Update app.py

app.py

@@ -222,6 +222,8 @@ def extract_final_answer(agent_response):
 # Replace BasicAgent with your SmolaAgent in the run_and_submit_all function
 import backoff
 import time
+import json
+import os
 
 # Add backoff decorator for API requests
 @backoff.on_exception(
@@ -246,7 +248,7 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
     space_id = os.getenv("SPACE_ID") # Get the SPACE_ID for sending link to the code
 
     if profile:
-        username= f"{profile.username}"
+        username = f"{profile.username}"
         print(f"User logged in: {username}")
     else:
         print("User not logged in.")
@@ -267,25 +269,49 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
     agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
     print(agent_code)
 
-    # 2. Fetch Questions with rate limit handling
-    print(f"Fetching questions from: {questions_url}")
-    try:
-        response = rate_limited_request("GET", questions_url, timeout=15)
-        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 requests.exceptions.RequestException as e:
-        print(f"Error fetching questions: {e}")
-        return f"Error fetching questions: {e}", None
-    except requests.exceptions.JSONDecodeError as e:
-         print(f"Error decoding JSON response from questions endpoint: {e}")
-         print(f"Response text: {response.text[:500]}")
-         return f"Error decoding server response for questions: {e}", None
-    except Exception as e:
-        print(f"An unexpected error occurred fetching questions: {e}")
-        return f"An unexpected error occurred fetching questions: {e}", None
+    # 2. Try to load cached questions or fetch from API with rate limit handling
+    cached_questions_path = "cached_questions.json"
+    
+    if os.path.exists(cached_questions_path) and os.path.getsize(cached_questions_path) > 2:
+        print(f"Loading cached questions from {cached_questions_path}")
+        try:
+            with open(cached_questions_path, "r") as f:
+                questions_data = json.load(f)
+            print(f"Loaded {len(questions_data)} questions from cache")
+        except Exception as e:
+            print(f"Error loading cached questions: {e}")
+            return f"Error loading cached questions: {e}", None
+    else:
+        # Fetch questions from API with rate limit handling
+        print(f"No cached questions found. Fetching from: {questions_url}")
+        try:
+            response = rate_limited_request("GET", questions_url, timeout=15)
+            questions_data = response.json()
+            
+            # Cache the questions for future runs
+            if questions_data:
+                try:
+                    with open(cached_questions_path, "w") as f:
+                        json.dump(questions_data, f)
+                    print(f"Cached {len(questions_data)} questions to {cached_questions_path}")
+                except Exception as e:
+                    print(f"Warning: Failed to cache questions: {e}")
+        except requests.exceptions.RequestException as e:
+            print(f"Error fetching questions: {e}")
+            return f"Error fetching questions: {e}", None
+        except requests.exceptions.JSONDecodeError as e:
+            print(f"Error decoding JSON response from questions endpoint: {e}")
+            print(f"Response text: {response.text[:500]}")
+            return f"Error decoding server response for questions: {e}", None
+        except Exception as e:
+            print(f"An unexpected error occurred fetching questions: {e}")
+            return f"An unexpected error occurred fetching questions: {e}", None
+
+    if not questions_data:
+        print("Questions list is empty.")
+        return "Questions list is empty or invalid format.", None
+    
+    print(f"Processing {len(questions_data)} questions...")
 
     # 3. Run your Agent
     results_log = []