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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) |