Update app.py

app.py

@@ -1,34 +1,113 @@
+```python
 import os
 import gradio as gr
 import requests
-import inspect
 import pandas as pd
+import json
+import time
+import asyncio
+from concurrent.futures import ThreadPoolExecutor
+from pathlib import Path
+from langchain_core.messages import HumanMessage
+from requests.adapters import HTTPAdapter
+from urllib3.util.retry import Retry
+from agent import AdvancedAgent
 
-# (Keep Constants as is)
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
+CACHE_FILE = "questions_cache.json"
+CACHE_EXPIRATION_SECONDS = 86400  # 1 day
+MAX_RETRIES = 3
+INITIAL_BACKOFF = 2  # seconds
 
-# --- Basic Agent Definition ---
-# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
-class BasicAgent:
-    def __init__(self):
-        print("BasicAgent initialized.")
-    def __call__(self, question: str) -> str:
-        print(f"Agent received question (first 50 chars): {question[:50]}...")
-        fixed_answer = "This is a default answer."
-        print(f"Agent returning fixed answer: {fixed_answer}")
-        return fixed_answer
-
-def run_and_submit_all( profile: gr.OAuthProfile | None):
+def create_retry_session():
+    """Create a requests session with retry logic for handling 429 errors."""
+    session = requests.Session()
+    retries = Retry(
+        total=MAX_RETRIES,
+        backoff_factor=INITIAL_BACKOFF,
+        status_forcelist=[429],
+        allowed_methods=["GET", "POST"]
+    )
+    adapter = HTTPAdapter(max_retries=retries)
+    session.mount("http://", adapter)
+    session.mount("https://", adapter)
+    return session
+
+def load_cached_questions():
+    """Load cached questions if the cache is still valid."""
+    cache_path = Path(CACHE_FILE)
+    if cache_path.exists():
+        try:
+            with cache_path.open('r') as f:
+                data = json.load(f)
+                cached_time = data.get("timestamp")
+                if cached_time and (time.time() - cached_time) < CACHE_EXPIRATION_SECONDS:
+                    questions = [
+                        {
+                            "task_id": item["task_id"],
+                            "question": HumanMessage(content=item["question"])
+                        }
+                        for item in data["questions"]
+                    ]
+                    print(f"Loaded {len(questions)} questions from cache.")
+                    return questions
+                else:
+                    print("Cache expired.")
+        except Exception as e:
+            print(f"Error loading cached questions: {e}")
+    return None
+
+def cache_questions(questions_data):
+    """Cache questions with a timestamp."""
+    cache_path = Path(CACHE_FILE)
+    try:
+        cache_data = {
+            "timestamp": time.time(),
+            "questions": [
+                {
+                    "task_id": item["task_id"],
+                    "question": item["question"].content
+                }
+                for item in questions_data
+            ]
+        }
+        with cache_path.open('w') as f:
+            json.dump(cache_data, f, indent=2)
+        print(f"Cached {len(questions_data)} questions to {CACHE_FILE}.")
+    except Exception as e:
+        print(f"Error caching questions: {e}")
+
+async def process_question(agent, item):
+    """Process a single question using the agent."""
+    task_id = item["task_id"]
+    question = item["question"]
+    try:
+        loop = asyncio.get_event_loop()
+        submitted_answer = await loop.run_in_executor(None, agent, question.content)
+        return {
+            "task_id": task_id,
+            "submitted_answer": submitted_answer,
+            "question_text": question.content
+        }
+    except Exception as e:
+        print(f"Error processing task {task_id}: {e}")
+        return {
+            "task_id": task_id,
+            "submitted_answer": f"AGENT ERROR: {e}",
+            "question_text": question.content
+        }
+
+async def run_and_submit_all(profile: gr.OAuthProfile | None):
     """
-    Fetches all questions, runs the BasicAgent on them, submits all answers,
+    Fetches all questions, runs the AdvancedAgent on them, submits all answers,
     and displays the results.
     """
     # --- Determine HF Space Runtime URL and Repo URL ---
-    space_id = os.getenv("SPACE_ID") # Get the SPACE_ID for sending link to the code
+    space_id = os.getenv("SPACE_ID")
 
     if profile:
-        username= f"{profile.username}"
+        username = f"{profile.username}"
         print(f"User logged in: {username}")
     else:
         print("User not logged in.")
@@ -38,60 +117,80 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
     questions_url = f"{api_url}/questions"
     submit_url = f"{api_url}/submit"
 
-    # 1. Instantiate Agent ( modify this part to create your agent)
+    # 1. Instantiate Agent
     try:
-        agent = BasicAgent()
+        agent = AdvancedAgent()
     except Exception as e:
         print(f"Error instantiating agent: {e}")
         return f"Error initializing agent: {e}", None
-    # In the case of an app running as a hugging Face space, this link points toward your codebase ( usefull for others so please keep it public)
+
     agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
     print(agent_code)
 
-    # 2. Fetch Questions
-    print(f"Fetching questions from: {questions_url}")
-    try:
-        response = requests.get(questions_url, timeout=15)
-        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 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. Fetch or Load Questions
+    questions_data = load_cached_questions()
+    if questions_data is None:
+        print(f"Fetching questions from: {questions_url}")
+        try:
+            session = create_retry_session()
+            response = session.get(questions_url, timeout=15)
+            response.raise_for_status()
+            raw_questions = response.json()
+            if not raw_questions:
+                print("Fetched questions list is empty.")
+                return "Fetched questions list is empty or invalid format.", None
+            questions_data = [
+                {
+                    "task_id": item["task_id"],
+                    "question": HumanMessage(content=item["question"])
+                }
+                for item in raw_questions
+            ]
+            print(f"Fetched {len(questions_data)} questions.")
+            cache_questions(questions_data)
+        except requests.exceptions.RequestException as e:
+            print(f"Error fetching questions: {e}")
+            # Fallback to cache even if expired
+            questions_data = load_cached_questions()
+            if questions_data:
+                print("Using expired cache due to API failure.")
+            else:
+                return f"Error fetching questions and no valid cache: {e}", None
+        except requests.exceptions.JSONDecodeError as e:
+            print(f"Error decoding JSON response: {e}")
+            return f"Error decoding server response: {e}", None
+        except Exception as e:
+            print(f"An unexpected error occurred fetching questions: {e}")
+            return f"An unexpected error occurred: {e}", None
 
-    # 3. Run your Agent
+    # 3. Run Agent Asynchronously
     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)
+    print(f"Running agent on {len(questions_data)} questions asynchronously...")
+
+    with ThreadPoolExecutor(max_workers=5) as executor:
+        tasks = [process_question(agent, item) for item in questions_data]
+        responses = await asyncio.gather(*tasks, return_exceptions=True)
+
+        for response in responses:
+            if isinstance(response, Exception):
+                print(f"Unexpected error in async processing: {response}")
+                continue
+            task_id = response["task_id"]
+            submitted_answer = response["submitted_answer"]
+            question_text = response["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}"})
+            results_log.append({
+                "Task ID": task_id,
+                "Question": question_text,
+                "Submitted Answer": submitted_answer
+            })
 
     if not answers_payload:
         print("Agent did not produce any answers to submit.")
         return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
 
-    # 4. Prepare Submission 
+    # 4. Prepare Submission
     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)
@@ -99,14 +198,15 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
     # 5. Submit
     print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
     try:
-        response = requests.post(submit_url, json=submission_data, timeout=60)
+        session = create_retry_session()
+        response = session.post(submit_url, json=submission_data, timeout=60)
         response.raise_for_status()
         result_data = response.json()
         final_status = (
             f"Submission Successful!\n"
             f"User: {result_data.get('username')}\n"
             f"Overall Score: {result_data.get('score', 'N/A')}% "
-            f"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')} correct)\n"
+            f"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?']} correct)\n"
             f"Message: {result_data.get('message', 'No message received.')}"
         )
         print("Submission successful.")
@@ -139,31 +239,25 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
         results_df = pd.DataFrame(results_log)
         return status_message, results_df
 
-
 # --- Build Gradio Interface using Blocks ---
 with gr.Blocks() as demo:
-    gr.Markdown("# Basic Agent Evaluation Runner")
+    gr.Markdown("# Advanced Agent Evaluation Runner")
     gr.Markdown(
         """
         **Instructions:**
-
-        1.  Please clone this space, then modify the code to define your agent's logic, the tools, the necessary packages, etc ...
-        2.  Log in to your Hugging Face account using the button below. This uses your HF username for submission.
-        3.  Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
-
+        1. Modify the `agent.py` to define your agent's logic, tools, and packages.
+        2. Log in to your Hugging Face account using the button below.
+        3. Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
         ---
         **Disclaimers:**
-        Once clicking on the "submit button, it can take quite some time ( this is the time for the agent to go through all the questions).
-        This space provides a basic setup and is intentionally sub-optimal to encourage you to develop your own, more robust solution. For instance for the delay process of the submit button, a solution could be to cache the answers and submit in a seperate action or even to answer the questions in async.
+        The submission process may take time due to the number of questions.
+        Questions are cached locally to reduce API calls.
         """
     )
 
     gr.LoginButton()
-
     run_button = gr.Button("Run Evaluation & Submit All Answers")
-
     status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
-    # Removed max_rows=10 from DataFrame constructor
     results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
 
     run_button.click(
@@ -173,9 +267,8 @@ with gr.Blocks() as demo:
 
 if __name__ == "__main__":
     print("\n" + "-"*30 + " App Starting " + "-"*30)
-    # Check for SPACE_HOST and SPACE_ID at startup for information
     space_host_startup = os.getenv("SPACE_HOST")
-    space_id_startup = os.getenv("SPACE_ID") # Get SPACE_ID at startup
+    space_id_startup = os.getenv("SPACE_ID")
 
     if space_host_startup:
         print(f"✅ SPACE_HOST found: {space_host_startup}")
@@ -183,14 +276,14 @@ if __name__ == "__main__":
     else:
         print("ℹ️  SPACE_HOST environment variable not found (running locally?).")
 
-    if space_id_startup: # Print repo URLs if SPACE_ID is found
+    if space_id_startup:
         print(f"✅ SPACE_ID found: {space_id_startup}")
         print(f"   Repo URL: https://huggingface.co/spaces/{space_id_startup}")
         print(f"   Repo Tree URL: https://huggingface.co/spaces/{space_id_startup}/tree/main")
     else:
-        print("ℹ️  SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined.")
+        print("ℹ️  SPACE_ID environment variable not found (running locally?).")
 
     print("-"*(60 + len(" App Starting ")) + "\n")
-
-    print("Launching Gradio Interface for Basic Agent Evaluation...")
-    demo.launch(debug=True, share=False)
+    print("Launching Gradio Interface for Advanced Agent Evaluation...")
+    demo.launch(debug=True, share=False)
+```