Update app.py

app.py

@@ -20,58 +20,149 @@ class MultiAttributeClassifier:
     def load_classification_models(self):
         """Load all classification models and encoders"""
         print("Loading classification models...")
+        print(f"📂 Looking for models in: models/classification/")
+        
+        # First, let's see what's actually in the classification folder
+        classification_path = "models/classification"
+        if os.path.exists(classification_path):
+            print(f"📁 Found classification directory")
+            files = os.listdir(classification_path)
+            print(f"📄 Available files: {files}")
+        else:
+            print(f"❌ Classification directory not found: {classification_path}")
+            return
         
         for category in self.categories:
             try:
                 # Load model from correct path
                 model_path = f"models/classification/{category}_model.h5"
                 if os.path.exists(model_path):
+                    print(f"🔍 Loading model: {model_path}")
                     self.models[category] = tf.keras.models.load_model(model_path)
-                    print(f"✅ Loaded {category} model")
+                    print(f"✅ Loaded {category} model ({os.path.getsize(model_path)/1024/1024:.1f} MB)")
                     
                     # Load encoder
                     encoder_path = f"models/classification/{category}_encoder.pkl"
                     if os.path.exists(encoder_path):
                         with open(encoder_path, 'rb') as f:
                             self.encoders[category] = pickle.load(f)
+                        print(f"✅ Loaded {category} encoder")
                     else:
-                        print(f"⚠️ {category} encoder not found")
+                        print(f"⚠️ {category} encoder not found at {encoder_path}")
                 else:
-                    print(f"⚠️ {category} model not found at {model_path}")
+                    print(f"❌ {category} model not found at {model_path}")
             except Exception as e:
                 print(f"❌ Failed to load {category}: {e}")
+                import traceback
+                traceback.print_exc()
+        
+        print(f"🎯 Successfully loaded {len(self.models)} classification models")
     
     def load_gan_models(self):
         """Load all GAN models for style transfer"""
         print("Loading GAN models...")
         
+        # First, let's scan what's actually in the GAN folders
+        gan_base_path = "models/gan"
+        if os.path.exists(gan_base_path):
+            print(f"📂 Found GAN models directory: {gan_base_path}")
+            for folder in os.listdir(gan_base_path):
+                folder_path = os.path.join(gan_base_path, folder)
+                if os.path.isdir(folder_path):
+                    print(f"📁 GAN folder: {folder}")
+                    for file in os.listdir(folder_path):
+                        print(f"   📄 {file}")
+        
+        # Try multiple possible file name patterns
         gan_paths = {
-            # Day/Night models
-            'day_to_night': 'models/gan/day_night/day_to_night_generator_final.keras',
-            'night_to_day': 'models/gan/day_night/night_to_day_generator_final.keras',
+            # Day/Night models - try multiple naming patterns
+            'day_to_night': [
+                'models/gan/day_night/day_to_night_generator_final.keras',
+                'models/gan/day_night/day_to_night_generator.keras',
+                'models/gan/day_night/day_to_night.keras',
+                'models/gan/day_night/generator_day_to_night.keras'
+            ],
+            'night_to_day': [
+                'models/gan/day_night/night_to_day_generator_final.keras',
+                'models/gan/day_night/night_to_day_generator.keras', 
+                'models/gan/day_night/night_to_day.keras',
+                'models/gan/day_night/generator_night_to_day.keras'
+            ],
             
-            # Foggy/Clear models  
-            'foggy_to_clear': 'models/gan/foggy/foggy_to_normal_generator_final.keras',
-            'clear_to_foggy': 'models/gan/foggy/normal_to_foggy_generator_final.keras',
+            # Foggy/Clear models
+            'foggy_to_clear': [
+                'models/gan/foggy/foggy_to_normal_generator_final.keras',
+                'models/gan/foggy/foggy_to_clear_generator.keras',
+                'models/gan/foggy/foggy_to_clear.keras'
+            ],
+            'clear_to_foggy': [
+                'models/gan/foggy/normal_to_foggy_generator_final.keras',
+                'models/gan/foggy/clear_to_foggy_generator.keras',
+                'models/gan/foggy/clear_to_foggy.keras'
+            ],
             
             # Japanese art models
-            'photo_to_japanese': 'models/gan/japanese/photo_to_ukiyoe_generator.keras',
-            'japanese_to_photo': 'models/gan/japanese/ukiyoe_to_photo_generator.keras',
+            'photo_to_japanese': [
+                'models/gan/japanese/photo_to_ukiyoe_generator.keras',
+                'models/gan/japanese/photo_to_japanese_generator.keras',
+                'models/gan/japanese/photo_to_japanese.keras'
+            ],
+            'japanese_to_photo': [
+                'models/gan/japanese/ukiyoe_to_photo_generator.keras',
+                'models/gan/japanese/japanese_to_photo_generator.keras',
+                'models/gan/japanese/japanese_to_photo.keras'
+            ],
             
             # Season models
-            'summer_to_winter': 'models/gan/summer_winter/summer_to_winter_generator_final.keras',
-            'winter_to_summer': 'models/gan/summer_winter/winter_to_summer_generator_final.keras'
+            'summer_to_winter': [
+                'models/gan/summer_winter/summer_to_winter_generator_final.keras',
+                'models/gan/summer_winter/summer_to_winter_generator.keras',
+                'models/gan/summer_winter/summer_to_winter.keras'
+            ],
+            'winter_to_summer': [
+                'models/gan/summer_winter/winter_to_summer_generator_final.keras',
+                'models/gan/summer_winter/winter_to_summer_generator.keras',
+                'models/gan/summer_winter/winter_to_summer.keras'
+            ]
         }
         
-        for model_name, model_path in gan_paths.items():
-            try:
-                if os.path.exists(model_path):
-                    self.gan_models[model_name] = tf.keras.models.load_model(model_path)
-                    print(f"✅ Loaded GAN: {model_name}")
-                else:
-                    print(f"⚠️ GAN model not found: {model_path}")
-            except Exception as e:
-                print(f"❌ Failed to load GAN {model_name}: {e}")
+        for model_name, possible_paths in gan_paths.items():
+            model_loaded = False
+            for model_path in possible_paths:
+                try:
+                    if os.path.exists(model_path):
+                        print(f"🔍 Trying to load: {model_path}")
+                        self.gan_models[model_name] = tf.keras.models.load_model(model_path)
+                        print(f"✅ Loaded GAN: {model_name} from {model_path}")
+                        model_loaded = True
+                        break
+                except Exception as e:
+                    print(f"❌ Failed to load {model_path}: {e}")
+                    continue
+            
+            if not model_loaded:
+                print(f"⚠️ Could not load GAN model: {model_name}")
+                # Let's also scan the actual directory to see what files exist
+                folder_map = {
+                    'day_to_night': 'day_night',
+                    'night_to_day': 'day_night', 
+                    'foggy_to_clear': 'foggy',
+                    'clear_to_foggy': 'foggy',
+                    'photo_to_japanese': 'japanese',
+                    'japanese_to_photo': 'japanese',
+                    'summer_to_winter': 'summer_winter',
+                    'winter_to_summer': 'summer_winter'
+                }
+                
+                if model_name in folder_map:
+                    folder_path = f"models/gan/{folder_map[model_name]}"
+                    if os.path.exists(folder_path):
+                        print(f"   📁 Available files in {folder_path}:")
+                        for file in os.listdir(folder_path):
+                            if file.endswith(('.keras', '.h5')):
+                                print(f"      📄 {file}")
+        
+        print(f"🎯 Successfully loaded {len(self.gan_models)} GAN models")
     
     def preprocess_image_for_classification(self, image_path):
         """Preprocess image for classification (224x224)"""
@@ -220,7 +311,6 @@ def get_available_transfers(classification_results):
                 })
     
     # Add season transfers (these work regardless of season classification)
-    # You might want to add season classification logic here if you have a season model
     transfers.extend([
         {
             'name': 'Add Winter Atmosphere',
@@ -239,7 +329,16 @@ def get_available_transfers(classification_results):
     return transfers
 
 # Initialize classifier globally
+print("🚀 Starting StyleTransfer App...")
 classifier = MultiAttributeClassifier()
+print(f"🎯 Initialization complete!")
+print(f"   📊 Classification models loaded: {len(classifier.models)}")
+print(f"   🎨 GAN models loaded: {len(classifier.gan_models)}")
+if len(classifier.models) > 0:
+    print(f"   ✅ Available categories: {list(classifier.models.keys())}")
+if len(classifier.gan_models) > 0:
+    print(f"   ✅ Available transformations: {list(classifier.gan_models.keys())}")
+print("="*50)
 
 def analyze_image(image):
     """Main analysis function"""
@@ -371,11 +470,9 @@ def apply_style_transfer(original_image, selected_transfers):
             os.unlink(temp_path)
 
 # Create Gradio interface
-with gr.Blocks(
-    title="🎨 Intelligent Style Transfer System",
-    theme=gr.themes.Soft()
-) as demo:
-    
+demo = gr.Blocks(title="🎨 Intelligent Style Transfer System")
+
+with demo:
     gr.Markdown("""
     # 🎨 Intelligent Multi-Attribute Style Transfer
     
@@ -398,8 +495,7 @@ with gr.Blocks(
             
             analyze_btn = gr.Button(
                 "🔍 Analyze Image",
-                variant="primary",
-                size="lg"
+                variant="primary"
             )
         
         with gr.Column(scale=1):
@@ -418,8 +514,7 @@ with gr.Blocks(
         apply_btn = gr.Button(
             "✨ Apply Selected Transfers",
             variant="secondary",
-            visible=False,
-            size="lg"
+            visible=False
         )
     
     with gr.Row():
@@ -433,20 +528,6 @@ with gr.Blocks(
                 value="Select transfers and click 'Apply' to see the magic happen!"
             )
     
-    # Example images (add some to your examples folder)
-    gr.Examples(
-        examples=[
-            ["examples/example(1).jpg"],
-            ["examples/example(2).jpg"], 
-            ["examples/example(3).jpg"],
-            ["examples/example(4).jpg"],
-            ["examples/example(5).jpg"], 
-            ["examples/example(6).jpg"],
-        ],
-        inputs=input_image,
-        label="🖼️ Try Example Images"
-    )
-    
     # Connect the interface
     analyze_btn.click(
         fn=analyze_image,
@@ -482,12 +563,11 @@ with gr.Blocks(
     Multiple transformations can be combined for creative effects!
     """)
 
-# Launch configuration for Docker deployment
+# Launch configuration for Hugging Face Spaces
 if __name__ == "__main__":
     demo.launch(
-        server_name="0.0.0.0",  # Listen on all interfaces (required for Docker)
-        server_port=7860,       # Use port 7860 (Hugging Face default)
-        share=True,             # Create shareable link for Docker environments
-        show_error=True,        # Show detailed errors for debugging
-        enable_queue=True       # Enable queuing for better stability
+        server_name="0.0.0.0",
+        server_port=7860,
+        share=True,
+        show_error=True
     )