+ async answer generation

app.py

@@ -4,6 +4,8 @@ import requests
 import inspect
 import pandas as pd
 from agents import LlamaIndexAgent
+import asyncio
+
 # (Keep Constants as is)
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
@@ -14,91 +16,95 @@ class BasicAgent:
     def __init__(self):
         self.agent = LlamaIndexAgent()
         print("BasicAgent initialized.")
-    def __call__(self, question: str) -> str:
+    async def aquery(self, question: str) -> str:
         print(f"Agent received question (first 50 chars): {question[:50]}...")
-        response = self.agent.run_query(question)
+        response = await self.agent.run_query(question)
         print(f"Agent returning fixed answer: {response}")
         return response
 
-def run_and_submit_all( profile: gr.OAuthProfile | None):
+# Global cache for answers (in-memory)
+cached_answers = None
+cached_results_log = None
+cached_questions = None
+
+async def generate_answers(profile: gr.OAuthProfile | None, progress=gr.Progress(track_tqdm=True)):
     """
-    Fetches all questions, runs the BasicAgent on them, submits all answers,
-    and displays the results.
+    Fetches all questions, runs the BasicAgent on them asynchronously, and returns the answers and log.
     """
-    # --- Determine HF Space Runtime URL and Repo URL ---
-    space_id = os.getenv("SPACE_ID") # Get the SPACE_ID for sending link to the code
-
+    global cached_answers, cached_results_log, cached_questions
+    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.")
-        return "Please Login to Hugging Face with the button.", None
-
+        return "Please Login to Hugging Face with the button.", None, gr.update(interactive=False), gr.update(value=0, visible=False)
     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)
-    try:
-        agent = BasicAgent()
-    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("Fetched questions list is empty.")
+            return "Fetched questions list is empty or invalid format.", None, gr.update(interactive=False), gr.update(value=0, visible=False)
         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
+        print(f"Error fetching questions: {e}")
+        return f"Error fetching questions: {e}", None, gr.update(interactive=False), gr.update(value=0, visible=False)
+    agent = BasicAgent()
     results_log = []
     answers_payload = []
-    print(f"Running agent on {len(questions_data)} questions...")
-    for item in questions_data:
+    cached_questions = questions_data
+    total = len(questions_data)
+    progress(0, desc="Starting answer generation...")
+    async def answer_one(item):
         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
+            return {"Task ID": task_id, "Question": question_text, "Submitted Answer": "SKIPPED"}, None
         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})
+            submitted_answer = await agent.aquery(question_text)
+            return {"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer}, {"task_id": task_id, "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:
-        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 
-    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}")
+            print(f"Error running agent on task {task_id}: {e}")
+            return {"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}"}, None
+    tasks = [answer_one(item) for item in questions_data]
+    results_log = []
+    answers_payload = []
+    for idx, coro in enumerate(asyncio.as_completed(tasks)):
+        log, answer = await coro
+        results_log.append(log)
+        if answer:
+            answers_payload.append(answer)
+        progress(int((idx+1)/total*100), desc=f"Answered {idx+1}/{total}")
+    cached_answers = answers_payload
+    cached_results_log = results_log
+    progress(100, desc="Done.")
+    results_df = pd.DataFrame(results_log)
+    return "Answer generation complete. Review and submit.", results_df, gr.update(interactive=True), gr.update(value=100, visible=True)
+
+def submit_answers(profile: gr.OAuthProfile | None):
+    """
+    Submits cached answers and returns the result.
+    """
+    global cached_answers, cached_results_log, cached_questions
+    space_id = os.getenv("SPACE_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
+    if not cached_answers:
+        print("No answers to submit.")
+        return "No answers to submit. Please generate answers first.", None
+    api_url = DEFAULT_API_URL
+    submit_url = f"{api_url}/submit"
+    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
+    submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": cached_answers}
+    print(f"Submitting {len(cached_answers)} answers to: {submit_url}")
     try:
         response = requests.post(submit_url, json=submission_data, timeout=60)
         response.raise_for_status()
@@ -110,36 +116,12 @@ 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("Submission successful.")
-        results_df = pd.DataFrame(results_log)
+        results_df = pd.DataFrame(cached_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
-
+        print(f"Submission error: {e}")
+        results_df = pd.DataFrame(cached_results_log)
+        return f"Submission Failed: {e}", results_df
 
 # --- Build Gradio Interface using Blocks ---
 with gr.Blocks() as demo:
@@ -150,26 +132,36 @@ with gr.Blocks() as demo:
 
         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.
+        3.  Click 'Generate Answers' to fetch questions and run your agent. Review the answers, then click 'Submit Answers' to submit them and see your 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.
+        Generating answers may take some time. This space provides a basic setup and is intentionally sub-optimal to encourage you to develop your own, more robust solution. For instance, you could cache the answers and submit in a separate action or answer the questions asynchronously.
         """
     )
 
     gr.LoginButton()
 
-    run_button = gr.Button("Run Evaluation & Submit All Answers")
+    with gr.Row():
+        generate_button = gr.Button("Generate Answers")
+        submit_button = gr.Button("Submit Answers", interactive=False)
 
-    status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
-    # Removed max_rows=10 from DataFrame constructor
+    progress_bar = gr.Progress(label="Progress", value=0, minimum=0, maximum=100, visible=False)
+
+    status_output = gr.Textbox(label="Status / Submission Result", lines=5, interactive=False)
     results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
 
-    run_button.click(
-        fn=run_and_submit_all,
-        outputs=[status_output, results_table]
+    generate_button.click(
+        fn=generate_answers,
+        inputs=[gr.OAuthProfile],
+        outputs=[status_output, results_table, submit_button, progress_bar],
+        api_name="generate_answers"
+    )
+    submit_button.click(
+        fn=submit_answers,
+        inputs=[gr.OAuthProfile],
+        outputs=[status_output, results_table],
+        api_name="submit_answers"
     )
 
 if __name__ == "__main__":