app.py

app.py

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+import gradio as gr
+from PIL import Image
+import cv2
+from ultralytics import YOLO
+
+# Load model (ensure the path to the weights file is correct)
+model_path = "Car-Logos/train15/weights/best.pt"
+detection_model = YOLO(model_path)
+
+def predict_image(pil_image):
+    """Process an image and return the annotated image."""
+    # Convert PIL image to NumPy array
+    frame = cv2.cvtColor(np.array(pil_image), cv2.COLOR_RGB2BGR)
+    
+    # Run YOLO model
+    results = detection_model.predict(frame, conf=0.5, iou=0.6)
+    
+    # Annotate the image
+    annotated_frame = results[0].plot()
+    
+
+    out_pil_image = Image.fromarray(annotated_frame[..., ::-1])  
+    return out_pil_image
+
+def predict_video(video_path):
+    """Process a video and return the path to the annotated output video."""
+    cap = cv2.VideoCapture(video_path)
+    output_frames = []
+    
+ 
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret:
+            break
+        results = detection_model.predict(frame, conf=0.5, iou=0.6)
+        annotated_frame = results[0].plot()
+        output_frames.append(annotated_frame)
+    
+    cap.release()
+    
+ 
+    if output_frames:
+        height, width, _ = output_frames[0].shape
+        out_path = "output_video.mp4"
+        out = cv2.VideoWriter(out_path, cv2.VideoWriter_fourcc(*"mp4v"), 30, (width, height))
+        for frame in output_frames:
+            out.write(frame)
+        out.release()
+        return out_path
+    else:
+        return "No frames processed."
+
+def create_gradio_interface():
+    with gr.Blocks() as demo:
+        with gr.Tab("Upload and Honda or Toyota Logo image"):
+            gr.Markdown("### Upload Honda or Toyota  for Object Detection")
+            image_input = gr.Image(type="pil", label="Input Image")
+            image_output = gr.Image(type="pil", label="Annotated Image")
+            image_button = gr.Button("Process Image")
+            image_button.click(fn=predict_image, inputs=image_input, outputs=image_output)
+        
+        with gr.Tab("Video Upload"):
+            gr.Markdown("### Upload a Video for Object Detection")
+            video_input = gr.Video(label="Input Video")
+            video_output = gr.File(label="Annotated Video")
+            video_button = gr.Button("Process Video")
+            video_button.click(fn=predict_video, inputs=video_input, outputs=video_output)
+        
+        demo.launch()
+
+
+create_gradio_interface()