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| import gradio as gr | |
| from matplotlib.pyplot import title | |
| import numpy as np | |
| import tensorflow as tf | |
| import random | |
| from tensorflow import keras | |
| damage_types = np.array(sorted(['disaster happened', 'no disaster happened'])) | |
| disaster_types = np.array(sorted(['volcano', 'flooding', 'earthquake', 'fire', 'wind', 'tsunami'])) | |
| # model = keras.models.load_model('./models/disaster-classification') | |
| def damage_classification(img): | |
| prediction = np.random.rand(1, 2)[0] | |
| return {damage_types[i]: prediction[i] for i in range(len(damage_types))} | |
| def disaster_classification(img): | |
| image = np.zeros((1, 1024, 1024, 3), dtype=np.uint8) | |
| image[0] = img | |
| # prediction = model.predict(image).tolist()[0] | |
| prediction = np.random.rand(1, 6)[0] | |
| return {disaster_types[i]: prediction[i] for i in range(len(disaster_types))} | |
| iface = gr.Interface( | |
| fn = [damage_classification, disaster_classification], | |
| inputs = gr.inputs.Image(shape=(1024, 1024), image_mode='RGB', invert_colors=False, source="upload", type='numpy'), | |
| outputs = gr.outputs.Label(), | |
| allow_screenshot=True, | |
| allow_flagging='never', | |
| examples=[ | |
| './sample_images/hurricane.png', | |
| './sample_images/volcano.png', | |
| './sample_images/wildfire.png' | |
| ], | |
| thumbnail='./soteria-logo.png', | |
| title="Soteria - AI for Natural Disaster Response", | |
| description=""" | |
| Check out our project @ https://github.com/Soteria-ai/Soteria, see below for more explantation! | |
| """, | |
| theme="grass", | |
| article=""" | |
| Explantation | |
| Model #1 - Damage Classification: weather a diasater has happened or not | |
| Model #2 - Disaster Classificaiton: what type of disaster happened? | |
| """ | |
| ) | |
| iface.launch(share=False, show_error=True, inline=True, debug=True) |