app.py

import gradio as gr
import torch
from PIL import Image
from torchvision import models, transforms

# Load the trained object detection model (make sure to change this to your model path)
model = torch.load("path/to/your/best_model.pt")
model.eval()

# Define a function to run inference on an uploaded image
def detect_objects(image):
    # Transform the input image to match the model input size and format
    transform = transforms.Compose([transforms.ToTensor()])
    image_tensor = transform(image).unsqueeze(0)  # Add batch dimension

    with torch.no_grad():
        predictions = model(image_tensor)  # Run inference

    # Process and return the bounding boxes and labels (you may need to adjust based on your model)
    return predictions

# Define the Gradio interface
interface = gr.Interface(
    fn=detect_objects, 
    inputs=gr.inputs.Image(type="pil"), 
    outputs="json", 
    live=True,
    description="Object Detection for Lesions vs Non-Lesions"
)

# Launch the interface in the browser
interface.launch()