Spaces:
Sleeping
Sleeping
| # AUTOGENERATED! DO NOT EDIT! File to edit: . (unless otherwise specified). | |
| __all__ = ['learn', 'classify_image', 'categories', 'title', 'description', 'image', 'label', 'jpg_files', 'examples', | |
| 'intf'] | |
| # Cell | |
| from fastai.vision.all import * | |
| import gradio as gr | |
| # Cell | |
| learn = load_learner('tower_parts_model.pkl') | |
| # Cell | |
| categories = learn.dls.vocab | |
| def classify_image(img): | |
| pred, idx, probs = learn.predict(img) | |
| return dict(zip(categories, map(float,probs))) | |
| # Cell | |
| title = "Classify Telecommunication Tower Parts" | |
| description = "This deep learning model was trained with fastai using only 478 images of telecommunication towers parts." | |
| description += "\nThe model was trained to recognize the following 8 categories:" | |
| description += "\n- Base plate | Grounding bar | Identification | Ladder | Light | Lightning rod | Platform | Transmission lines" | |
| description += "\n- You can test the model with the given examples (see below), or upload your own pictures." | |
| # Cell | |
| image = gr.inputs.Image( | |
| shape=(200,200), | |
| label='Load image' | |
| ) | |
| label = gr.outputs.Label() | |
| #examples = ['DSC01955.jpg', 'DSC01956.jpg'] | |
| jpg_files = os.listdir() | |
| examples = [file for file in jpg_files if file.endswith('.jpg')] | |
| # Cell | |
| intf = gr.Interface( | |
| fn=classify_image, | |
| inputs=image, | |
| outputs=label, | |
| examples=examples, | |
| title=title, | |
| description=description, | |
| ) | |
| intf.launch(inline=False) |