app.py

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

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True).to(device)

model.conf = 0.5
if device.type == 'cuda':
    model.half()

def process_frame(image):
    if image is None:
        print("No image received!")
        return None

    try:
        print("Processing frame...")
        image_pil = Image.fromarray(image)
        
        with torch.no_grad():
            results = model(image_pil)
        
        rendered_images = results.render()
        processed_image = np.array(rendered_images[0]) if rendered_images else image
        print("Frame processed successfully!")
        return processed_image
    
    except Exception as e:
        print(f"Processing error: {e}")
        return image

with gr.Blocks(title="Real-Time Object Detection") as app:
    gr.Markdown("# Real-Time Object Detection with Dual Input")
    
    with gr.Tabs():
        with gr.TabItem("📷 Live Camera"):
            with gr.Row():
                webcam_input = gr.Image(source="webcam", streaming=True, label="Live Feed")  # ✅ FIXED
                live_output = gr.Image(label="Processed Feed")
            webcam_input.stream(process_frame, inputs=webcam_input, outputs=live_output)  # ✅ FIXED

        with gr.TabItem("🖼️ Image Upload"):
            with gr.Row():
                upload_input = gr.Image(type="numpy", label="Upload Image")
                upload_output = gr.Image(label="Detection Result")
            upload_input.change(process_frame, upload_input, upload_output)

app.queue().launch(server_name="0.0.0.0", server_port=7860, share=False)