Update main.py

main.py

@@ -1,46 +1,46 @@
-from fastapi import FastAPI, UploadFile, file_uploader
-import json
-from PIL import Image
-from io import BytesIO
-import numpy as np
-from model import build_model
-app = FastAPI()
-
-#Load model
-image_shape = (224,224,3)
-num_classes = 6
-model = build_model(image_shape, num_classes)
-model.load_weights('./model_with_weights.h5')
-classes = {
-    0: 'Ahegao',
-    1: 'Angry',
-    2: 'Happy',
-    3: 'Neutral'.
-    4: 'Sad',
-    5: 'Surprise'
-}
-
-@app.get("/")
-def first_api():
-    return {
-        "response": "Face Expression Prediction"
-    }
-
-@app.post("/prediction")
-async def prediction(image: UploadFile = File(...)):
-    image = await image.read()
-
-    # process image
-    image = Image.open(BytesIO(image))
-    image = image.resize((image_shape[0], image_shape[1]))
-    image = image.convert('L')
-    image = np.expand_dims(image, axis=2)
-
-    image = np.expand_dims(image, axis=0)
-    prediction = model.predict(image)[0]
-    label = np.argmax(prediction, axis=-1).tolist()
-
-    return {
-        "label": label,
-        "class": classes[label]
+from fastapi import FastAPI, UploadFile, file_uploader
+import json
+from PIL import Image
+from io import BytesIO
+import numpy as np
+from model import build_model
+app = FastAPI()
+
+#Load model
+image_shape = (224,224,3)
+num_classes = 6
+model = build_model(image_shape, num_classes)
+model.load_weights('./model_with_weights.h5')
+classes = {
+    0: 'Ahegao',
+    1: 'Angry',
+    2: 'Happy',
+    3: 'Neutral',
+    4: 'Sad',
+    5: 'Surprise'
+}
+
+@app.get("/")
+def first_api():
+    return {
+        "response": "Face Expression Prediction"
+    }
+
+@app.post("/prediction")
+async def prediction(image: UploadFile = File(...)):
+    image = await image.read()
+
+    # process image
+    image = Image.open(BytesIO(image))
+    image = image.resize((image_shape[0], image_shape[1]))
+    image = image.convert('L')
+    image = np.expand_dims(image, axis=2)
+
+    image = np.expand_dims(image, axis=0)
+    prediction = model.predict(image)[0]
+    label = np.argmax(prediction, axis=-1).tolist()
+
+    return {
+        "label": label,
+        "class": classes[label]
     }