Image rescaling bug fix

backend.py

@@ -1,64 +1,64 @@
-import os
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-
-from fastapi import FastAPI, UploadFile, File
-import json
-from PIL import Image
-from io import BytesIO
-import numpy as np
-
-from model import get_model
-
-app = FastAPI()
-
-IMAGE_WIDTH = 224
-IMAGE_HEIGHT = 224
-
-MODEL_WEIGHT_PATH = 'vgg_face_weights2.h5'
-model = get_model(
-		image_shape = (IMAGE_WIDTH, IMAGE_HEIGHT, 3),
-		num_classes = 6,
-		model_weights = MODEL_WEIGHT_PATH
-	)
-print(model.summary())
-print("Model Loaded Successfully")
-
-######### Utilities #########
-def load_image(image_data):
-	image = Image.open(BytesIO(image_data))
-	return image
-
-def preprocess(image):
-	image = image.resize((IMAGE_WIDTH, IMAGE_HEIGHT))
-
-	image = np.array(image)
-	image = np.expand_dims(image, axis=0)
-
-	return image
-
-def get_prediction(image):
-	probs = model.predict(image)[0]
-	label = np.argmax(probs)
-
-	return {
-		'pred_probs': probs.tolist(),
-		'label': int(label)
-	}
-
-@app.get("/")
-def foo():
-	return {
-		"status": "Face Expression Classifier"
-	}
-
-
-@app.post("/get_prediction")
-async def predict(face_img: UploadFile = File(...)):
-	image = load_image(await face_img.read())
-
-	image = preprocess(image)
-	result = get_prediction(image)
-
-	return {
-		"result": json.dumps(result)
+import os
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+
+from fastapi import FastAPI, UploadFile, File
+import json
+from PIL import Image
+from io import BytesIO
+import numpy as np
+
+from model import get_model
+
+app = FastAPI()
+
+IMAGE_WIDTH = 224
+IMAGE_HEIGHT = 224
+
+MODEL_WEIGHT_PATH = 'vgg_face_weights2.h5'
+model = get_model(
+		image_shape = (IMAGE_WIDTH, IMAGE_HEIGHT, 3),
+		num_classes = 6,
+		model_weights = MODEL_WEIGHT_PATH
+	)
+print(model.summary())
+print("Model Loaded Successfully")
+
+######### Utilities #########
+def load_image(image_data):
+	image = Image.open(BytesIO(image_data))
+	return image
+
+def preprocess(image):
+	image = image.resize((IMAGE_WIDTH, IMAGE_HEIGHT))
+
+	image = np.array(image)
+	image = np.expand_dims(image, axis=0) / 255.0
+
+	return image
+
+def get_prediction(image):
+	probs = model.predict(image)[0]
+	label = np.argmax(probs)
+
+	return {
+		'pred_probs': probs.tolist(),
+		'label': int(label)
+	}
+
+@app.get("/")
+def foo():
+	return {
+		"status": "Face Expression Classifier"
+	}
+
+
+@app.post("/get_prediction")
+async def predict(face_img: UploadFile = File(...)):
+	image = load_image(await face_img.read())
+
+	image = preprocess(image)
+	result = get_prediction(image)
+
+	return {
+		"result": json.dumps(result)
 	}