app.py

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

# Device configuration
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

# Load YOLOv5s model
model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True).to(device)

# Performance optimizations
model.conf = 0.5  # Confidence threshold (adjust for speed/accuracy balance)
if device.type == 'cuda':
    model.half()  # FP16 precision

def process_frame(image):
    """Process single frame with error handling"""
    if image is None:
        return None
    
    try:
        # Convert numpy array to PIL Image
        image_pil = Image.fromarray(image)
        
        # Perform inference
        with torch.no_grad():
            results = model(image_pil)
        
        # Render results
        rendered_images = results.render()
        return np.array(rendered_images[0]) if rendered_images else 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")
    gr.Markdown("Supports live webcam streaming and image uploads")
    
    with gr.Tabs():
        with gr.TabItem("📷 Live Camera"):
            with gr.Row():
                webcam_input = gr.Video(label="Live Feed", streaming=True)
                live_output = gr.Image(label="Processed Feed", streaming=True)
            webcam_input.change(process_frame, webcam_input, live_output)
            
        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)
            
    gr.Markdown("Performance Settings")
    with gr.Accordion("Advanced Settings", open=False):
        gr.Slider(minimum=0.1, maximum=0.9, value=0.5, 
                 label="Confidence Threshold", interactive=True)
        gr.Checkbox(label="Enable FP16 Acceleration", value=True)

# Configure queue and launch
app.queue().launch(  
    server_name="0.0.0.0",
    server_port=7860,
    share=False,
    enable_queue=True
)