Update app.py

app.py

@@ -1,9 +1,10 @@
 import os
 import json
+import time
 os.environ['HF_HOME'] = '/tmp/cache'
 os.environ['TORCH_HOME'] = '/tmp/cache'
 
-from fastapi import FastAPI, File, UploadFile
+from fastapi import FastAPI, File, UploadFile, HTTPException
 from fastapi.middleware.cors import CORSMiddleware
 from fastapi.responses import HTMLResponse
 from PIL import Image
@@ -25,23 +26,30 @@ app.add_middleware(
     expose_headers=["*"]
 )
 
-# --- CHARGE LE MODÈLE MARQO FASHIONSIGLIP ---
+# --- ÉTAT DU MODÈLE ---
 print("⚠️ Démarrage du chargement du modèle Marqo-FashionSigLIP...")
 model = None
 processor = None
+model_loading = False
+model_loaded = False
+model_error = None
 
 def load_fashion_model():
-    global model, processor
+    global model, processor, model_loading, model_loaded, model_error
+    
+    model_loading = True
     try:
         from transformers import AutoModel, AutoProcessor
         
         model_name = "Marqo/Marqo-FashionSigLIP-Classification"
         
-        # Charger le modèle SigLIP spécialisé fashion
+        print("📦 Téléchargement du modèle... (cela peut prendre 5-10 minutes)")
+        
+        # Charger le modèle SigLIP
         model = AutoModel.from_pretrained(
             model_name,
             cache_dir="/tmp/cache",
-            torch_dtype=torch.float16,  # Moins de mémoire
+            torch_dtype=torch.float16,
             trust_remote_code=True
         )
         
@@ -51,55 +59,65 @@ def load_fashion_model():
         )
         
         print("✅ Modèle Marqo-FashionSigLIP chargé avec succès !")
-        print(f"📍 Modèle device: {next(model.parameters()).device}")
+        model_loaded = True
+        model_loading = False
         
     except Exception as e:
         print(f"❌ Erreur chargement modèle: {e}")
+        model_error = str(e)
+        model_loading = False
         import traceback
         traceback.print_exc()
 
-# Catégories de mode pour SigLIP (adaptées au modèle)
+# Démarrer le chargement IMMÉDIATEMENT
+load_fashion_model()
+
+# Catégories de mode
 categories = [
     "t-shirt", "dress", "jeans", "shirt", "skirt", 
     "sneakers", "handbag", "jacket", "shorts", "sweater",
     "coat", "high heels", "blouse", "boots", "hat"
 ]
 
-@app.on_event("startup")
-async def startup_event():
-    import threading
-    thread = threading.Thread(target=load_fashion_model)
-    thread.daemon = True
-    thread.start()
-
 @app.get("/")
 def read_root():
-    return {"message": "Fashion Classification API is running!", "status": "OK"}
+    return {
+        "message": "Fashion Classification API is running!", 
+        "status": "OK",
+        "model_status": "loaded" if model_loaded else "loading" if model_loading else "error",
+        "model_name": "Marqo-FashionSigLIP-Classification"
+    }
 
 @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",
-        "model_name": "Marqo-FashionSigLIP-Classification"
+        "model_loaded": model_loaded,
+        "model_loading": model_loading,
+        "model_error": model_error,
+        "status": "ready" if model_loaded else "loading" if model_loading else "error",
+        "model_name": "Marqo-FashionSigLIP-Classification",
+        "timestamp": time.time()
     }
 
 @app.post("/analyze")
 async def analyze_image(file: UploadFile = File(...)):
+    # Vérifier si le modèle est chargé
+    if not model_loaded:
+        if model_loading:
+            raise HTTPException(status_code=423, detail="Model still loading. Please wait 5-10 minutes and check /health")
+        else:
+            raise HTTPException(status_code=500, detail=f"Model failed to load: {model_error}")
+    
     if model is None or processor is None:
-        return {"error": "Model not loaded yet. Please check /health endpoint."}
+        raise HTTPException(status_code=500, detail="Model not available")
     
     try:
         # Lire et préparer l'image
         contents = await file.read()
         image = Image.open(io.BytesIO(contents)).convert("RGB")
-        
-        # Redimensionner pour SigLIP
         image = image.resize((384, 384))
         
-        # --- TRAITEMENT AVEC SIGLIP ---
-        # Préparer les inputs
+        # Traitement avec SigLIP
         inputs = processor(
             text=categories,
             images=image,
@@ -107,30 +125,23 @@ async def analyze_image(file: UploadFile = File(...)):
             padding=True,
             truncation=True,
             max_length=64,
-            return_overflowing_tokens=False
         )
         
-        # Déplacer sur le device du modèle
         device = next(model.parameters()).device
         inputs = {k: v.to(device) for k, v in inputs.items()}
         
-        # Inférence
         with torch.no_grad():
             outputs = model(**inputs)
         
-        # SigLIP utilise des logits différents
         logits_per_image = outputs.logits_per_image
-        
-        # Convertir en probabilités
-        probs = torch.sigmoid(logits_per_image)  # SigLIP utilise sigmoid, pas softmax!
+        probs = torch.sigmoid(logits_per_image)
         probs = probs.cpu().numpy()[0]
         
-        # Trouver la meilleure catégorie
         predicted_idx = np.argmax(probs)
         category_name = categories[predicted_idx]
         confidence_score = float(probs[predicted_idx])
         
-        # --- ANALYSE COULEUR ---
+        # Analyse couleur
         try:
             with tempfile.NamedTemporaryFile(suffix='.jpg', delete=False) as tmp:
                 image.save(tmp, format='JPEG')
@@ -139,116 +150,84 @@ async def analyze_image(file: UploadFile = File(...)):
             color_thief = colorthief.ColorThief(tmp_path)
             dominant_color = color_thief.get_color(quality=1)
             hex_color = '#%02x%02x%02x' % dominant_color
-            
             os.unlink(tmp_path)
             
-        except Exception as color_error:
-            print(f"⚠️ Erreur analyse couleur: {color_error}")
+        except Exception:
             hex_color = "#000000"
 
-        # --- RÉSULTATS DÉTAILLÉS ---
-        top_categories = []
-        for i, (cat, prob) in enumerate(zip(categories, probs)):
-            if prob > 0.1:  # Seuil minimal
-                top_categories.append({
-                    "category": cat,
-                    "score": round(float(prob), 4)
-                })
-        
-        # Trier par score décroissant
-        top_categories.sort(key=lambda x: x["score"], reverse=True)
-        top_5 = top_categories[:5]
-
         return {
-            "top_prediction": {
-                "category": category_name,
-                "confidence": round(confidence_score, 4),
-                "color_hex": hex_color
-            },
-            "top_categories": top_5,
+            "category": category_name,
+            "confidence": round(confidence_score, 4),
+            "color_hex": hex_color,
             "model": "Marqo-FashionSigLIP-Classification"
         }
 
     except Exception as e:
-        return {"error": f"Erreur lors de l'analyse: {str(e)}"}
+        raise HTTPException(status_code=500, detail=f"Analysis error: {str(e)}")
 
-# Interface de test
+# Interface de test avec statut de chargement
 @app.get("/test-ui", response_class=HTMLResponse)
 async def test_ui():
-    return """
+    return f"""
     <html>
         <head>
             <title>FashionSigLIP Detection</title>
             <style>
-                body { 
-                    font-family: Arial, sans-serif; 
-                    margin: 40px; 
-                    background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
-                    color: white;
-                }
-                .container { 
-                    max-width: 600px; 
-                    margin: 0 auto; 
-                    background: rgba(255, 255, 255, 0.1);
-                    padding: 30px;
-                    border-radius: 15px;
-                    backdrop-filter: blur(10px);
-                }
-                form { 
-                    border: 2px dashed rgba(255, 255, 255, 0.3); 
-                    padding: 30px; 
-                    text-align: center; 
-                    margin-bottom: 20px;
-                }
-                input[type="file"] { 
-                    margin: 15px 0; 
-                    padding: 10px;
-                    background: rgba(255, 255, 255, 0.2);
-                    border: none;
-                    border-radius: 5px;
-                    color: white;
-                }
-                input[type="submit"] { 
-                    background: #ff6b6b; 
-                    color: white; 
-                    padding: 12px 25px; 
-                    border: none; 
-                    cursor: pointer; 
-                    border-radius: 25px;
-                    font-weight: bold;
-                    transition: background 0.3s;
-                }
-                input[type="submit"]:hover {
-                    background: #ee5a52;
-                }
-                .result { 
-                    margin-top: 20px; 
-                    padding: 20px; 
-                    background: rgba(255, 255, 255, 0.1);
-                    border-radius: 10px;
-                }
+                body {{ font-family: Arial, sans-serif; margin: 40px; }}
+                .container {{ max-width: 600px; margin: 0 auto; }}
+                form {{ border: 2px dashed #ccc; padding: 30px; text-align: center; }}
+                .status {{ padding: 15px; margin: 10px 0; border-radius: 5px; }}
+                .loading {{ background: #fff3cd; color: #856404; }}
+                .ready {{ background: #d4edda; color: #155724; }}
+                .error {{ background: #f8d7da; color: #721c24; }}
             </style>
+            <script>
+                function checkStatus() {{
+                    fetch('/health')
+                        .then(response => response.json())
+                        .then(data => {{
+                            const statusDiv = document.getElementById('model-status');
+                            const submitBtn = document.getElementById('submit-btn');
+                            
+                            if (data.model_loaded) {{
+                                statusDiv.innerHTML = '✅ <b>Modèle chargé et prêt !</b>';
+                                statusDiv.className = 'status ready';
+                                submitBtn.disabled = false;
+                            }} else if (data.model_loading) {{
+                                statusDiv.innerHTML = '⏳ <b>Chargement du modèle en cours...</b><br>Cela peut prendre 5-10 minutes';
+                                statusDiv.className = 'status loading';
+                                submitBtn.disabled = true;
+                                setTimeout(checkStatus, 5000); // Re-check dans 5 sec
+                            }} else {{
+                                statusDiv.innerHTML = '❌ <b>Erreur de chargement:</b><br>' + (data.model_error || 'Unknown error');
+                                statusDiv.className = 'status error';
+                                submitBtn.disabled = true;
+                            }}
+                        }});
+                }}
+                
+                // Vérifier le statut au chargement de la page
+                window.onload = checkStatus;
+            </script>
         </head>
         <body>
             <div class="container">
                 <h1>👗 FashionSigLIP Detector</h1>
-                <p>Powered by Marqo/Marqo-FashionSigLIP-Classification</p>
+                
+                <div id="model-status" class="status loading">
+                    Vérification du statut du modèle...
+                </div>
                 
                 <form action="/analyze" method="post" enctype="multipart/form-data">
                     <h3>Uploader une image de vêtement :</h3>
                     <input type="file" name="file" accept="image/*" required>
-                    <br>
-                    <input type="submit" value="Analyser la mode 🎯">
+                    <br><br>
+                    <input type="submit" id="submit-btn" value="Analyser" disabled>
                 </form>
                 
-                <div class="result">
-                    <h3>📊 Résultats :</h3>
-                    <p>Les résultats apparaîtront ici après analyse...</p>
-                </div>
-                
-                <div style="margin-top: 20px; font-size: 12px; opacity: 0.7;">
-                    <p>Modèle : Marqo-FashionSigLIP-Classification</p>
-                    <p>Spécialisé dans la classification de vêtements</p>
+                <div style="margin-top: 20px;">
+                    <button onclick="checkStatus()">Actualiser le statut</button>
+                    <button onclick="location.reload()">Rafraîchir la page</button>
                 </div>
             </div>
         </body>