V0

app.py

@@ -434,12 +434,7 @@ def process_image(input_image, damage_model_path, deepfake_model_path, deepfake_
     progress_info = []
     
     # Debug: log input parameters
-    progress_info.append(f"Processing with:")
-    progress_info.append(f"- Damage model: {damage_model_path}")
-    progress_info.append(f"- Deepfake model: {deepfake_model_path}")
-    progress_info.append(f"- Config: {deepfake_cfg_path}")
-    progress_info.append(f"- Thresholds: Damage={damage_threshold}, Deepfake={deepfake_threshold}")
-    
+     
     # Check model files
     if not skip_damage and damage_model_path:
         if not os.path.exists(damage_model_path):
@@ -469,11 +464,9 @@ def process_image(input_image, damage_model_path, deepfake_model_path, deepfake_
     except Exception as e:
         return None, f"Error loading image: {str(e)}"
     
-    progress_info.append("Image loaded successfully")
     
     # Setup device
     device = setup_device(device_str)
-    progress_info.append(f"Using device: {device}")
     
     # Initialize models
     damage_detector = None
@@ -482,41 +475,22 @@ def process_image(input_image, damage_model_path, deepfake_model_path, deepfake_
     
     # Setup damage detector
     if not skip_damage and damage_model_path:
-        progress_info.append("Setting up damage detector...")
         damage_detector, _ = setup_damage_detector(damage_model_path, float(damage_threshold))
-        if damage_detector is None:
-            progress_info.append("Warning: Failed to initialize damage detector")
-        else:
-            progress_info.append("Damage detector ready")
-    
+        
     # Setup deepfake detector
     if deepfake_model_path and deepfake_cfg_path:
-        progress_info.append("Setting up deepfake detector...")
         deepfake_model, deepfake_cfg = load_deepfake_model(deepfake_model_path, deepfake_cfg_path, device)
-        if deepfake_model is None:
-            progress_info.append("Warning: Failed to initialize deepfake detector")
-        else:
-            progress_info.append("Deepfake detector ready")
-    
-    # Check if we have at least one working detector
-    if damage_detector is None and deepfake_model is None:
-        return None, "\n".join(progress_info) + "\nError: No working detectors available"
-    
+       
+     
     # Step 1: Detect damage
-    progress_info.append("Detecting damage regions...")
     start_time = time.time()
     img, damage_outputs, damage_regions = detect_damage(img, damage_detector)
     damage_time = time.time() - start_time
     
-    if damage_regions:
-        progress_info.append(f"Found {len(damage_regions)} damage regions in {damage_time:.2f}s")
-    else:
-        progress_info.append("No damage regions found, analyzing whole image")
-    
+     
     # Step 2: Check for deepfakes
     deepfake_results = []
     if deepfake_model is not None:
-        progress_info.append("Checking for deepfakes...")
         start_time = time.time()
         deepfake_results = check_deepfake(
             img, damage_regions, deepfake_model, deepfake_cfg, device, float(deepfake_threshold)
@@ -524,7 +498,6 @@ def process_image(input_image, damage_model_path, deepfake_model_path, deepfake_
         deepfake_time = time.time() - start_time
         
         if deepfake_results:
-            progress_info.append(f"Deepfake analysis completed in {deepfake_time:.2f}s")
             
             # Generate report
             for result in deepfake_results:
@@ -532,23 +505,24 @@ def process_image(input_image, damage_model_path, deepfake_model_path, deepfake_
                     region_id = result["region_id"]
                     fake_prob = result["deepfake_prob"]
                     is_fake = result["is_fake"]
-                    progress_info.append(f"Region {region_id}: {'⚠️ FAKE' if is_fake else '✅ REAL'} ({fake_prob*100:.2f}%)")
                 elif "region" in result and result["region"] == "full_image":
                     fake_prob = result["deepfake_prob"]
                     is_fake = result["is_fake"]
-                    progress_info.append(f"Whole image: {'⚠️ FAKE' if is_fake else '✅ REAL'} ({fake_prob*100:.2f}%)")
         else:
             progress_info.append("No deepfake detection results")
     
+    fake_regions = [r for r in deepfake_results if r.get("is_fake", False)]
+    if fake_regions:
+        progress_info.append("\n🚨 VERDICT: This image contains FAKE damage 🚨")
+    else:
+        progress_info.append("\n✅ VERDICT: All damage appears REAL")
     # Step 3: Visualize results
-    progress_info.append("Generating visualization...")
     result_img = visualize_results(img, damage_outputs, deepfake_results, float(damage_threshold))
     
     # Convert back to RGB for Gradio
     if len(result_img.shape) == 3 and result_img.shape[2] == 3:
         result_img = cv2.cvtColor(result_img, cv2.COLOR_BGR2RGB)
     
-    progress_info.append("✅ Analysis complete!")
     
     return result_img, "\n".join(progress_info)