Update app.py

app.py

@@ -1,132 +1,139 @@
-import json
 import os
+import json
 os.environ['HF_HOME'] = '/tmp/cache'
 os.environ['TORCH_HOME'] = '/tmp/cache'
 
-
-from fastapi import FastAPI, File, UploadFile, Response
+from fastapi import FastAPI, File, UploadFile
 from fastapi.middleware.cors import CORSMiddleware
 from PIL import Image
 import torch
-from transformers import CLIPProcessor, CLIPModel  # CHANGÉ : CLIP au lieu de Auto
 import io
 import colorthief
 
-# --- Charge le modèle Marqo fashionCLIP ---
-print("⚠️ Démarrage du chargement du modèle...")
-model_name = "Marqo/marqo-fashionCLIP"
-# CHANGÉ : On charge le modèle CLIP standard
-model = CLIPModel.from_pretrained(model_name)
-processor = CLIPProcessor.from_pretrained(model_name)
-print("✅ Modèle chargé avec succès !")
-# ---------------------------------------------------------
-
 app = FastAPI(title="Fashion Detection API")
 
-# Middleware pour autoriser les appels depuis votre application Lovable
+# Middleware CORS
 app.add_middleware(
     CORSMiddleware,
-    allow_origins=["*"],  # Pour le développement. Pour la production, remplacez par l'URL de Lovable.
+    allow_origins=["*"],
     allow_credentials=True,
     allow_methods=["*"],
     allow_headers=["*"],
     expose_headers=["*"]
 )
 
-# Liste de catégories possibles en Anglais. Le modèle comprend mieux l'Anglais.
-# MODIFIEZ CETTE LISTE PERSONNALISEE SELON VOS BESOINS !
+# --- CHARGE LE MODÈLE MARQO FASHIONCLIP ---
+print("⚠️ Démarrage du chargement du modèle Marqo fashionCLIP...")
+model = None
+processor = None
+
+def load_marqo_model():
+    global model, processor
+    try:
+        # Import différé pour éviter les problèmes de compatibilité
+        from transformers import CLIPProcessor, CLIPModel
+        
+        model_name = "Marqo/marqo-fashionCLIP"
+        model = CLIPModel.from_pretrained(
+            model_name,
+            cache_dir="/tmp/cache",
+            torch_dtype=torch.float16  # Réduit la mémoire
+        )
+        processor = CLIPProcessor.from_pretrained(model_name)
+        print("✅ Modèle Marqo fashionCLIP chargé avec succès !")
+    except Exception as e:
+        print(f"❌ Erreur chargement modèle Marqo: {e}")
+        print("Assurez-vous que les versions dans requirements.txt sont compatibles")
+
+# Charge le modèle au démarrage (mais en différé)
+@app.on_event("startup")
+async def startup_event():
+    import threading
+    # Charge le modèle dans un thread séparé pour ne pas bloquer le démarrage
+    thread = threading.Thread(target=load_marqo_model)
+    thread.daemon = True
+    thread.start()
+
+# Catégories fashion simplifiées pour tests
 categories = [
     "a t-shirt", "a dress", "jeans", "a shirt", "a skirt", "sneakers", 
-    "a handbag", "a jacket", "shorts", "a sweater", "a coat", "high heels", 
-    "a scarf", "sunglasses", "a hat", "pants", "a blouse", "boots", 
-    "a sweatshirt", "a jumper", "an apron", "a ball gown", "a bandanna", 
-    "a baseball cap", "a beanie", "a belt", "a beret", "Bermuda shorts", 
-    "baby clothes", "a bib", "a bikini", "a blazer", "a bow tie", 
-    "boxer shorts", "a bra", "a bracelet", "breeches", "a buckle", 
-    "a button", "camouflage", "a cap", "a cape", "a cardigan", "a cloak", 
-    "clogs", "a corset", "a crown", "cuff links", "a dress shirt", 
-    "dungarees", "earmuffs", "earrings", "a flannel shirt", "flip-flops", 
-    "a fur coat", "a gilet", "glasses", "gloves", "a gown", "a Hawaiian shirt", 
-    "a helmet", "a hijab", "a hoodie", "a hospital gown", "jewelry", 
-    "a jumpsuit", "khakis", "a kilt", "knickers", "a lab coat", 
-    "a leather jacket", "leggings", "a leotard", "a life jacket", 
-    "lingerie", "loafers", "a miniskirt", "mittens", "a necklace", 
-    "a nightgown", "a nightshirt", "onesies", "pajamas", "a pantsuit", 
-    "pantyhose", "a parka", "a polo shirt", "a poncho", "a purse", 
-    "a raincoat", "a ring", "a robe", "a rugby shirt", "sandals", 
-    "scrubs", "shoes", "slippers", "socks", "a spacesuit", "stockings", 
-    "a stole", "a suit", "a sun hat", "a sundress", "suspenders", 
-    "sweatpants", "a swimsuit", "a tank top", "a tiara", "a tie", 
-    "a tie clip", "tights", "a toga", "a top", "a top coat", "a top hat", 
-    "a train", "a trench coat", "trousers", "trunks", "a tube top", 
-    "a turban", "a turtleneck", "a tutu", "a tuxedo", "an umbrella", 
-    "a veil", "a vest", "a waistcoat", "a wedding gown", "a wetsuit", 
-    "a windbreaker", "joggers", "palazzo pants", "cargo pants", 
-    "dress pants", "chinos", "a crop top", "a romper", "an insulated jacket", 
-    "a fleece", "a rain jacket", "a running jacket", "a graphic top", 
-    "a skort", "a sports bra", "water shorts", "goggles", "boxing gloves", 
-    "leg gaiters", "a neck gaiter", "a watch", "a swim trunk", 
-    "a pocket watch", "insoles", "climbing shoes"
+    "a handbag", "a jacket", "shorts", "a sweater", "a coat", "high heels"
 ]
 
-# Ajoutez cette route AVANT votre route /analyze
 @app.get("/")
 def read_root():
     return {"message": "Fashion Detection API is running!", "status": "OK"}
 
+@app.get("/health")
+def health_check():
+    return {
+        "model_loaded": model is not None,
+        "processor_loaded": processor is not None,
+        "status": "ready" if model and processor else "loading"
+    }
+
 @app.post("/analyze")
 async def analyze_image(file: UploadFile = File(...)):
-    # 1. Lire l'image envoyée par l'utilisateur
-    contents = await file.read()
-    image = Image.open(io.BytesIO(contents)).convert("RGB")
-
-    # 2. ANALYSE AVEC LE MODÈLE MARQO FASHIONCLIP (CODE CORRIGÉ)
+    # Vérifier que le modèle est chargé
+    if model is None or processor is None:
+        return {"error": "Model not loaded yet. Please wait or check /health endpoint."}
+    
     try:
-        # CHANGÉ : Préparer les inputs correctement pour CLIP
-        inputs = processor(text=categories, images=image, return_tensors="pt", padding=True)
+        # Lire l'image
+        contents = await file.read()
+        image = Image.open(io.BytesIO(contents)).convert("RGB")
+        
+        # Réduire la taille pour économiser la mémoire
+        image.thumbnail((384, 384))
         
-        # Passer through the model
+        # Analyse avec Marqo fashionCLIP
+        inputs = processor(
+            text=categories, 
+            images=image, 
+            return_tensors="pt", 
+            padding=True,
+            truncation=True
+        )
+        
+        # Utiliser le CPU (plus stable sur Hugging Face Spaces free)
         with torch.no_grad():
             outputs = model(**inputs)
         
-        # Récupérer les similarités image-texte
         logits_per_image = outputs.logits_per_image
-        probs = logits_per_image.softmax(dim=1)  # Convertir en probabilités
+        probs = logits_per_image.softmax(dim=1)
         
-        # Trouver la catégorie avec la probabilité la plus élevée
         predicted_class_idx = probs.argmax(dim=1).item()
         category_name = categories[predicted_class_idx]
         confidence_score = probs[0][predicted_class_idx].item()
 
-    except Exception as e:
-        return {"error": f"Erreur lors de l'analyse AI: {str(e)}"}
-
-    # 3. ANALYSE DE LA COULEUR (avec ColorThief)
-    try:
-        # On sauvegarde l'image en mémoire pour ColorThief
+        # Analyse couleur
         img_buffer = io.BytesIO()
         image.save(img_buffer, format="PNG")
         img_buffer.seek(0)
-        # Extrait la couleur dominante
         color_thief = colorthief.ColorThief(img_buffer)
         dominant_color = color_thief.get_color(quality=1)
-        # Convertit le RGB (ex: (255, 0, 0)) en code hexadécimal (ex: #ff0000)
         hex_color = '#%02x%02x%02x' % dominant_color
-    except Exception as e:
-        hex_color = "#000000"  # Couleur noire par défault en cas d'erreur
 
-    # 4. Renvoie le résultat à Lovable
-    return Response(
-        content=json.dumps({
+        return {
             "category": category_name,
             "color_hex": hex_color,
             "confidence": round(confidence_score, 4)
-        }),
-        media_type="application/json",
-        headers={
-            "Access-Control-Allow-Origin": "*",
-            "Access-Control-Allow-Credentials": "true"
         }
-    )  # Arrondit le score de confiance à 4 décimales
 
-# Cette partie est importante pour Hugging Face Spaces
+    except Exception as e:
+        return {"error": f"Erreur lors de l'analyse: {str(e)}"}
+
+# Interface simple pour tester
+@app.get("/test-ui", response_class=HTMLResponse)
+async def test_ui():
+    return """
+    <html>
+        <body>
+            <h1>Test Fashion Detection</h1>
+            <form action="/analyze" method="post" enctype="multipart/form-data">
+                <input type="file" name="file">
+                <input type="submit" value="Analyzer">
+            </form>
+        </body>
+    </html>
+    """