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import gradio as gr |
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from matplotlib.pyplot import title |
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import numpy as np |
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import tensorflow as tf |
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import random |
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from tensorflow import keras |
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import json |
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import requests |
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def get_rest_url(model_name, host='127.0.0.1', port='8501', verb='predict'): |
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url = 'http://{0}:{1}/v1/models/{2}:predict'.format(host, port, model_name) |
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return url |
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def rest_request(data, url): |
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payload = json.dumps({'instances': data.tolist()}) |
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response = requests.post(url=url, data=payload) |
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return response |
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damage_types = np.array(sorted(['disaster happened', 'no disaster happened'])) |
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disaster_types = np.array(sorted(['volcano', 'flooding', 'earthquake', 'fire', 'wind', 'tsunami'])) |
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damage_levels = np.array(['no damage', 'minor damage', 'major damage', 'destroyed']) |
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def damage_classification(img): |
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image = np.zeros((1, 1024, 1024, 3), dtype=np.uint8) |
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image[0] = img |
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results = json.loads(rest_request(image, get_rest_url(model_name='binary-damage-classification-model', host='54.89.217.229')).content) |
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prediction = results['predictions'][0] |
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return {damage_types[i]: prediction[i] for i in range(len(damage_types))} |
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def disaster_classification(img): |
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image = np.zeros((1, 1024, 1024, 3), dtype=np.uint8) |
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image[0] = img |
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results = json.loads(rest_request(image, get_rest_url(model_name='disaster-classification-model', host='3.86.228.238')).content) |
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prediction = results['predictions'][0] |
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return {disaster_types[i]: prediction[i] for i in range(len(disaster_types))} |
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def regional_damage_classification(img): |
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image = np.zeros((1, 1024, 1024, 3), dtype=np.uint8) |
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image[0] = img |
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results = json.loads(rest_request(image, get_rest_url(model_name='regional-damage-classification-model', host='54.145.173.193')).content) |
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prediction = results['predictions'][0] |
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return {damage_levels[i]: prediction[i] for i in range(len(damage_levels))} |
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iface = gr.Interface( |
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fn = [damage_classification, disaster_classification, regional_damage_classification], |
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inputs = gr.inputs.Image(shape=(1024, 1024), image_mode='RGB', invert_colors=False, source="upload", type='numpy'), |
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outputs = gr.outputs.Label(), |
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allow_screenshot=True, |
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allow_flagging='never', |
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examples=[ |
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'./sample_images/hurricane.png', |
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'./sample_images/volcano.png', |
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'./sample_images/wildfire.png', |
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'./sample_images/earthquake.png', |
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'./sample_images/tsunami.png', |
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], |
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title="Soteria - AI for Natural Disaster Response", |
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description=""" |
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Check out our project @ https://github.com/Soteria-ai/Soteria for more explantation! Demo below takes ~15 seconds to get the results. |
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""", |
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theme="grass", |
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) |
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iface.launch(share=False, show_error=True, inline=True, debug=True) |
