
+ "source_name": "https://example.com/article",
+ "metadata": {"title": "Article"},
+ "transcription": "Texto completo em português",
+ "summary": "Resumo persistido",
+ "result_path": str(output_path),
+ "transcription_path": None,
+ "summary_path": str(output_path),
+ },
+ ):
+ response = self.client.post(
+ "/api/process",
+ json={"url": "https://example.com/article", "format": "txt", "mode": "summarize"},
+ )
+
+ job_id = response.json()["job_id"]
+
+ import lazier.api.main as main_module
+
+ reloaded_main = importlib.reload(main_module)
+ second_client = TestClient(reloaded_main.create_app())
+ second_client.post("/login", data={"username": "admin", "password": "secret"})
+
+ history = second_client.get("/api/history")
+ jobs = history.json()["jobs"]
+ persisted_job = next(job for job in jobs if job["id"] == job_id)
+
+ self.assertEqual(persisted_job["status"], "completed")
+ self.assertEqual(persisted_job["mode"], "summarize")
diff --git a/tests/test_jobs.py b/tests/test_jobs.py
@@ -0,0 +1,64 @@
+import os
+import shutil
+import unittest
+import uuid
+from datetime import datetime
+from pathlib import Path
+
+from lazier.core.jobs import JobStore, build_job_artifact_path, build_job_output_dir
+
+
+class JobStoreTests(unittest.TestCase):
+ def setUp(self):
+ self.temp_dir = Path(os.getcwd()) / ".tmp-tests" / f"jobs-{uuid.uuid4().hex[:8]}"
+ self.temp_dir.mkdir(parents=True, exist_ok=True)
+ os.environ["LAZIER_DATA_DIR"] = str(self.temp_dir)
+ os.environ["LAZIER_OUTPUT_DIR"] = str(self.temp_dir)
+ self.store = JobStore(self.temp_dir / "jobs.db")
+
+ def tearDown(self):
+ shutil.rmtree(self.temp_dir, ignore_errors=True)
+
+ def test_persists_and_marks_incomplete_jobs_as_interrupted(self):
+ self.store.create_job(
+ {
+ "id": "job-1",
+ "mode": "transcribe",
+ "status": "processing",
+ "progress": 42,
+ "format": "txt",
+ "source_name": "arquivo.mp3",
+ "created_at": "2026-03-31T12:00:00",
+ }
+ )
+
+ interrupted = self.store.mark_incomplete_as_interrupted()
+ job = self.store.get_job("job-1")
+
+ self.assertEqual(interrupted, 1)
+ self.assertEqual(job["status"], "interrupted")
+ self.assertEqual(job["progress"], 42)
+ self.assertIn("reinicializacao", job["error"])
+
+ def test_builds_output_paths_with_date_job_and_expected_names(self):
+ created_at = datetime(2026, 3, 31, 15, 45, 0)
+ output_dir = build_job_output_dir(
+ job_id="abc12345-xyz",
+ source_name="Minha Reunião.mp3",
+ created_at=created_at,
+ output_root=self.temp_dir,
+ )
+ summary_path = build_job_artifact_path(
+ job_id="abc12345-xyz",
+ source_name="Minha Reunião.mp3",
+ format_type="txt",
+ artifact_kind="summary",
+ created_at=created_at,
+ output_root=self.temp_dir,
+ )
+
+ self.assertIn("2026", str(output_dir))
+ self.assertIn("03", str(output_dir))
+ self.assertIn("31", str(output_dir))
+ self.assertTrue(output_dir.name.startswith("minha-reuniao-abc12345"))
+ self.assertEqual(summary_path.name, "sumario.txt")
diff --git a/tests/test_processing.py b/tests/test_processing.py
@@ -0,0 +1,65 @@
+import os
+import shutil
+import unittest
+import uuid
+from pathlib import Path
+from unittest.mock import patch
+
+from lazier.core.processing import process_source
+
+
+class ProcessingTests(unittest.TestCase):
+ def setUp(self):
+ self.temp_dir = Path(os.getcwd()) / ".tmp-tests" / f"processing-{uuid.uuid4().hex[:8]}"
+ self.temp_dir.mkdir(parents=True, exist_ok=True)
+ os.environ["OPENAI_API_KEY"] = "test-key"
+ os.environ["LAZIER_OUTPUT_DIR"] = str(self.temp_dir)
+ os.environ["LAZIER_DATA_DIR"] = str(self.temp_dir)
+
+ def tearDown(self):
+ shutil.rmtree(self.temp_dir, ignore_errors=True)
+
+ def test_audio_transcribe_generates_portuguese_transcription_file(self):
+ audio_path = self.temp_dir / "sample.mp3"
+ audio_path.write_bytes(b"fake-audio")
+
+ with patch("lazier.core.processing.transcribe_audio", return_value="Hello world"), patch(
+ "lazier.core.processing.render_text_in_portuguese", return_value="Olá mundo"
+ ):
+ result = process_source(
+ str(audio_path),
+ mode="transcribe",
+ output_format="txt",
+ run_id="job-audio",
+ source_name="sample.mp3",
+ output_root=self.temp_dir,
+ )
+
+ self.assertEqual(result["transcription"], "Olá mundo")
+ self.assertTrue(result["result_path"].endswith("transcricao.txt"))
+ self.assertTrue(Path(result["result_path"]).exists())
+
+ def test_text_summarize_generates_summary_file(self):
+ text_path = self.temp_dir / "article.txt"
+ text_path.write_text("This is a long article in English.", encoding="utf-8")
+
+ with patch(
+ "lazier.core.processing.render_text_in_portuguese",
+ return_value="Este é um artigo longo em português.",
+ ), patch(
+ "lazier.core.processing.summarize_text",
+ return_value="Resumo em português.",
+ ):
+ result = process_source(
+ str(text_path),
+ mode="summarize",
+ output_format="txt",
+ run_id="job-text",
+ source_name="article.txt",
+ output_root=self.temp_dir,
+ )
+
+ self.assertEqual(result["summary"], "Resumo em português.")
+ self.assertEqual(result["transcription"], "Este é um artigo longo em português.")
+ self.assertTrue(result["result_path"].endswith("sumario.txt"))
+ self.assertTrue(Path(result["result_path"]).exists())
