app.py

import gradio as gr
import cv2
from ultralytics import YOLO
import tempfile
import os

# Load the YOLO model
model = YOLO("best.pt")  # Replace with the path to your model

# Define the inference function
def yolo_inference(input_file):
    # Check if the input is an image or a video
    if input_file.endswith((".jpg", ".jpeg", ".png")):
        # Process as an image
        img = cv2.imread(input_file)
        results = model(img)
        annotated_img = results[0].plot()

        # Display the annotated image in a window
        cv2.imshow("YOLO Detection", annotated_img)
        cv2.waitKey(0)
        cv2.destroyAllWindows()

        return input_file  # Return the original file for consistency (can be adjusted)

    elif input_file.endswith((".mp4", ".avi", ".mov")):
        # Process as a video
        cap = cv2.VideoCapture(input_file)
        fourcc = cv2.VideoWriter_fourcc(*'mp4v')
        fps = int(cap.get(cv2.CAP_PROP_FPS))
        width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
        height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
        
        # Create a temporary output video path
        temp_dir = tempfile.mkdtemp()
        output_video_path = os.path.join(temp_dir, "output.mp4")
        out = cv2.VideoWriter(output_video_path, fourcc, fps, (width, height))
        
        while cap.isOpened():
            ret, frame = cap.read()
            if not ret:
                break
            
            # Run YOLO on each frame
            results = model(frame)
            annotated_frame = results[0].plot()
            
            # Display the annotated frame in a window
            cv2.imshow("YOLO Detection", annotated_frame)
            if cv2.waitKey(1) & 0xFF == ord('q'):  # Press 'q' to quit early
                break
            
            # Save the annotated frame to the video
            out.write(annotated_frame)
        
        cap.release()
        out.release()
        cv2.destroyAllWindows()

        return input_file  # Return the original video file for consistency (can be adjusted)

    else:
        raise ValueError("Unsupported file format. Please upload an image or video.")

# Define the Gradio interface
interface = gr.Interface(
    fn=yolo_inference,
    inputs=gr.File(label="Upload an Image or Video"),
    outputs="text",  # Display a message about console output
    title="YOLO Object Detection",
    description="Upload an image or video for object detection. The results are displayed on the console."
)

# Launch the app
if __name__ == "__main__":
    interface.launch(share=True)