app.py

import os
import gradio as gr
import requests
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

# --- 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

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 AdvancedAgent on them, submits all answers,
    and displays the results.
    """
    # --- Determine HF Space Runtime URL and Repo URL ---
    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

    api_url = DEFAULT_API_URL
    questions_url = f"{api_url}/questions"
    submit_url = f"{api_url}/submit"

    # 1. Instantiate Agent
    try:
        agent = AdvancedAgent()
    except Exception as e:
        print(f"Error instantiating agent: {e}")
        return f"Error initializing agent: {e}", None

    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
    print(agent_code)

    # 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 Agent Asynchronously
    results_log = []
    answers_payload = []
    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
            })

    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}")
    try:
        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"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

# --- Build Gradio Interface using Blocks ---
with gr.Blocks() as demo:
    gr.Markdown("# Advanced Agent Evaluation Runner")
    gr.Markdown(
        """
        **Instructions:**
        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:**
        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)
    results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)

    run_button.click(
        fn=run_and_submit_all,
        outputs=[status_output, results_table]
    )

if __name__ == "__main__":
    print("\n" + "-"*30 + " App Starting " + "-"*30)
    space_host_startup = os.getenv("SPACE_HOST")
    space_id_startup = os.getenv("SPACE_ID")

    if space_host_startup:
        print(f"✅ SPACE_HOST found: {space_host_startup}")
        print(f"   Runtime URL should be: https://{space_host_startup}.hf.space")
    else:
        print("ℹ️  SPACE_HOST environment variable not found (running locally?).")

    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?).")

    print("-"*(60 + len(" App Starting ")) + "\n")
    print("Launching Gradio Interface for Advanced Agent Evaluation...")
    demo.launch(debug=True, share=False)