Create app.py

app.py

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+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()