app.py

import gradio as gr
import torch
import cv2
import numpy as np
from PIL import Image

# Load the YOLOv8 model
model = torch.hub.load('ultralytics/yolov8', 'custom', path='best.pt')  # YOLOv8 specific

def process_image(image):
    # Convert PIL image to numpy array if necessary
    if isinstance(image, Image.Image):
        image = np.array(image)

    # Perform detection
    results = model(image)
    
    # Render results
    annotated_image = results.render()[0]
    return Image.fromarray(annotated_image)

def process_video(video_path):
    cap = cv2.VideoCapture(video_path)
    frames = []

    while(cap.isOpened()):
        ret, frame = cap.read()
        if not ret:
            break
        
        # Perform detection
        results = model(frame)
        
        # Render results
        annotated_frame = results.render()[0]
        frames.append(annotated_frame)

    cap.release()
    
    # Convert frames back to a video format
    height, width, layers = frames[0].shape
    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
    video_out = cv2.VideoWriter('output.mp4', fourcc, 30, (width, height))

    for frame in frames:
        video_out.write(frame)
    
    video_out.release()
    
    return 'output.mp4'

# Create Gradio interface
image_input = gr.inputs.Image(type="pil", label="Upload an image")
video_input = gr.inputs.Video(type="mp4", label="Upload a video")

image_output = gr.outputs.Image(type="pil", label="Detected image")
video_output = gr.outputs.Video(type="mp4", label="Detected video")

iface = gr.Interface(fn={'image': process_image, 'video': process_video},
                     inputs=[image_input, video_input],
                     outputs=[image_output, video_output],
                     title="YOLOv8 Object Detection",
                     description="Upload an image or video to detect objects using YOLOv8.")

if __name__ == "__main__":
    iface.launch()