app.py

import gradio as gr
import torch
from PIL import Image
import requests
from io import BytesIO

# Load YOLOv5 pre-trained model from Hugging Face
model = torch.hub.load('ultralytics/yolov5', 'yolov5s')  # You can choose other versions like yolov5m or yolov5l

# Function for object detection
def detect_objects(input_image):
    # If the input is a URL, download the image
    if isinstance(input_image, str):
        response = requests.get(input_image)
        img = Image.open(BytesIO(response.content))
    else:
        img = Image.fromarray(input_image)
    
    # Run YOLOv5 object detection
    results = model(img)
    
    # Render results on image
    results.render()  # Render boxes on the image
    
    # Return image with detections
    output_image = results.imgs[0]
    return Image.fromarray(output_image)

# Create Gradio interface
interface = gr.Interface(
    fn=detect_objects, 
    inputs=gr.inputs.Image(type="numpy", label="Upload an image"), 
    outputs=gr.outputs.Image(type="pil", label="Detected Image"), 
    title="YOLOv5 Object Detection",
    description="Upload an image and detect objects using YOLOv5 model. The model can identify objects like people, cars, animals, and more.",
    theme="huggingface"
)

# Launch the interface
interface.launch()