init

.gitattributes

@@ -25,3 +25,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zstandard filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,8 +1,8 @@
 ---
 title: Soteria
-emoji: 📊
-colorFrom: indigo
-colorTo: yellow
+emoji: 🌍
+colorFrom: green
+colorTo: gray
 sdk: gradio
 app_file: app.py
 pinned: false

app.py

@@ -0,0 +1,79 @@
+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()})
+    response = requests.post(url=url, data=payload)
+    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 takes ~15 seconds to get the results.
+    """,
+    theme="grass",
+)
+iface.launch(share=False, show_error=True, inline=True, debug=True)

requirements.txt

@@ -0,0 +1,3 @@
+numpy
+tensorflow
+cloudpathlib[s3]