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Build error
| import gradio as gr | |
| from fastai.vision.all import * | |
| from fastcore.all import * | |
| from fastdownload import download_url | |
| from PIL import Image | |
| learn = load_learner('guitar_classifier_model.pkl') | |
| categories = ('acoustic', 'flying v', 'jazzmaster', 'lespaul', 'sg', 'stratocaster', 'telecaster') | |
| def predict(image): | |
| # Ensure the image is a PIL image (if not already) | |
| if not isinstance(image, Image.Image): | |
| image = Image.fromarray(image) | |
| # Resize the image to (192, 192) | |
| image = image.resize((192, 192)) | |
| # Predict | |
| pred, idx, probs = learn.predict(image) | |
| return dict(zip(categories, map(float, probs))) | |
| # Remove the 'shape' parameter from gr.Image | |
| image_component = gr.Image(type="pil") | |
| label_component = gr.Label() | |
| # Example images (ensure these files are available) | |
| examples = ["acoustic.jpg", "fly.jpeg", "lespaul.jpeg", "sg.jpg", "strat.jpg", "telecaster.jpg"] | |
| iface = gr.Interface( | |
| fn=predict, | |
| inputs=image_component, | |
| outputs=label_component, | |
| examples=examples, | |
| title="Guitar Classifier", | |
| description="Upload a guitar image to get a prediction.", | |
| ) | |
| iface.launch() | |