Update app.py

app.py

@@ -2,106 +2,73 @@ import gradio as gr
 from transformers import pipeline
 from PIL import Image
 import numpy as np
-import cv2
-import time
 
-# Import des catégories
-from categories import FASHION_CATEGORIES
+# Liste des catégories de vêtements
+FASHION_CATEGORIES = [
+    "t-shirt", "long sleeve shirt", "short sleeve shirt", 
+    "sleeveless shirt", "polo shirt", "sweatshirt", 
+    "hoodie", "sweater", "cardigan", "jacket", "coat", 
+    "blazer", "dress", "long dress", "short dress", 
+    "skirt", "long skirt", "short skirt", "jeans", 
+    "pants", "trousers", "shorts", "leggings", 
+    "sports shoes", "sneakers", "boots", "heels", "sandals"
+]
 
-# Initialisation des modèles
-print("🔧 Loading models...")
-try:
-    # Modèle de segmentation
-    seg_pipe = pipeline(
-        "image-segmentation", 
-        model="mattmdjaga/segformer_b2_clothes",
-        device=-1  # Force l'utilisation du CPU pour plus de stabilité
-    )
-    
-    # Modèle de classification
-    class_pipe = pipeline(
-        "zero-shot-image-classification", 
-        model="openai/clip-vit-base-patch32",
-        device=-1  # Force l'utilisation du CPU
-    )
-    print("✅ Models loaded successfully!")
-except Exception as e:
-    print(f"❌ Error loading models: {e}")
-    raise e
+# Charger le modèle de classification
+print("Loading classification model...")
+class_pipe = pipeline(
+    "zero-shot-image-classification", 
+    model="openai/clip-vit-base-patch32"
+)
+print("Model loaded successfully!")
 
-def process_image(input_image):
-    """Traite l'image et retourne les résultats"""
+def classify_image(input_image):
+    """Fonction simple pour classifier les images"""
     try:
         if input_image is None:
-            return "⚠️ Please upload an image first", None, None
+            return "Please upload an image first"
         
-        # Conversion en PIL Image
+        # Convertir en format PIL si nécessaire
         if isinstance(input_image, np.ndarray):
-            pil_image = Image.fromarray(input_image)
-        else:
-            pil_image = input_image
-        
-        # Redimensionnement pour éviter les problèmes de mémoire
-        pil_image = pil_image.resize((224, 224))
-        
-        # Étape 1: Segmentation
-        print("🔍 Segmenting image...")
-        segments = seg_pipe(pil_image)
-        
-        if not segments:
-            return "❌ No clothing detected", None, None
-        
-        # Trouver le plus grand segment
-        largest_segment = max(segments, key=lambda x: np.sum(x['mask']))
-        mask = largest_segment['mask']
+            input_image = Image.fromarray(input_image)
         
-        # Étape 2: Extraction du vêtement
-        mask_np = np.array(mask).astype(np.uint8) * 255
-        masked_image = cv2.bitwise_and(np.array(pil_image), np.array(pil_image), mask=mask_np)
-        masked_pil = Image.fromarray(masked_image)
+        # Redimensionner pour de meilleures performances
+        input_image = input_image.resize((224, 224))
         
-        # Étape 3: Classification
-        print("📊 Classifying...")
+        # Classification
         predictions = class_pipe(
-            masked_pil, 
+            input_image, 
             candidate_labels=FASHION_CATEGORIES,
             hypothesis_template="This is a photo of {}"
         )
         
-        # Formatage des résultats
-        result_text = "🎯 Classification Results:\n\n"
-        for i, pred in enumerate(predictions[:3]):
+        # Formater les résultats
+        result_text = "👗 Classification Results:\n\n"
+        for i, pred in enumerate(predictions[:5]):
             result_text += f"{i+1}. {pred['label']}: {pred['score']*100:.1f}%\n"
         
-        return result_text, masked_pil, pil_image
+        return result_text
         
     except Exception as e:
-        return f"❌ Error: {str(e)}", None, None
+        return f"Error: {str(e)}"
 
-# Interface Gradio améliorée
-with gr.Blocks(
-    title="Fashion Classifier", 
-    theme=gr.themes.Soft(),
-    css="""
-    .gradio-container {max-width: 900px !important;}
-    """
-) as demo:
-    
+# Interface Gradio simple
+with gr.Blocks(title="Fashion Classifier", theme=gr.themes.Soft()) as demo:
     gr.Markdown("""
     # 👗 Fashion Category Classifier
-    Upload a picture of clothing. The AI will detect and classify it.
+    Upload a picture of clothing to classify it.
     """)
     
     with gr.Row():
         with gr.Column():
             image_input = gr.Image(
-                label="📤 Upload Image",
+                label="📤 Upload Clothing Image",
                 type="pil",
-                height=200
+                height=300
             )
             
-            process_btn = gr.Button(
-                "🚀 Process Image", 
+            classify_btn = gr.Button(
+                "🔍 Classify Image", 
                 variant="primary",
                 size="lg"
             )
@@ -109,52 +76,36 @@ with gr.Blocks(
         with gr.Column():
             output_text = gr.Textbox(
                 label="📊 Results",
-                lines=5,
+                lines=8,
                 interactive=False
             )
-            
-            with gr.Row():
-                original_output = gr.Image(
-                    label="Original",
-                    type="pil",
-                    height=200,
-                    interactive=False
-                )
-                masked_output = gr.Image(
-                    label="Detected Item",
-                    type="pil",
-                    height=200,
-                    interactive=False
-                )
     
     # Instructions
     gr.Markdown("""
-    ### 📝 Instructions:
-    1. Upload an image of clothing
-    2. Click 'Process Image' 
+    ### 📝 How to use:
+    1. Upload an image of a clothing item
+    2. Click the 'Classify Image' button
     3. See the classification results
     """)
     
-    # Lier les événements
-    process_btn.click(
-        fn=process_image,
+    # Lier le bouton à la fonction
+    classify_btn.click(
+        fn=classify_image,
         inputs=image_input,
-        outputs=[output_text, masked_output, original_output]
+        outputs=output_text
     )
     
-    # Exemple de texte
-    gr.Markdown("""
-    ### 💡 Tips:
-    - Use clear, well-lit photos
-    - Focus on one clothing item at a time
-    - Avoid busy backgrounds for better results
-    """)
+    # Ajouter aussi le changement sur l'upload
+    image_input.upload(
+        fn=classify_image,
+        inputs=image_input,
+        outputs=output_text
+    )
 
-# Lancement de l'application
+# Lancer l'application
 if __name__ == "__main__":
     demo.launch(
         server_name="0.0.0.0",
         server_port=7860,
-        share=False,
-        debug=True
+        share=False
     )