model from aws

app.py

@@ -5,10 +5,15 @@ import tensorflow as tf
 import random 
 from tensorflow import keras
 
+from cloudpathlib import CloudPath
 
-damage_types = np.array(sorted(['yes', 'no']))
+cp = CloudPath("s3://soteria-ml-models/")
+cp.download_to("./models")
+
+
+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('Disaster-Classification')
+model = keras.models.load_model('./models/disaster-classification')
 
 def damage_classification(img):
     prediction = np.random.rand(1, 2)[0]
@@ -17,8 +22,8 @@ def damage_classification(img):
 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]
+    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(