minor changes

keras_model.h5 → model/keras_model.h5

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:9c72459c9ab862eb75b5d073e54b34dd04e81c1fc117aa64dcff248c97e97840
+oid sha256:e371d10c84ec45c9a2009e84e7a60cbf79726a71403908339fc08f37df7557a7
 size 2453432

model/pothole_model.py

@@ -23,12 +23,10 @@
 #     return predicted_class
 
 
-import tensorflow as tf
-from PIL import Image, ImageOps
+from keras.models import load_model  # TensorFlow is required for Keras to work
+from PIL import Image, ImageOps  # Install pillow instead of PIL
 import numpy as np
-import requests
 from io import BytesIO
-from keras.models import load_model
 
 
 
@@ -36,11 +34,11 @@ def load_image_model(image):
     # Disable scientific notation for clarity
     np.set_printoptions(suppress=True)
 
-    # Load the model from the URL
-    model = load_model("keras_model.h5", compile=False)
+    # Load the model
+    model = load_model("model\keras_model.h5", compile=False)
 
     # Load the labels
-    class_names = open("labels.txt", "r").readlines()
+    class_names = open("model\labels.txt", "r").readlines()
 
     # Create the array of the right shape to feed into the keras model
     # The 'length' or number of images you can put into the array is

routes/route.py

@@ -264,11 +264,11 @@ async def verify_pothole(potholeModel: PotholeModel, token: HTTPAuthorizationCre
         # Pass image bytes to your model function
         results = load_image_model(image_bytes)
 
-        if results == 1:
-            return JSONResponse(content={"response": "Pothole"})
-        else:
-            return JSONResponse(content={"response": "notPothole"})
-        # return JSONResponse(content={"response": "Pothole"})
+        # if results == 'pothple':
+        #     return JSONResponse(content={"response": "Pothole"})
+        # else:
+        #     return JSONResponse(content={"response": "notPothole"})
+        return JSONResponse(content={"response": {results}})
     except Exception as e:
         return JSONResponse(content={"response": f"{e}"})