Update app.py

app.py

@@ -1,44 +1,43 @@
-from ultralytics import YOLO
-import cv2
-import torch
-import gradio as gr
-from PIL import Image
-import numpy as np
-
-# Load fine-tuned YOLOv8 model for car damage detection
-model = YOLO("best.pt")  # Replace with your trained model file
-
-def predict(input_img):
-    # Convert PIL image to OpenCV format
-    image = np.array(input_img)
-    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
-    
-    # Run inference
-    results = model(image)
-    
-    # Draw bounding boxes
-    for result in results:
-        for box in result.boxes:
-            x1, y1, x2, y2 = map(int, box.xyxy[0])
-            conf = float(box.conf[0])
-            label = f"Damage: {conf:.2f}"
-            
-            # Draw red bounding box
-            cv2.rectangle(image, (x1, y1), (x2, y2), (0, 0, 255), 3)  # Red color (BGR: 0,0,255)
-            cv2.putText(image, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
-    
-    # Convert back to PIL format
-    output_img = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
-    return output_img
-
-# Gradio interface
-gradio_app = gr.Interface(
-    fn=predict,
-    inputs=gr.Image(label="Upload a car image", sources=['upload', 'webcam'], type="pil"),
-    outputs=gr.Image(label="Detected Damage"),
-    title="Car Damage Detection",
-    description="Upload an image of a car, and the model will detect and highlight damaged areas."
-)
-
-if __name__ == "__main__":
-    gradio_app.launch()
+from ultralytics import YOLO
+import cv2
+import torch
+import gradio as gr
+from PIL import Image
+import numpy as np
+
+# Load fine-tuned YOLOv8 model for car damage detection
+model = YOLO("best.pt")  # Replace with your trained model file
+
+def predict(input_img):
+    # Convert PIL image to OpenCV format
+    image = np.array(input_img)
+    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+    
+    # Run inference
+    results = model(image)
+    
+    # Draw bounding boxes
+    for result in results:
+        for box in result.boxes:
+            x1, y1, x2, y2 = map(int, box.xyxy[0])
+            conf = float(box.conf[0])
+            label = f"Damage: {conf:.2f}"
+            
+            # Draw red bounding box
+            cv2.rectangle(image, (x1, y1), (x2, y2), (0, 0, 255), 3)  # Red color (BGR: 0,0,255)
+            cv2.putText(image, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
+    
+    # Convert back to PIL format
+    output_img = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
+    return output_img
+
+# Gradio interface
+gradio_app = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(label="Upload a car image", sources=['upload', 'webcam'], type="pil"),
+    outputs=gr.Image(label="Detected Damage"),
+    title="Car Damage Detection",
+    description="Upload an image of a car, and the model will detect and highlight damaged areas."
+)
+
+gradio_app.launch()