V1.1

app.py

@@ -26,11 +26,9 @@ import gradio as gr
 from PIL import Image
 from torchvision import transforms
 
-
-damage_model_path =  "./output/model_final.pth"
-deepfake_model_path =  "./output/PoseEfficientNet_custom_laanet_model_final.pth"
-deepfake_cfg_path =  "./configs/detector2.yaml"
-
+DEFAULT_DAMAGE_MODEL_PATH = "./model_final.pth"  # Replace with your actual path
+DEFAULT_DEEPFAKE_MODEL_PATH = "./PoseEfficientNet_custom_laanet_model_final.pth"  # Replace with your actual path  
+DEFAULT_DEEPFAKE_CFG_PATH = "./configs/detector2.yaml"  # Replace with your actual path
 
 
 # Add current directory to path
@@ -511,11 +509,80 @@ def process_image(input_image, damage_model_path, deepfake_model_path, deepfake_
     
     return result_img, "\n".join(progress_info)
 
-def create_gradio_interface():
+
+    def create_gradio_interface():
     with gr.Blocks(title="Car Damage & Deepfake Detection") as app:
         gr.Markdown("# Car Damage Detection & Deepfake Verification")
         gr.Markdown("Upload an image to detect car damage and check if it's a deepfake")
         
+        with gr.Tab("Basic Interface"):
+            with gr.Row():
+                with gr.Column(scale=1):
+                    input_image = gr.Image(type="numpy", label="Input Image")
+                    
+                    # Simple controls
+                    skip_damage = gr.Checkbox(label="Skip Damage Detection", value=False)
+                    damage_threshold = gr.Slider(minimum=0.1, maximum=1.0, value=0.7, step=0.05, 
+                                               label="Damage Detection Threshold")
+                    deepfake_threshold = gr.Slider(minimum=0.1, maximum=1.0, value=0.5, step=0.05,
+                                                 label="Deepfake Detection Threshold")
+                    device = gr.Dropdown(choices=["auto", "cuda", "cpu", "mps"], value="auto", 
+                                        label="Computation Device")
+                    
+                    process_btn = gr.Button("Process Image", variant="primary")
+                    
+                with gr.Column(scale=1):
+                    output_image = gr.Image(type="numpy", label="Result")
+                    output_text = gr.Textbox(label="Detection Results", lines=10)
+            
+        with gr.Tab("Advanced Settings"):
+            with gr.Row():
+                with gr.Column():
+                    # Create empty textboxes - values will be provided by the wrapper function
+                    damage_model_path = gr.Textbox(label="Damage Model Path (optional)", 
+                                                 placeholder="Leave blank to use default model")
+                    deepfake_model_path = gr.Textbox(label="Deepfake Model Path (optional)", 
+                                                   placeholder="Leave blank to use default model")
+                    deepfake_cfg_path = gr.Textbox(label="Deepfake Config Path (optional)", 
+                                                 placeholder="Leave blank to use default config")
+        
+        # Create a wrapper function that applies default values when fields are empty
+        def process_with_defaults(input_image, damage_path, deepfake_path, cfg_path, 
+                                damage_threshold, deepfake_threshold, skip_damage, device):
+            # Use default paths if none provided
+            actual_damage_path = damage_path if damage_path.strip() else DEFAULT_DAMAGE_MODEL_PATH
+            actual_deepfake_path = deepfake_path if deepfake_path.strip() else DEFAULT_DEEPFAKE_MODEL_PATH
+            actual_cfg_path = cfg_path if cfg_path.strip() else DEFAULT_DEEPFAKE_CFG_PATH
+            
+            # Call the original process function with the actual paths
+            return process_image(
+                input_image, actual_damage_path, actual_deepfake_path, actual_cfg_path,
+                damage_threshold, deepfake_threshold, skip_damage, device
+            )
+        
+        # Connect the wrapper function
+        process_btn.click(
+            fn=process_with_defaults,
+            inputs=[
+                input_image, 
+                damage_model_path,
+                deepfake_model_path,
+                deepfake_cfg_path,
+                damage_threshold,
+                deepfake_threshold,
+                skip_damage,
+                device
+            ],
+            outputs=[output_image, output_text]
+        )
+        
+        # Examples
+        gr.Markdown("## Examples")
+        gr.Markdown("Note: Examples will use the default models if you don't specify custom paths.")
+            
+    return app        gr.Markdown("# Car Damage Detection & Deepfake Verification")
+        gr.Markdown("Upload an image to detect car damage and check if it's a deepfake")
+        
         with gr.Tab("Basic Interface"):
             with gr.Row():
                 with gr.Column(scale=1):