V0.1

app.py

@@ -37,7 +37,7 @@ except ImportError:
 
 # Define model paths
 DEFAULT_DAMAGE_MODEL_PATH = "./model_final.pth"
-DEFAULT_DEEPFAKE_MODEL_PATH = "./vit_deepfake_best.pth"
+DEFAULT_DEEPFAKE_MODEL_PATH = "./vit_deepfake_best_20250422_222444.pth"
 
 # Sample images for demo (add your own paths)
 SAMPLE_IMAGES = [
@@ -45,6 +45,9 @@ SAMPLE_IMAGES = [
     "./test5.png",
 ]
 
+# Maximum number of tries allowed
+MAX_TRIES = 3
+
 def verify_detectron2_installation():
     """Verify that Detectron2 is properly installed"""
     results = {
@@ -384,9 +387,13 @@ def visualize_results(image, damage_outputs, deepfake_results, damage_threshold)
         return np.array(image, dtype=np.uint8)
 
 def process_image(input_image, damage_model_path, deepfake_model_path, damage_threshold, 
-                 deepfake_threshold, skip_damage, device_str):
+                 deepfake_threshold, skip_damage, device_str, usage_count):
     """Process an image through the detection pipeline"""
+    # Increment usage count and check if limit reached
+    usage_count = usage_count + 1
+    
     progress_info = []
+    progress_info.append(f"Usage: {usage_count}/{MAX_TRIES}")
     
     # Check model files
     if not skip_damage and damage_model_path:
@@ -407,12 +414,12 @@ def process_image(input_image, damage_model_path, deepfake_model_path, damage_th
             if len(img.shape) == 3 and img.shape[2] == 3:
                 img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
         else:
-            return None, "Error: Unsupported image format"
+            return None, "Error: Unsupported image format", usage_count
         
         if img is None:
-            return None, "Error: Could not read the image"
+            return None, "Error: Could not read the image", usage_count
     except Exception as e:
-        return None, f"Error loading image: {str(e)}"
+        return None, f"Error loading image: {str(e)}", usage_count
     
     # Setup device
     device = setup_device(device_str)
@@ -492,7 +499,23 @@ def process_image(input_image, damage_model_path, deepfake_model_path, damage_th
     if len(result_img.shape) == 3 and result_img.shape[2] == 3:
         result_img = cv2.cvtColor(result_img, cv2.COLOR_BGR2RGB)
     
-    return result_img, "\n".join(progress_info)
+    # Add usage information at the bottom of the image
+    if usage_count >= MAX_TRIES:
+        # Add a "Usage limit reached" message to the bottom of the image in red
+        cv2.putText(result_img, f"USAGE LIMIT REACHED: {usage_count}/{MAX_TRIES}", 
+                    (10, result_img.shape[0] - 20), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
+    else:
+        # Add a usage counter to the bottom of the image
+        cv2.putText(result_img, f"Usage: {usage_count}/{MAX_TRIES}", 
+                    (10, result_img.shape[0] - 20), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
+    
+    # Add usage info to the progress text
+    if usage_count >= MAX_TRIES:
+        progress_info.append("\n⚠️ You have reached the maximum number of tries allowed ⚠️")
+    else:
+        progress_info.append(f"\nRemaining tries: {MAX_TRIES - usage_count}")
+    
+    return result_img, "\n".join(progress_info), usage_count
 
 def auto_install_dependencies():
     """Attempt to install dependencies if needed"""
@@ -547,6 +570,9 @@ def create_gradio_interface():
         secondary_hue="orange",
     )
     
+    # Initialize usage counter
+    usage_counter = gr.State(0)
+    
     with gr.Blocks(title="Car Damage & Deepfake Detector", theme=theme) as app:
         gr.Markdown("""
         # 🚗 Car Damage Fraud Detector with Vision Transformer
@@ -556,6 +582,8 @@ def create_gradio_interface():
         2. Verify if the damage is real or artificially generated (deepfake)
         
         *This app uses a Vision Transformer (ViT) model for deepfake detection.*
+        
+        ⚠️ **Note: You have a maximum of 3 tries to analyze images.**
         """)
         
         # Main Interface Tab
@@ -568,6 +596,9 @@ def create_gradio_interface():
                         process_btn = gr.Button("Analyze Image", variant="primary")
                         clear_btn = gr.Button("Clear", variant="secondary")
                     
+                    # Usage limit display
+                    usage_display = gr.Markdown("**Usage: 0/3**")
+                    
                     with gr.Accordion("Advanced Settings", open=False):
                         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,