| __all__ = ['learn', 'classify_image', 'categories', 'image', 'label', 'examples', 'intf'] | |
| from fastai.vision.all import * | |
| import gradio as gr | |
| learn = load_learner('architecturemodel.pkl') | |
| categories = ('Achaemenid architecture', | |
| 'American Foursquare architecture', | |
| 'American craftsman style', | |
| 'Ancient Egyptian architecture', | |
| 'Art Deco architecture', | |
| 'Art Nouveau architecture', | |
| 'Baroque architecture', | |
| 'Bauhaus architecture', | |
| 'Beaux-Arts architecture', | |
| 'Byzantine architecture', | |
| 'Chicago school architecture', | |
| 'Colonial architecture', | |
| 'Deconstructivism', | |
| 'Edwardian architecture', | |
| 'Georgian architecture', | |
| 'Gothic architecture', | |
| 'Greek Revival architecture', | |
| 'International style', | |
| 'Novelty architecture', | |
| 'Palladian architecture', | |
| 'Postmodern architecture', | |
| 'Queen Anne architecture', | |
| 'Romanesque architecture', | |
| 'Russian Revival architecture', | |
| 'Tudor Revival architecture') | |
| def classify_image(img): | |
| pred,idx,probs = learn.predict(img) | |
| return dict(zip(categories, map(float,probs))) | |
| image = gr.inputs.Image(shape=(192, 192)) | |
| label = gr.outputs.Label() | |
| examples = ['bigben.jpeg','pyramid.jpeg','robiehouse.jpeg'] | |
| intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples) | |
| intf.launch(inline=False) |