| import gradio as gr | |
| from fastai.learner import load_learner # Assuming FastAI is used | |
| # Define a custom function named 'is_cat' (replace with your actual function) | |
| def is_cat(x): | |
| # Your actual function logic here | |
| return x[0].isupper() | |
| # Load your pre-trained FastAI model (replace 'model.pkl' with your actual model path) | |
| model = load_learner('model.pkl') | |
| def classify_image(image): | |
| # Use your FastAI model to make a prediction | |
| pred_class, pred_idx, outputs = model.predict(image) | |
| return f"This image is {str(pred_class)} with confidence {outputs[pred_idx]:.2f}" | |
| # Update the Gradio interface | |
| iface = gr.Interface(fn=classify_image, inputs=gr.inputs.Image(shape=(224, 224)), outputs='text') | |
| iface.launch() | |