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MODLI commited on Sep 11, 2025

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e3527ed

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1 Parent(s): 087c8f5

Update app.py

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Files changed (1) hide show

app.py +49 -21

app.py CHANGED Viewed

@@ -5,11 +5,12 @@ os.environ['TORCH_HOME'] = '/tmp/cache'
 from fastapi import FastAPI, File, UploadFile
 from fastapi.middleware.cors import CORSMiddleware
-from fastapi.responses import HTMLResponse  # ← AJOUT IMPORT MANQUANT
 from PIL import Image
 import torch
 import io
 import colorthief
 app = FastAPI(title="Fashion Detection API")
@@ -38,24 +39,21 @@ def load_marqo_model():
         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"
@@ -75,7 +73,6 @@ def health_check():
 @app.post("/analyze")
 async def analyze_image(file: UploadFile = File(...)):
-    # 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."}
@@ -84,7 +81,7 @@ async def analyze_image(file: UploadFile = File(...)):
         contents = await file.read()
         image = Image.open(io.BytesIO(contents)).convert("RGB")
-        # Réduire la taille pour économiser la mémoire
         image.thumbnail((384, 384))
         # Analyse avec Marqo fashionCLIP
@@ -96,7 +93,6 @@ async def analyze_image(file: UploadFile = File(...)):
             truncation=True
         )
-        # Utiliser le CPU (plus stable sur Hugging Face Spaces free)
         with torch.no_grad():
             outputs = model(**inputs)
@@ -107,13 +103,24 @@ async def analyze_image(file: UploadFile = File(...)):
         category_name = categories[predicted_class_idx]
         confidence_score = probs[0][predicted_class_idx].item()
-        # Analyse couleur
-        img_buffer = io.BytesIO()
-        image.save(img_buffer, format="PNG")
-        img_buffer.seek(0)
-        color_thief = colorthief.ColorThief(img_buffer)
-        dominant_color = color_thief.get_color(quality=1)
-        hex_color = '#%02x%02x%02x' % dominant_color
         return {
             "category": category_name,
@@ -129,12 +136,33 @@ async def analyze_image(file: UploadFile = File(...)):
 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>
     """

 from fastapi import FastAPI, File, UploadFile
 from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import HTMLResponse
 from PIL import Image
 import torch
 import io
 import colorthief
+import tempfile
 app = FastAPI(title="Fashion Detection API")
         model = CLIPModel.from_pretrained(
             model_name,
             cache_dir="/tmp/cache",
+            torch_dtype=torch.float16
         )
         processor = CLIPProcessor.from_pretrained(model_name)
         print("✅ Modèle Marqo fashionCLIP chargé avec succès !")
     except Exception as e:
         print(f"❌ Erreur chargement modèle Marqo: {e}")
 @app.on_event("startup")
 async def startup_event():
     import threading
     thread = threading.Thread(target=load_marqo_model)
     thread.daemon = True
     thread.start()
+# Catégories fashion simplifiées
 categories = [
     "a t-shirt", "a dress", "jeans", "a shirt", "a skirt", "sneakers",
     "a handbag", "a jacket", "shorts", "a sweater", "a coat", "high heels"
 @app.post("/analyze")
 async def analyze_image(file: UploadFile = File(...)):
     if model is None or processor is None:
         return {"error": "Model not loaded yet. Please wait or check /health endpoint."}
         contents = await file.read()
         image = Image.open(io.BytesIO(contents)).convert("RGB")
+        # Réduire la taille
         image.thumbnail((384, 384))
         # Analyse avec Marqo fashionCLIP
             truncation=True
         )
         with torch.no_grad():
             outputs = model(**inputs)
         category_name = categories[predicted_class_idx]
         confidence_score = probs[0][predicted_class_idx].item()
+        # --- CORRECTION DE L'ANALYSE COULEUR ---
+        try:
+            # Sauvegarder l'image dans un fichier temporaire pour ColorThief
+            with tempfile.NamedTemporaryFile(suffix='.jpg', delete=False) as tmp:
+                image.save(tmp, format='JPEG')
+                tmp_path = tmp.name
+            # Utiliser ColorThief avec le fichier temporaire
+            color_thief = colorthief.ColorThief(tmp_path)
+            dominant_color = color_thief.get_color(quality=1)
+            hex_color = '#%02x%02x%02x' % dominant_color
+            # Nettoyer le fichier temporaire
+            os.unlink(tmp_path)
+        except Exception as color_error:
+            print(f"Erreur analyse couleur: {color_error}")
+            hex_color = "#000000"  # Couleur par défaut
         return {
             "category": category_name,
 async def test_ui():
     return """
     <html>
+        <head>
+            <title>Fashion Detection Test</title>
+            <style>
+                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; }
+                input[type="file"] { margin: 10px 0; }
+                input[type="submit"] { background: #007bff; color: white; padding: 10px 20px; border: none; cursor: pointer; }
+            </style>
+        </head>
         <body>
+            <div class="container">
+                <h1>🎨 Test Fashion Detection</h1>
+                <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 l'image 👗">
+                </form>
+                <div style="margin-top: 30px; padding: 20px; background: #f8f9fa;">
+                    <h3>📝 Instructions :</h3>
+                    <p>• Uploader une image claire d'un vêtement</p>
+                    <p>• Formats supportés : JPG, PNG, WebP</p>
+                    <p>• Taille recommandée : moins de 2MB</p>
+                </div>
+            </div>
         </body>
     </html>
     """