Update app.py

app.py

@@ -2,6 +2,7 @@ import os
 import gradio as gr
 import requests
 import pandas as pd
+import json
 from transformers import pipeline
 
 # --- Constants ---
@@ -17,9 +18,9 @@ class ZeroShotAgent:
         print(f"Received question: {question[:100]}")
         labels = ["Yes", "No", "Not Enough Information"]
         result = self.classifier(question, labels)
-        best_answer = result["labels"][0]
+        best_answer = result["labels"][0] if result and "labels" in result else "Not Enough Information"
         print(f"Answer: {best_answer}")
-        return best_answer
+        return best_answer or "Not Enough Information"
 
 def run_and_submit_all(profile: gr.OAuthProfile | None):
     space_id = os.getenv("SPACE_ID")
@@ -49,35 +50,39 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
         response.raise_for_status()
         questions_data = response.json()
         if not questions_data:
-            print("Fetched questions list is empty.")
             return "Fetched questions list is empty or invalid format.", None
         print(f"Fetched {len(questions_data)} questions.")
     except Exception as e:
-        print(f"Error fetching questions: {e}")
         return f"Error fetching questions: {e}", None
 
     results_log = []
     answers_payload = []
-    print(f"Running agent on {len(questions_data)} questions...")
     for item in questions_data:
         task_id = item.get("task_id")
         question_text = item.get("question")
         if not task_id or question_text is None:
-            print(f"Skipping item with missing task_id or question: {item}")
             continue
         try:
             submitted_answer = agent(question_text)
+            if not submitted_answer:
+                submitted_answer = "Not Enough Information"
             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:
         return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
 
-    submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
-    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
+    submission_data = {
+        "username": username.strip(),
+        "agent_code": agent_code,
+        "answers": answers_payload
+    }
+
+    # Debug payload
+    print("Submission Payload Preview:")
+    print(json.dumps(submission_data, indent=2)[:1000])  # Safe preview log
 
     try:
         response = requests.post(submit_url, json=submission_data, timeout=60)
@@ -90,10 +95,11 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
             f"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')} correct)\n"
             f"📝 Message: {result_data.get('message', 'No message received.')}"
         )
-        print(final_status)
         return final_status, pd.DataFrame(results_log)
+    except requests.exceptions.RequestException as e:
+        return f"❌ Submission Failed: {str(e)}", pd.DataFrame(results_log)
     except Exception as e:
-        return f"❌ Submission Failed: {e}", pd.DataFrame(results_log)
+        return f"❌ Unexpected Error: {e}", pd.DataFrame(results_log)
 
 # --- Gradio UI ---
 with gr.Blocks() as demo:
@@ -103,7 +109,8 @@ with gr.Blocks() as demo:
         ✅ **Instructions:**
         1. Login to your Hugging Face account.
         2. Click 'Run Evaluation & Submit All Answers'.
-        3. Wait for results. You need ~30% to pass Level 1 and unlock the certificate.
+        3. Wait ~1–2 mins for results.
+        📌 You need at least **30% correct** on Level 1 to unlock the certificate.
         """
     )