updated roboflow model to improve accuracy

app.py

@@ -112,7 +112,6 @@ def fetchImage():
         print(f"Image downloaded and saved as {file_name}")
     else:
         print(f"Failed to download image. Status code: {response.status_code}")
-    # Load image
     image = cv2.imread(file_name)
 
     rf = Roboflow(api_key="LqD8Cs4OsoK8seO3CPkf")
@@ -262,8 +261,16 @@ def fetchImage():
         api_url="https://detect.roboflow.com",
         api_key="LqD8Cs4OsoK8seO3CPkf"
     )
-    result = CLIENT.infer(file_name, model_id="car-damage-detection-krsix/1")
-    labels = [item["class"] for item in result["predictions"]]
+    print(file_name)
+
+    result = CLIENT.infer(file_name, model_id="car-damage-detection-krsix/1",
+                          confidence=0.3)  # Set confidence threshold to 50%
+    threshold = 0.3  # Adjust this as needed
+    filtered_labels = [item["class"]
+                       for item in result["predictions"] if item["confidence"] >= threshold]
+
+    print(filtered_labels)
+    labels = filtered_labels
     print(labels)
     for class_ in labels:
         total_cost += repair_costs.get(class_, 0)