app.py

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)