initial

part3/api.py

@@ -0,0 +1,56 @@
+from flask import Flask, request
+import numpy as np
+import tensorflow as tf
+from PIL import Image
+import io
+
+app = Flask(__name__)
+model = tf.keras.models.load_model('vgg16Variant.keras')
+
+@app.route('/summary', methods=['GET'])
+def model_info():
+   return {
+      "version": "vgg16Variant",
+      "name": "Katrina_damage",
+      "description": "Classify satellite images of buildings in the aftermath of a hurricane into 'damage' or 'no_damage' categories",
+      "number_of_parameters": 16812353
+   }
+# def model_info():
+#    return {
+#       "version": "leNet5Variant",
+#       "name": "Katrina_damage",
+#       "description": "Classify satellite images of buildings in the aftermath of a hurricane into 'damage' or 'no_damage' categories",
+#       "number_of_parameters": 2601153
+#    }
+    
+@app.route('/inference', methods=['POST'])
+def upload_file():
+
+    # check if the post request has the file part
+   if 'image' not in request.files:
+      # if the user did not pass the image under `image`, we don't know what they are
+      # don't, so return an error.
+      return {"error": "Invalid request; pass a binary image file as a multi-part form under the image key."}, 404
+   # get the data
+   data = request.files['image']
+   # do something with data...
+   print(data) # apparently this is a FileStorage object?? 
+    # Googled how to deal with it and got the following code to turn it into a numpy array
+   image_bytes = data.read()
+   image_stream = io.BytesIO(image_bytes)
+    
+   with Image.open(image_stream).convert("RGB") as pil_image:
+       data = np.asarray(pil_image)
+
+   print(data.shape) 
+   data = data / np.max(data)
+   data = data.reshape(1,128,128,3)
+   prediction = model.predict(data)[0][0]
+   if prediction < 0.5:
+       return {"prediction": "damage"}
+   else:
+       return {"prediction": "no_damage"}
+
+# start the development server
+if __name__ == '__main__':
+   app.run(debug=True, host='0.0.0.0')