Update app.py

app.py

@@ -27,57 +27,57 @@ def preprocess_image(image):
     # Convert to PIL Image if needed
     if isinstance(image, np.ndarray):
         image = Image.fromarray(image)
-    
+
     # Convert to RGB if necessary
     if image.mode != 'RGB':
         image = image.convert('RGB')
-    
+
     # Resize to model's expected input size
     image = image.resize((224, 224))
-    
+
     # Convert to numpy array
     img_array = np.array(image).astype('float32')
-    
+
     # Add batch dimension
     img_array = np.expand_dims(img_array, axis=0)
-    
+
     # Note: preprocessing is handled by the Lambda layer in the model
     # So we don't need to apply preprocess_input here
-    
+
     return img_array
 
 def predict(image):
     """
     Predict whether an image is AI-generated or real.
-    
+
     Args:
         image: Input image (PIL Image or numpy array)
-    
+
     Returns:
         Dictionary with class labels and their probabilities
     """
     if model is None:
         return {"Error": 1.0}
-    
+
     if image is None:
         return {"Error": 1.0}
-    
+
     try:
         # Preprocess the image
         processed_image = preprocess_image(image)
-        
-        # Make prediction (training=False to disable augmentation layers)
-        prediction = model.predict(processed_image, verbose=0, training=False)[0][0]
-        
+
+        # Make prediction (removed training=False parameter - not supported in Keras 3)
+        prediction = model.predict(processed_image, verbose=0)[0][0]
+
         # Convert to probabilities for each class
         ai_prob = float(prediction)
         real_prob = 1.0 - ai_prob
-        
+
         return {
             "Real Image": real_prob,
             "AI-Generated": ai_prob
         }
-    
+
     except Exception as e:
         print(f"Prediction error: {e}")
         return {"Error": 1.0}
@@ -85,35 +85,35 @@ def predict(image):
 def classify_image(image):
     """
     Main classification function with detailed output.
-    
+
     Args:
         image: Input image
-    
+
     Returns:
         Tuple of (label_output, confidence_text)
     """
     if model is None:
         return None, "❌ **Model failed to load. Please check the model file.**"
-    
+
     if image is None:
         return None, "Please upload an image to analyze."
-    
+
     results = predict(image)
-    
+
     if "Error" in results:
         return None, "Error processing image. Please try again."
-    
+
     # Determine the classification
     ai_prob = results["AI-Generated"]
     real_prob = results["Real Image"]
-    
+
     if ai_prob > 0.5:
         classification = "AI-Generated"
         confidence = ai_prob
     else:
         classification = "Real Image"
         confidence = real_prob
-    
+
     # Create detailed analysis text
     analysis = f"""
 ### Analysis Results
@@ -124,31 +124,27 @@ def classify_image(image):
 - Real Image: {real_prob:.2%}
 - AI-Generated: {ai_prob:.2%}
 ---
-*Note: This model was trained on CIFAKE and Tiny GenImage datasets. 
+*Note: This model was trained on CIFAKE and Tiny GenImage datasets.
 Results may vary for images outside these domains.*
 """
-    
+
     return results, analysis
 
 # Create the Gradio interface
 with gr.Blocks(
     title="AI Image Detector"
-    # theme=gr.themes.Soft(
-    #     primary_hue="blue",
-    #     secondary_hue="gray",
-    # )
 ) as demo:
     gr.Markdown(
         """
         # 🔍 AI Image Detector
-        
+
         Upload an image to detect whether it's **AI-generated** or a **real photograph**.
-        
-        This model uses transfer learning with MobileNetV2 and was trained on the 
+
+        This model uses transfer learning with MobileNetV2 and was trained on the
         CIFAKE and Tiny GenImage datasets, achieving ~95% accuracy on validation data.
         """
     )
-    
+
     with gr.Row():
         with gr.Column(scale=1):
             input_image = gr.Image(
@@ -156,13 +152,13 @@ with gr.Blocks(
                 type="pil",
                 height=400
             )
-            
+
             submit_btn = gr.Button(
                 "🔎 Analyze Image",
                 variant="primary",
                 size="lg"
             )
-            
+
             gr.Markdown(
                 """
                 ### Tips for best results:
@@ -171,46 +167,46 @@ with gr.Blocks(
                 - Works with JPG, PNG, and other common formats
                 """
             )
-        
+
         with gr.Column(scale=1):
             output_label = gr.Label(
                 label="Classification Results",
                 num_top_classes=2
             )
-            
+
             output_text = gr.Markdown(
                 label="Detailed Analysis"
             )
-    
+
     # Connect the interface
     submit_btn.click(
         fn=classify_image,
         inputs=input_image,
         outputs=[output_label, output_text]
     )
-    
+
     input_image.change(
         fn=classify_image,
         inputs=input_image,
         outputs=[output_label, output_text]
     )
-    
+
     gr.Markdown(
         """
         ---
         ### About this model
-        
+
         - **Architecture:** MobileNetV2 with custom classification head
         - **Training Data:** CIFAKE dataset + Tiny GenImage dataset (~128,000 images)
         - **Input Size:** 224×224 pixels
         - **Classes:** Real (0) vs AI-Generated (1)
-        
-        **Disclaimer:** This tool is for educational and research purposes. 
+
+        **Disclaimer:** This tool is for educational and research purposes.
         AI detection is an evolving field, and no detector is 100% accurate.
-    
+
         """
     )
 
 # Launch the app
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()