Adds cache loading and verbose cache updates

app.py

@@ -8,7 +8,7 @@ from llama_index.llms.huggingface_api import HuggingFaceInferenceAPI
 from llama_index.core.agent.workflow import AgentWorkflow
 from _tools import tools
 import asyncio
-from utils import cache_answers, update_cache_answer, get_cached_answer
+from utils import cache_answers, update_cache_answer, get_cached_answer, load_cache
 
 # (Keep Constants as is)
 # --- Constants ---
@@ -114,23 +114,6 @@ def fetch_questions(questions_url):
         
         return None, f"An unexpected error occurred fetching questions: {e}"
 
-async def fetch_file(question: Question) -> str | None:
-    """
-    Fetch files from the provided list of file paths.
-    """
-    file_url = f"{DEFAULT_API_URL}/files"
-    
-    try:
-        response = requests.get(f"{file_url}/{question['task_id']}", timeout=15)
-        response.raise_for_status()
-
-        print(f"File {question['task_id']} fetched successfully.")
-        return response.content
-    
-    except requests.exceptions.RequestException as e:
-        print(f"Error fetching file {question['task_id']}: {e}")
-        return None
-
 async def run_agent_on_questions(agent: BasicAgent, questions_data: Questions):
     results_log = []
     answers_payload = []
@@ -256,7 +239,9 @@ async def run_and_submit_all(profile: gr.OAuthProfile | None):
 
     submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
     status_update = f"Agent finished. Submitting {len(answers_payload)} answers for user '{username}'..."
+    
     print(status_update)
+    print(f"Cached answers: {load_cache()}")
 
     return submit_answers(submit_url, submission_data, results_log)
 

utils.py

@@ -33,11 +33,21 @@ def cache_answers(questions, cache_path: str = CACHE_FILE) -> AnswerCache:
     return cache
 
 def update_cache_answer(task_id: str, answer: str, is_correct: bool = False, cache_path: str = CACHE_FILE):
+    """
+    Update the cache with the answer and its correctness status.
+    """
+    print(f"Updating cache for task ID {task_id} with answer: {answer} and isCorrect: {is_correct}")
+    
     cache = load_cache(cache_path)
     cache[task_id] = {"answer": answer, "isCorrect": is_correct}
     save_cache(cache, cache_path)
 
 
 def get_cached_answer(task_id: str, cache_path: str = CACHE_FILE) -> Optional[AnswerCacheEntry]:
+    """
+    Retrieve the cached answer for a given task ID.
+    """
     cache = load_cache(cache_path)
+    print(f"Cached answer for task ID {task_id}: {cache.get(task_id)}")
+    
     return cache.get(task_id)