Update app.py

app.py

@@ -1,106 +1,114 @@
 import gradio as gr
 from transformers import pipeline
-import os
+import torch
 
-# Global variable to store the pipeline
-deepfake_pipe = None
+# Initialize the deepfake detection pipeline
+print("Loading Jerry's detection system...")
+pipe = pipeline(
+    "image-classification", 
+    model="prithivMLmods/Deep-Fake-Detector-v2-Model",
+    device=0 if torch.cuda.is_available() else -1
+)
+print("Jerry is ready!")
 
-def load_model():
-    """Load the model once and cache it"""
-    global deepfake_pipe
-    if deepfake_pipe is None:
-        print("Loading Deepfake Detection Model...")
-        deepfake_pipe = pipeline(
-            "image-classification", 
-            model="prithivMLmods/Deep-Fake-Detector-v2-Model"
-        )
-        print("Model loaded successfully!")
-    return deepfake_pipe
-
-def predict_deepfake(image):
-    """Predict if image is deepfake or real"""
-    try:
-        # Load model if not already loaded
-        pipe = load_model()
-        
-        # Make prediction
-        results = pipe(image)
-        
-        # Format results
-        prediction = {result['label']: result['score'] for result in results}
-        
-        # Determine final verdict
-        deepfake_score = prediction.get('Deepfake', 0)
-        realism_score = prediction.get('Realism', 0)
-        
-        if deepfake_score > realism_score:
-            verdict = f"🚨 DEEPFAKE DETECTED ({deepfake_score:.2%} confidence)"
-            color = "red"
-        else:
-            verdict = f"✅ REAL IMAGE ({realism_score:.2%} confidence)"
-            color = "green"
+def detect_deepfake(image):
+    """
+    Analyze an image to detect if it's a deepfake
+    """
+    if image is None:
+        return "Please upload an image first!"
+    
+    # Get predictions from the model
+    results = pipe(image)
+    
+    # Format the results
+    output_text = "🔍 **Jerry's Analysis Results:**\n\n"
+    
+    for result in results:
+        label = result['label']
+        confidence = result['score'] * 100
         
-        return prediction, verdict
+        # Create a visual confidence bar
+        bar_length = int(confidence / 5)
+        bar = "█" * bar_length + "░" * (20 - bar_length)
         
-    except Exception as e:
-        return {"Error": 1.0}, f"❌ Prediction failed: {str(e)}"
+        output_text += f"**{label}**: {confidence:.2f}%\n"
+        output_text += f"{bar}\n\n"
+    
+    # Add a conclusion
+    top_result = results[0]
+    if top_result['score'] > 0.7:
+        certainty = "high"
+    elif top_result['score'] > 0.5:
+        certainty = "moderate"
+    else:
+        certainty = "low"
+    
+    output_text += f"\n🎯 **Jerry's Verdict:** {top_result['label']} (with {certainty} confidence)"
+    
+    return output_text
 
 # Create the Gradio interface
-with gr.Blocks(theme=gr.themes.Soft(), title="Jerry - Deepfake Detector") as demo:
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
     gr.Markdown(
         """
-        # 🕵️ Jerry - Deepfake Detection Tool
-        **Upload an image to check if it's real or AI-generated!**
+        # 🕵️ Jerry - Deepfake Detector
+        ### Your AI-Powered Image Authenticity Analyzer
         
+        Upload an image and let Jerry analyze it to determine if it's authentic or artificially generated!
         """
     )
     
     with gr.Row():
-        with gr.Column():
+        with gr.Column(scale=1):
             image_input = gr.Image(
-                type="filepath", 
-                label="Upload Image",
-                height=300
+                label="Upload Image for Analysis",
+                type="pil",
+                height=400
             )
-            analyze_btn = gr.Button("🔍 Analyze Image", variant="primary")
-        
-        with gr.Column():
-            results_output = gr.Label(
-                label="Detection Results",
-                num_top_classes=2
+            analyze_btn = gr.Button(
+                "🔍 Analyze with Jerry",
+                variant="primary",
+                size="lg"
             )
-            verdict_output = gr.Textbox(
-                label="Verdict",
-                interactive=False
+            
+        with gr.Column(scale=1):
+            output_text = gr.Markdown(
+                label="Detection Results",
+                value="Upload an image and click 'Analyze with Jerry' to begin!"
             )
     
-    # Set up the analysis function
-    analyze_btn.click(
-        fn=predict_deepfake,
-        inputs=image_input,
-        outputs=[results_output, verdict_output]
-    )
-    
     gr.Markdown(
         """
         ---
-        **How it works:**
-        - Upload any facial image
-        - Jerry analyzes it and give appropriate verdict
-        - Returns confidence scores for "Deepfake" vs "Realism"
+        ### 💡 Tips for Best Results:
+        - Upload clear, high-quality images
+        - Works best with photos of faces or people
+        - Supports common image formats (JPG, PNG, etc.)
         
-        **Note:** The model loads only once when first used, then runs quickly for subsequent predictions!
+        ### ⚠️ Important Note:
+        Jerry provides analysis based on AI detection patterns. Results should be used as guidance, not absolute proof.
         """
     )
+    
+    # Connect the button to the function
+    analyze_btn.click(
+        fn=detect_deepfake,
+        inputs=image_input,
+        outputs=output_text
+    )
+    
+    # Also allow Enter key or automatic analysis
+    image_input.change(
+        fn=detect_deepfake,
+        inputs=image_input,
+        outputs=output_text
+    )
 
 # Launch the app
 if __name__ == "__main__":
-    print("🚀 Starting Jerry - Deepfake Detection App...")
-    print("Model will load on first prediction...")
-    
     demo.launch(
-        share=False,
-        server_name="127.0.0.1",  # Changed from 0.0.0.0 to localhost
+        server_name="0.0.0.0",
         server_port=7860,
-        show_error=True
+        share=False
     )