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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
import json
import requests
def get_rest_url(model_name, host='127.0.0.1', port='8501', verb='predict'):
url = 'http://{0}:{1}/v1/models/{2}:predict'.format(host, port, model_name)
return url
def rest_request(data, url):
payload = json.dumps({'instances': data.tolist()})
print('im here')
print(url)
response = requests.post(url=url, data=payload)
print('wooo')
return response
damage_types = np.array(sorted(['disaster happened', 'no disaster happened']))
disaster_types = np.array(sorted(['volcano', 'flooding', 'earthquake', 'fire', 'wind', 'tsunami']))
damage_levels = np.array(['no damage', 'minor damage', 'major damage', 'destroyed'])
def damage_classification(img):
# prediction = np.random.rand(1, 2)[0]
# return {damage_types[i]: prediction[i] for i in range(len(damage_types))}
image = np.zeros((1, 1024, 1024, 3), dtype=np.uint8)
image[0] = img
results = json.loads(rest_request(image, get_rest_url(model_name='binary-damage-classification-model', host='54.89.217.229')).content)
prediction = results['predictions'][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]
results = json.loads(rest_request(image, get_rest_url(model_name='disaster-classification-model', host='3.86.228.238')).content)
prediction = results['predictions'][0]
return {disaster_types[i]: prediction[i] for i in range(len(disaster_types))}
def regional_damage_classification(img):
image = np.zeros((1, 1024, 1024, 3), dtype=np.uint8)
image[0] = img
results = json.loads(rest_request(image, get_rest_url(model_name='regional-damage-classification-model', host='54.145.173.193')).content)
prediction = results['predictions'][0]
return {damage_levels[i]: prediction[i] for i in range(len(damage_levels))}
iface = gr.Interface(
fn = [damage_classification, disaster_classification, regional_damage_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',
'./sample_images/earthquake.png',
'./sample_images/tsunami.png',
],
title="Soteria - AI for Natural Disaster Response",
description="""
Check out our project @ https://github.com/Soteria-ai/Soteria for more explantation! Demo below should takes ~30 seconds to load, if error occurs try downloading the app under "ml-demo-app" on github and run locally with gradio!
""",
theme="grass",
enable_queue=True
)
iface.launch(share=False, show_error=True, inline=True, debug=True) |