Update app.py

app.py

@@ -3,6 +3,10 @@ import gradio as gr
 import os
 import torch
 import numpy as np
+from roboflow import Roboflow
+rf = Roboflow(api_key="gjZE3lykkitagkxHplyJ")
+project = rf.workspace().project("rideit")
+model = project.version(1).model
 
 from model import create_effnetb2_model
 from timeit import default_timer as timer
@@ -45,6 +49,9 @@ def predict(img) -> Tuple[Dict, float]:
     # Transform the target image and add a batch dimension
     img1 = effnetb2_transforms(img).unsqueeze(0)
     pix = normalize_2d(np.array(img))
+
+    pix1=model.predict(img, confidence=40, overlap=30)
+
     
     # Put model into evaluation mode and turn on inference mode
     effnetb2.eval()
@@ -59,7 +66,7 @@ def predict(img) -> Tuple[Dict, float]:
     pred_time = round(timer() - start_time, 5)
     
     # Return the prediction dictionary and prediction time 
-    return pred_labels_and_probs, pred_time,pix
+    return pred_labels_and_probs, pred_time,pix1
 
 ### 4. Gradio app ###