Run agent again

app.py

@@ -82,17 +82,17 @@ def run_and_submit_all( profile: gr.OAuthProfile | None, mock_submission: bool =
         if not task_id or question_text is None:
             print(f"Skipping item with missing task_id or question: {item}")
             continue
-        if "wiki" in question_text.lower() or "youtube" in question_text.lower():
+        if "youtube" in question_text.lower():
             continue
-        # try:
-        #     if file_path:
-        #         question_text = question_text + f"\n\nHere is also the path to the file for the task (file name matches with task ID and is not in plain English): {file_path}"
-        #     submitted_answer = agent(question_text)
-        #     answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
-        #     results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer})
-        # except Exception as e:
-        #     print(f"Error running agent on task {task_id}: {e}")
-        #     results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}"})
+        try:
+            if file_path:
+                question_text = question_text + f"\n\nHere is also the path to the file for the task (file name matches with task ID and is not in plain English): {file_path}"
+            submitted_answer = agent(question_text)
+            answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
+            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer})
+        except Exception as e:
+            print(f"Error running agent on task {task_id}: {e}")
+            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}"})
 
     if not answers_payload:
         print("Agent did not produce any answers to submit.")

vlm_tools.py

@@ -231,7 +231,6 @@ def image_processing(image: str, brightness: float = 1.0, contrast: float = 1.0)
     return processed_image
 
 onnx_path = "vlm_assets/yolov3-8.onnx"
-names_path = "vlm_assets/obj.names"
 
 class ObjectDetectionTool(Tool):
     name = "object_detection"
@@ -249,7 +248,6 @@ class ObjectDetectionTool(Tool):
     def setup(self):
         # Load ONNX model
         self.onnx_path = onnx_path
-        self.names_path = names_path
         self.onnx_model = onnxruntime.InferenceSession(self.onnx_path)
         
         # Load class labels - using a predefined list since we can't use open()