Update app.py

app.py

@@ -5,6 +5,7 @@ import torch
 import numpy as np
 from roboflow import Roboflow
 import cv2
+
 rf = Roboflow(api_key="gjZE3lykkitagkxHplyJ")
 project = rf.workspace().project("rideit")
 model = project.version(1).model
@@ -41,18 +42,17 @@ def normalize_2d(matrix):
     return matrix
 
 # Create predict function
-def predict(img) -> Tuple[Dict, float]:
-    """Transforms and performs a prediction on img and returns prediction and time taken.
-    """
-    # Start the timer
+def predict(img):
+
     start_time = timer()
     
     # Transform the target image and add a batch dimension
-    img1=PIL.Image.open(img)
+    image = cv2.imread(img)
+    img1 = PIL.Image.open(img)
     img1 = effnetb2_transforms(img1).unsqueeze(0)
     pix = normalize_2d(np.array(img))
 
-    pix1=model.predict(str(img), confidence=40, overlap=30)
+    pix1=model.predict(str(image), confidence=40, overlap=30).[0].cpu().numpy()
 
     
     # Put model into evaluation mode and turn on inference mode