init

app.py

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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(['yes', 'no']))
+disaster_types = np.array(sorted(['volcano', 'flooding', 'earthquake', 'fire', 'wind', 'tsunami']))
+# model = keras.models.load_model('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)

requirements.txt

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+numpy
+tensorflow
+random