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| # Bismillahir Rahmaanir Raheem | |
| # Almadadh Ya Gause Radi Allahu Ta'alah Anh - Ameen | |
| from fastai.vision.all import * | |
| import gradio as gr | |
| def is_cat(x): | |
| return x[0].isupper() | |
| # load the trained fast ai model for predictions | |
| learn = load_learner('model.pkl') | |
| # define the function to call | |
| categories = ('Dog', 'Cat') | |
| def predict(img): | |
| pred, idx, probs = learn.predict(img) | |
| return dict(zip(categories, map(float, probs))) | |
| title = "CAT PREDICTOR" | |
| description = "A cat predictor model trained on the Pets dataset with fastai. Created as a demo for Gradio and Hugging Face spaces." | |
| article = "<p style='text-align: center'><span style='font-size: 15pt;'>CAT_PREDICTOR. 2022. </span></p>" | |
| image = gr.inputs.Image(shape=(192, 192)) | |
| label = gr.outputs.Label() | |
| examples = ['chapter1_cat_example.jpg'] | |
| interpretation = 'default' | |
| enable_queue = True | |
| iface = gr.Interface( | |
| fn=predict, | |
| title=title, | |
| description=description, | |
| article=article, | |
| inputs=image, | |
| outputs=label, | |
| #theme="grass", | |
| examples=examples, | |
| interpretation=interpretation, | |
| enable_queue=enable_queue | |
| ) | |
| iface.launch(inline=False) | |