Upload 2 files

app.py

@@ -97,17 +97,27 @@ def detect_manipulation(image):
         
         heatmap = segmentation_map.cpu().detach().numpy()
     
-    # Create visualization
-    fig, ax = plt.subplots(figsize=(10, 10))
+    # Create visualization with exact size
+    # Calculate figure size to match image dimensions
+    dpi = 100
+    fig_height = original_size[1] / dpi
+    fig_width = original_size[0] / dpi
+    
+    fig = plt.figure(figsize=(fig_width, fig_height), dpi=dpi)
+    ax = fig.add_axes([0, 0, 1, 1])  # No margins
     ax.imshow(heatmap, cmap='jet')
     ax.axis('off')
-    plt.tight_layout(pad=0)
     
     # Convert to numpy array
     buf = io.BytesIO()
-    plt.savefig(buf, format='png', bbox_inches='tight', pad_inches=0, dpi=150)
+    plt.savefig(buf, format='png', bbox_inches='tight', pad_inches=0, dpi=dpi)
     buf.seek(0)
     result_image = Image.open(buf)
+    
+    # Ensure exact size match by resizing if needed
+    if result_image.size != original_size:
+        result_image = result_image.resize(original_size, Image.LANCZOS)
+    
     result_array = np.array(result_image)
     plt.close(fig)
     
@@ -126,25 +136,58 @@ custom_css = """
     background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
     -webkit-background-clip: text;
     -webkit-text-fill-color: transparent;
+    background-clip: text;
+}
+#subtitle {
+    text-align: center;
+    font-size: 1.2em;
+    color: #666;
+    margin-bottom: 1em;
+}
+#info {
+    background: #e8f4fd;
+    border-left: 4px solid #2196F3;
+    padding: 15px;
+    border-radius: 5px;
+    margin-bottom: 20px;
+    color: #1976D2;
 }
 """
 
-# Create interface using Gradio 3.x syntax
-demo = gr.Interface(
-    fn=detect_manipulation,
-    inputs=gr.Image(label="Upload Image"),
-    outputs=gr.Image(label="Manipulation Heatmap"),
-    title="🎯 RADAR - Image Manipulation Detection",
-    description="""
-    **ReliAble iDentification of inpainted AReas**
+# Create interface using Gradio 4.x Blocks
+with gr.Blocks(css=custom_css, title="RADAR - Image Manipulation Detection") as demo:
+    gr.HTML('<h1 id="title">🎯 RADAR</h1>')
+    gr.HTML('<p id="subtitle">ReliAble iDentification of inpainted AReas</p>')
+    
+    gr.HTML('''
+        <div id="info">
+            <strong>ℹ️ About RADAR:</strong> Upload an image to detect and localize regions 
+            that have been manipulated using diffusion-based inpainting models. 
+            The output shows a heatmap where red areas indicate detected manipulations.
+        </div>
+    ''')
+    
+    with gr.Row():
+        with gr.Column():
+            input_image = gr.Image(label="Upload Image", type="numpy")
+        with gr.Column():
+            output_image = gr.Image(label="Manipulation Heatmap", type="numpy")
+    
+    submit_btn = gr.Button("🔍 Detect Manipulations", variant="primary")
+    
+    # Connect the button
+    submit_btn.click(
+        fn=detect_manipulation,
+        inputs=input_image,
+        outputs=output_image
+    )
     
-    Upload an image to detect and localize regions that have been manipulated using 
-    diffusion-based inpainting models. The output shows a heatmap where red areas 
-    indicate detected manipulations.
-    """,
-    css=custom_css,
-    allow_flagging="never"
-)
+    # Also trigger on image upload
+    input_image.change(
+        fn=detect_manipulation,
+        inputs=input_image,
+        outputs=output_image
+    )
 
 # Launch
 if __name__ == "__main__":

requirements.txt

@@ -1,7 +1,8 @@
-gradio==3.50.2
-matplotlib
-numpy
-Pillow
+gradio==4.20.0
+gradio-client==0.10.1
 torch
 torchvision
-transformers
+transformers
+pillow
+matplotlib
+numpy