Upload 2 files

api.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 from fastapi import FastAPI, UploadFile, File, Form, BackgroundTasks, HTTPException
-from fastapi.responses import JSONResponse
+from fastapi.responses import JSONResponse, FileResponse
 from fastapi.middleware.cors import CORSMiddleware
 from pathlib import Path
 import shutil
@@ -10,6 +10,7 @@ import uuid
 from datetime import datetime
 from typing import Dict
 from enum import Enum
+import os
 
 from video_processing import process_video_pipeline
 from casting_loader import ensure_chroma, build_faces_index, build_voices_index
@@ -125,6 +126,19 @@ def get_job_status(job_id: str):
     
     return response
 
+@app.get("/files/{video_name}/{char_id}/{filename}")
+def serve_character_file(video_name: str, char_id: str, filename: str):
+    """
+    Sirve archivos estáticos de personajes (imágenes).
+    Ejemplo: /files/dif_catala_1/char1/representative.jpg
+    """
+    file_path = TEMP_ROOT / video_name / char_id / filename
+    
+    if not file_path.exists():
+        raise HTTPException(status_code=404, detail="File not found")
+    
+    return FileResponse(file_path)
+
 def process_video_job(job_id: str):
     """
     Procesa el vídeo de forma asíncrona.
@@ -155,7 +169,8 @@ def process_video_job(job_id: str):
                 video_path=video_path,
                 output_base=str(base),
                 epsilon=epsilon,
-                min_cluster_size=min_cluster_size
+                min_cluster_size=min_cluster_size,
+                video_name=video_name
             )
             
             characters = result.get("characters", [])

character_detection.py

@@ -34,15 +34,17 @@ class CharacterDetector:
     Detector de personajes que integra el trabajo de Ana.
     """
     
-    def __init__(self, video_path: str, output_base: Path):
+    def __init__(self, video_path: str, output_base: Path, video_name: str = None):
         """
         Args:
             video_path: Ruta al archivo de vídeo
             output_base: Directorio base para guardar resultados (ej: /tmp/temp/video_name)
+            video_name: Nombre del vídeo (para construir URLs)
         """
         self.video_path = video_path
         self.output_base = Path(output_base)
         self.output_base.mkdir(parents=True, exist_ok=True)
+        self.video_name = video_name or self.output_base.name
         
         # Crear subdirectorios
         self.faces_dir = self.output_base / "faces"
@@ -227,10 +229,14 @@ class CharacterDetector:
                     shutil.copy(representative_src, representative_dst)
             
             # Metadata del personaje
+            # Construir URL relativa para la imagen
+            image_url = f"/files/{self.video_name}/{char_id}/representative.jpg"
+            
             characters.append({
                 "id": char_id,
                 "name": f"Personatge {cluster_id + 1}",
-                "image_path": str(char_dir / "representative.jpg"),
+                "image_path": str(char_dir / "representative.jpg"),  # Ruta local
+                "image_url": image_url,  # URL para el API
                 "num_faces": len(face_indices),
                 "folder": str(char_dir)
             })
@@ -298,7 +304,8 @@ class CharacterDetector:
 
 # Función de conveniencia para usar en el API
 def detect_characters_from_video(video_path: str, output_base: str, 
-                                 epsilon: float = 0.5, min_cluster_size: int = 2) -> Dict[str, Any]:
+                                 epsilon: float = 0.5, min_cluster_size: int = 2,
+                                 video_name: str = None) -> Dict[str, Any]:
     """
     Función de alto nivel para detectar personajes en un vídeo.
     
@@ -307,11 +314,12 @@ def detect_characters_from_video(video_path: str, output_base: str,
         output_base: Directorio base para guardar resultados
         epsilon: Parámetro epsilon para DBSCAN
         min_cluster_size: Tamaño mínimo de cluster
+        video_name: Nombre del vídeo (para construir URLs)
     
     Returns:
         Dict con resultados: {"characters": [...], "analysis_path": "..."}
     """
-    detector = CharacterDetector(video_path, Path(output_base))
+    detector = CharacterDetector(video_path, Path(output_base), video_name=video_name)
     characters, analysis_path = detector.detect_characters(epsilon, min_cluster_size)
     
     return {