| import gradio as gr | |
| from fastai.vision.all import * | |
| from fastai.vision.all import PILImage | |
| # Load the trained model | |
| learn = load_learner('levit.pkl') | |
| # Get the labels from the data loaders | |
| labels = learn.dls.vocab | |
| # Define the prediction function | |
| def predict(img): | |
| img = PILImage.create(img) | |
| img = img.resize((512, 512)) | |
| pred, pred_idx, probs = learn.predict(img) | |
| return {labels[i]: float(probs[i]) for i in range(len(labels))} | |
| # Example images for demonstration | |
| examples = ['image.jpg'] | |
| # Create the Gradio interface | |
| interface = gr.Interface( | |
| fn=predict, | |
| inputs=gr.Image(), | |
| outputs=gr.Label(num_top_classes=3) | |
| ) | |
| # Enable the queue to handle POST requests | |
| interface.queue(api_open=True) | |
| # Launch the interface | |
| interface.launch() |