Update app.py

app.py

@@ -1,125 +1,168 @@
 import gradio as gr
 from transformers import pipeline
-from PIL import Image, ImageOps, ImageFilter
+from PIL import Image
 import numpy as np
-import cv2
 
-# Catégories précises et distinctes
+# Catégories de vêtements bien définies
 FASHION_CATEGORIES = [
     "t-shirt", "button-down shirt", "polo shirt", 
     "sweatshirt", "hoodie", "sweater",
     "jacket", "coat", "blazer",
     "dress", "long dress", "short dress",
     "skirt", "long skirt", "short skirt",
-    "jeans", "pants", "shorts",
+    "jeans", "pants", "shorts", "leggings",
     "sneakers", "boots", "heels", "sandals"
 ]
 
-# Chargement des modèles
-print("🔧 Loading models...")
-seg_pipe = pipeline("image-segmentation", model="mattmdjaga/segformer_b2_clothes")
-class_pipe = pipeline("zero-shot-image-classification", model="openai/clip-vit-base-patch32")
-print("✅ Models loaded!")
+print("🔧 Loading classification model...")
+# Modèle principal pour l'analyse globale
+class_pipe = pipeline(
+    "zero-shot-image-classification", 
+    model="openai/clip-vit-base-patch32"
+)
 
-def preprocess_image(image):
-    """Prétraitement avancé de l'image"""
-    # Conversion en numpy array si nécessaire
-    if isinstance(image, np.ndarray):
-        image = Image.fromarray(image)
-    
-    # Réduction de taille pour meilleures performances
-    image = image.resize((512, 512))
-    
-    return image
+# Modèle de secours pour confirmation
+backup_pipe = pipeline(
+    "image-classification",
+    model="google/vit-base-patch16-224"
+)
 
-def remove_background(image):
-    """Suppression professionnelle de l'arrière-plan"""
-    # Segmentation
-    segments = seg_pipe(image)
-    if not segments:
-        return image
-    
-    # Trouver le vêtement principal
-    largest_segment = max(segments, key=lambda x: np.sum(x['mask']))
-    mask = np.array(largest_segment['mask'])
-    
-    # Application du masque
-    image_np = np.array(image)
-    masked_image = np.zeros_like(image_np)
-    masked_image[mask > 0] = image_np[mask > 0]
-    
-    return Image.fromarray(masked_image)
+print("✅ Models loaded successfully!")
 
-def classify_fashion(image):
-    """Classification précise"""
+def analyze_complete_image(image):
+    """Analyse l'image ENTIÈRE sans segmentation"""
     try:
-        # Préprocessing
-        processed_image = preprocess_image(image)
+        if image is None:
+            return "❌ Please upload an image first", None
+            
+        # Conversion en PIL Image si nécessaire
+        if isinstance(image, np.ndarray):
+            image = Image.fromarray(image)
         
-        # Suppression de l'arrière-plan
-        isolated_image = remove_background(processed_image)
+        # Réduction de taille pour de meilleures performances
+        image = image.resize((512, 512))
         
-        # Classification avec paramètres optimisés
+        # 🔥 ANALYSE PRINCIPALE - Image entière
         predictions = class_pipe(
-            isolated_image, 
+            image, 
             candidate_labels=FASHION_CATEGORIES,
-            hypothesis_template="a clear photo of {}",
+            hypothesis_template="a complete photo of {}",
             multi_label=False
         )
         
-        # Filtrage des résultats (seulement > 10% de confiance)
-        filtered_predictions = [p for p in predictions if p['score'] > 0.1]
+        # 🔥 ANALYSE DE CONFIRMATION avec modèle secondaire
+        backup_preds = backup_pipe(image)
+        
+        # Filtrage des résultats peu confidentiels
+        confident_predictions = [
+            p for p in predictions 
+            if p['score'] > 0.15  # Seuil de confiance augmenté
+        ]
         
-        if not filtered_predictions:
-            return "❌ No confident prediction. Try a clearer image.", isolated_image
+        if not confident_predictions:
+            return "❌ No confident prediction. Try a clearer image.", image
         
         # Formatage des résultats
-        result_text = "🎯 **Top Predictions:**\n\n"
-        for i, pred in enumerate(filtered_predictions[:3]):
+        result_text = "🎯 **Fashion Analysis Results:**\n\n"
+        result_text += "**Main predictions:**\n"
+        
+        for i, pred in enumerate(confident_predictions[:3]):
             result_text += f"{i+1}. **{pred['label']}**: {pred['score']*100:.1f}%\n"
         
-        return result_text, isolated_image
+        # Ajouter la prédiction du modèle de secours
+        if backup_preds:
+            result_text += f"\n**Secondary model suggests**: {backup_preds[0]['label']}\n"
+            result_text += f"**Confidence**: {backup_preds[0]['score']*100:.1f}%"
+        
+        # Conseils basés sur la prédiction
+        top_pred = confident_predictions[0]['label']
+        result_text += f"\n\n💡 **Tip**: For better accuracy, make sure the {top_pred} is clearly visible and centered."
+        
+        return result_text, image
         
     except Exception as e:
         return f"❌ Error: {str(e)}", None
 
-# Interface améliorée
-with gr.Blocks(theme=gr.themes.Soft(), css=".gradio-container {max-width: 800px;}") as demo:
-    gr.Markdown("# 👗 **Fashion AI - Professional Classifier**")
+# Interface optimisée
+with gr.Blocks(
+    title="Fashion AI - Complete Image Analysis",
+    theme=gr.themes.Soft(),
+    css="""
+    .gradio-container { max-width: 900px; margin: auto; }
+    .header { text-align: center; margin-bottom: 20px; }
+    """
+) as demo:
+    
+    gr.Markdown("""
+    <div class='header'>
+    <h1>👗 Fashion AI - Complete Image Analysis</h1>
+    <p>Analyzes the ENTIRE image without cropping or segmentation</p>
+    </div>
+    """)
     
     with gr.Row():
-        with gr.Column():
+        with gr.Column(scale=1):
             gr.Markdown("### 📤 Upload Image")
-            image_input = gr.Image(type="pil", label="Clothing Image")
-            process_btn = gr.Button("🚀 Analyze Image", variant="primary")
+            image_input = gr.Image(
+                type="pil", 
+                label="Upload Complete Image",
+                height=300
+            )
+            analyze_btn = gr.Button(
+                "🔍 Analyze Complete Image", 
+                variant="primary",
+                size="lg"
+            )
             
+        with gr.Column(scale=1):
+            gr.Markdown("### 📊 Analysis Results")
+            output_text = gr.Markdown(
+                label="Results",
+                show_label=False
+            )
+            output_image = gr.Image(
+                label="Original Image (for reference)",
+                interactive=False,
+                height=300
+            )
+    
+    # Section d'instructions
+    with gr.Row():
         with gr.Column():
-            gr.Markdown("### 📊 Results")
-            output_text = gr.Markdown(label="Analysis")
-            output_image = gr.Image(label="Processed Image", interactive=False)
+            gr.Markdown("""
+            ### 💡 Best Practices:
+            - ✅ **Full garment visible** - don't crop
+            - ✅ **Good lighting** - no shadows
+            - ✅ **Neutral background** - less distraction
+            - ✅ **Single item** - one piece per photo
+            - ✅ **Clear view** - front angle preferred
+            """)
     
-    # Instructions
-    gr.Markdown("""
-    ### 💡 **Pro Tips for Best Results:**
-    - ✅ Use clear, well-lit photos
-    - ✅ Center the clothing item
-    - ✅ Use plain backgrounds when possible
-    - ✅ Avoid multiple items in one photo
-    - ❌ Don't use blurry or dark images
-    """)
+    # Section d'exemples
+    with gr.Row():
+        gr.Markdown("""
+        ### 🎯 Examples of good images:
+        - Full t-shirt visible on plain background
+        - Complete dress without cropping
+        - Entire pair of jeans clearly visible
+        """)
     
-    # Events
-    process_btn.click(
-        fn=classify_fashion,
+    # Événements
+    analyze_btn.click(
+        fn=analyze_complete_image,
         inputs=image_input,
         outputs=[output_text, output_image]
     )
     
     image_input.upload(
-        fn=classify_fashion,
+        fn=analyze_complete_image,
         inputs=image_input,
         outputs=[output_text, output_image]
     )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        share=False
+    )