rework app to use async and cache

.gitignore

@@ -0,0 +1,3 @@
+answer_cache.json
+uv.lock
+.venv

agent.py

@@ -0,0 +1,10 @@
+# --- Basic Agent Definition ---
+# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
+class Agent:
+    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

app.py

@@ -1,196 +1,236 @@
+import asyncio
+import json
 import os
+from pathlib import Path
+
+import aiohttp
 import gradio as gr
-import requests
-import inspect
 import pandas as pd
 
-# (Keep Constants as is)
-# --- Constants ---
+from agent import Agent
+
+# --- Constants --------------------------------------------------------------
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
+CACHE_PATH = Path("answers_cache.json")  # local answer cache
+MAX_CONCURRENCY = int(os.getenv("MAX_CONCURRENCY", 8))  # tune if needed
+# ----------------------------------------------------------------------------
 
-# --- 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):
+
+# ---------------------------------------------------------------------------
+# Small helpers
+# ---------------------------------------------------------------------------
+def load_cache() -> dict[str, str]:
+    if CACHE_PATH.is_file():
+        try:
+            return json.loads(CACHE_PATH.read_text())
+        except Exception:
+            print("⚠️  Cache file corrupt – starting fresh.")
+    return {}
+
+
+def save_cache(cache: dict[str, str]) -> None:
+    tmp = CACHE_PATH.with_suffix(".tmp")
+    tmp.write_text(json.dumps(cache, ensure_ascii=False, indent=2))
+    tmp.replace(CACHE_PATH)
+
+
+# ---------------------------------------------------------------------------
+# Core async logic
+# ---------------------------------------------------------------------------
+async def _fetch_questions(
+    session: aiohttp.ClientSession, url: str
+) -> list[dict]:
+    async with session.get(url, timeout=15) as r:
+        r.raise_for_status()
+        return await r.json()
+
+
+async def _submit_answers(session: aiohttp.ClientSession, url: str, data: dict):
+    async with session.post(url, json=data, timeout=60) as r:
+        r.raise_for_status()
+        return await r.json()
+
+
+async def _run_agent_async(
+    agent: Agent,
+    question: str,
+    task_id: str | int,
+    cache: dict[str, str],
+    semaphore: asyncio.Semaphore,
+) -> tuple[str | int, str]:
     """
-    Fetches all questions, runs the BasicAgent on them, submits all answers,
-    and displays the results.
+    Run the agent in a threadpool (because most agents are sync / blocking),
+    respecting concurrency limits, and fill the cache.
     """
-    # --- Determine HF Space Runtime URL and Repo URL ---
-    space_id = os.getenv("SPACE_ID") # Get the SPACE_ID for sending link to the code
+    if str(task_id) in cache:
+        return task_id, cache[str(task_id)]
 
-    if profile:
-        username= f"{profile.username}"
-        print(f"User logged in: {username}")
-    else:
-        print("User not logged in.")
-        return "Please Login to Hugging Face with the button.", None
+    loop = asyncio.get_running_loop()
+    async with semaphore:
+        answer = await loop.run_in_executor(
+            None, agent, question
+        )  # execute in default thread‑pool
+    cache[str(task_id)] = answer
+    return task_id, answer
+
+
+async def _async_main(profile: gr.OAuthProfile | None):
+    """
+    Async counterpart of run_and_submit_all; returns (status_msg, results_df)
+    """
+    if not profile:
+        return "Please login with the Hugging Face button.", None
+
+    username = profile.username
+    space_id = os.getenv("SPACE_ID")
+    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
 
     api_url = DEFAULT_API_URL
     questions_url = f"{api_url}/questions"
     submit_url = f"{api_url}/submit"
 
-    # 1. Instantiate Agent ( modify this part to create your agent)
+    # 1. Build agent (sync, cheap)
     try:
-        agent = BasicAgent()
+        agent = Agent()
     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
-
-    # 3. Run your Agent
-    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
+    # 2. Fetch questions
+    async with aiohttp.ClientSession() as session:
         try:
-            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})
+            questions = await _fetch_questions(session, questions_url)
         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.")
-        return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
+            return f"Error fetching questions: {e}", None
+
+        if not questions:
+            return "Fetched questions list is empty.", None
+
+        # 3. Run agent with cache + limited concurrency
+        cache = load_cache()
+        sem = asyncio.Semaphore(MAX_CONCURRENCY)
+        coros = [
+            _run_agent_async(agent, q["question"], q["task_id"], cache, sem)
+            for q in questions
+            if q.get("task_id") and q.get("question") is not None
+        ]
+
+        results_log: list[dict] = []
+        answers_json: list[dict] = []
+
+        for task_id, answer in await asyncio.gather(*coros):
+            answers_json.append(
+                {"task_id": task_id, "submitted_answer": answer}
+            )
+            results_log.append(
+                {
+                    "Task ID": task_id,
+                    "Question": next(
+                        (
+                            q["question"]
+                            for q in questions
+                            if q["task_id"] == task_id
+                        ),
+                        "",
+                    ),
+                    "Submitted Answer": answer,
+                }
+            )
+
+        # 3b. Persist cache for later runs
+        save_cache(cache)
+
+        if not answers_json:
+            return "Agent produced no answers.", pd.DataFrame(results_log)
+
+        # 4. Submit
+        submission = {
+            "username": username.strip(),
+            "agent_code": agent_code,
+            "answers": answers_json,
+        }
+        try:
+            result = await _submit_answers(session, submit_url, submission)
+        except Exception as e:
+            status = f"Submission failed: {e}"
+            return status, pd.DataFrame(results_log)
+
+    # Format success message
+    final = (
+        "Submission Successful!\n"
+        f"User: {result.get('username')}\n"
+        f"Overall Score: {result.get('score', 'N/A')}% "
+        f"({result.get('correct_count', '?')}/{result.get('total_attempted', '?')} correct)\n"
+        f"Message: {result.get('message', 'No message received.')}"
+    )
+    return final, pd.DataFrame(results_log)
 
-    # 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)
 
-    # 5. Submit
-    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
-    try:
-        response = requests.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"Message: {result_data.get('message', 'No message received.')}"
-        )
-        print("Submission successful.")
-        results_df = pd.DataFrame(results_log)
-        return final_status, results_df
-    except requests.exceptions.HTTPError as e:
-        error_detail = f"Server responded with status {e.response.status_code}."
-        try:
-            error_json = e.response.json()
-            error_detail += f" Detail: {error_json.get('detail', e.response.text)}"
-        except requests.exceptions.JSONDecodeError:
-            error_detail += f" Response: {e.response.text[:500]}"
-        status_message = f"Submission Failed: {error_detail}"
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
-    except requests.exceptions.Timeout:
-        status_message = "Submission Failed: The request timed out."
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
-    except requests.exceptions.RequestException as e:
-        status_message = f"Submission Failed: Network error - {e}"
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
-    except Exception as e:
-        status_message = f"An unexpected error occurred during submission: {e}"
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
+# ---------------------------------------------------------------------------
+# Thin sync wrapper required by gradio
+# ---------------------------------------------------------------------------
+def run_and_submit_all(profile: gr.OAuthProfile | None):
+    """
+    Synchronous façade expected by Gradio; just dispatches to asyncio.
+    """
+    return asyncio.run(_async_main(profile))
 
 
-# --- Build Gradio Interface using Blocks ---
+# ---------------------------------------------------------------------------
+# Gradio UI (unchanged except for doc‑string tweaks)
+# ---------------------------------------------------------------------------
 with gr.Blocks() as demo:
-    gr.Markdown("# Basic Agent Evaluation Runner")
+    gr.Markdown("# Async‑cached 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.
+        **Quick‑start**
 
-        ---
-        **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.
+        1. Fork this space, bring your own `Agent` in `agent.py`.
+        2. Log in with the HF button (needed for ranking).
+        3. Hit **Run Evaluation & Submit All Answers** – answers are cached
+           locally so reruns are instant; agent calls & HTTP are parallel.
         """
     )
 
     gr.LoginButton()
 
-    run_button = gr.Button("Run Evaluation & Submit All Answers")
+    run_btn = gr.Button("Run Evaluation & Submit All Answers")
+    status_box = gr.Textbox(label="Status / Submission Result", lines=5)
+    results_table = gr.DataFrame(label="Questions & Answers", wrap=True)
 
-    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(
+    run_btn.click(
         fn=run_and_submit_all,
-        outputs=[status_output, results_table]
+        outputs=[status_box, results_table],
     )
 
 if __name__ == "__main__":
-    print("\n" + "-"*30 + " App Starting " + "-"*30)
+    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")  # Get SPACE_ID at startup
 
     if space_host_startup:
         print(f"✅ SPACE_HOST found: {space_host_startup}")
-        print(f"   Runtime URL should be: https://{space_host_startup}.hf.space")
+        print(
+            f"   Runtime URL should be: https://{space_host_startup}.hf.space"
+        )
     else:
-        print("ℹ️  SPACE_HOST environment variable not found (running locally?).")
+        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 repo URLs if SPACE_ID is found
         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")
+        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?). Repo URL cannot be determined."
+        )
 
-    print("-"*(60 + len(" App Starting ")) + "\n")
+    print("-" * (60 + len(" App Starting ")) + "\n")
 
     print("Launching Gradio Interface for Basic Agent Evaluation...")
-    demo.launch(debug=True, share=False)
+    demo.launch(debug=True, share=False)

requirements.txt

@@ -1,2 +1,3 @@
 gradio
-requests
+requests
+aiohttp