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| from fastai.vision.all import * | |
| import gradio as gr | |
| import logging | |
| EXAMPLES_PATH = Path('./examples') | |
| learn = load_learner('watersports.pkl') | |
| categories = learn.dls.vocab | |
| def classify_image(img): | |
| logging.warning('Watch out!!!') # will print a message to the console | |
| print('Classifying: ', img) | |
| pred,idx,probs = learn.predict(img) | |
| return( dict(zip(categories, map(float,probs)))) | |
| interface_options = { | |
| "title": "Which Watersport? Compare your guess to the machine inference.", | |
| "description": "Click an image. Click Submit. Click Clear. Other images can be dragged directly from a google search. \nNote, the machine classifier relies purely on vision, of the image pixels - no other info used.", | |
| "examples": [f'{EXAMPLES_PATH}/{f.name}' for f in EXAMPLES_PATH.iterdir()], | |
| "examples_per_page": "100" | |
| } | |
| iface = gr.Interface(fn=classify_image, | |
| inputs=gr.inputs.Image(shape=(192,192)), | |
| outputs=gr.outputs.Label(), | |
| **interface_options) | |
| iface.launch(inline=False) | |