| import gradio as gr | |
| import model | |
| from config import app_config | |
| def init(): | |
| if model != None: | |
| print("Initializing App...") | |
| app_config.model = model.load_model() | |
| def clear(): | |
| return None, 2, None, None, None | |
| def create_interface(): | |
| md = """ | |
| # Famous Landmark Classifier using CNN | |
| **Choose an image containing any of the `50 possible classes` of world famous landmarks,** | |
| **choose the number of prediction required (k) and hit `Predict`, model will try to identify** | |
| **the landmark in the image.** | |
| **Please note that the model is trained on a small set of only 4,000 images hence it may not** | |
| **be right all the time, but its fun to try out.** | |
| Visit the [project's repo](https://github.com/sssingh/landmark-classification-tagging) | |
| """ | |
| with gr.Blocks( | |
| title=app_config.title, theme=app_config.theme, css=app_config.css | |
| ) as app: | |
| with gr.Row(): | |
| gr.Markdown(md) | |
| with gr.Accordion( | |
| "Expand to see 50 classes:", open=False, elem_classes="accordion" | |
| ): | |
| gr.JSON(app_config.classes, elem_classes="json-box") | |
| with gr.Row(): | |
| with gr.Column(): | |
| img = gr.Image(type="pil", elem_classes="image-picker") | |
| k = gr.Slider( | |
| label="Number of predictions (k):", | |
| minimum=2, | |
| maximum=5, | |
| value=2, | |
| step=1, | |
| elem_classes="slider", | |
| ) | |
| with gr.Row(): | |
| submit_btn = gr.Button( | |
| "Predict", | |
| icon="assets/button-icon.png", | |
| elem_classes="submit-button", | |
| ) | |
| clear_btn = gr.ClearButton(elem_classes="clear-button") | |
| with gr.Column(): | |
| landmarks = gr.JSON( | |
| label="Predicted Landmarks:", elem_classes="json-box" | |
| ) | |
| proba = gr.JSON( | |
| label="Predicted Probabilities:", elem_classes="json-box" | |
| ) | |
| plot = gr.Plot(container=True, elem_classes="plot") | |
| with gr.Row(): | |
| with gr.Accordion( | |
| "Expand for examples:", open=False, elem_classes="accordion" | |
| ): | |
| gr.Examples( | |
| examples=[ | |
| ["assets/examples/gateway-of-india.jpg", 3], | |
| ["assets/examples/grand-canyon.jpg", 2], | |
| ["assets/examples/opera-house.jpg", 3], | |
| ["assets/examples/stone-henge.jpg", 4], | |
| ["assets/examples/temple-of-zeus.jpg", 5], | |
| ], | |
| inputs=[img, k], | |
| outputs=[landmarks, proba], | |
| elem_id="examples", | |
| ) | |
| submit_btn.click( | |
| fn=model.predict, inputs=[img, k], outputs=[landmarks, proba, plot] | |
| ) | |
| clear_btn.click(fn=clear, inputs=[], outputs=[img, k, landmarks, proba, plot]) | |
| img.clear(fn=clear, inputs=[], outputs=[img, k, landmarks, proba, plot]) | |
| return app | |
| if __name__ == "__main__": | |
| init() | |
| app = create_interface() | |
| app.launch() | |