app.py

import os
import json
from pathlib import Path

import gradio as gr
import requests
import pandas as pd
from langchain_core.messages import HumanMessage

from load_data import (
    ensure_validation_data,
    get_file_from_gaia_level1_data,
    get_question,
)
from graph import react_graph


# (Keep Constants as is)
# --- Constants ---
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"


# --- 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 _invoke_react_graph(task_id: str) -> str:
    """
    Invokes the react graph with the given task_id and returns the final answer.
    """
    input_file = get_file_from_gaia_level1_data(task_id)
    question = get_question(task_id)
    print(
        f"Invoking react graph for task_id={task_id} with question: {question[:50]}... and input_file: {input_file}"
    )

    messages = [HumanMessage(content=question)]

    messages = react_graph.invoke(
        {"messages": messages, "input_file": input_file},
        config={"recursion_limit": 100},
    )

    final_message = messages["messages"][-1]
    print(f"Final message from react graph: {final_message.content[:100]}...")

    # Extract the final answer from the message content
    final_answer_prefix = "FINAL ANSWER:"
    if final_answer_prefix in final_message.content:
        final_answer = final_message.content.split(final_answer_prefix)[-1].strip()
        print(f"Extracted final answer: {final_answer}")
        return final_answer
    else:
        print(
            f"Warning: 'FINAL ANSWER:' prefix not found in react graph output. Returning full message content as answer."
        )
        return final_message.content.strip()


def run_and_submit_all(profile: gr.OAuthProfile | None):
    """
    Fetches all questions, runs the BasicAgent 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

    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 ( modify this part to create your agent)
    # 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. Load Questions (cache first, API fallback)
    cache_path = Path(__file__).resolve().parent / "cached_questions.json"
    questions_data = None

    # 2.a Try cache first
    if cache_path.exists():
        try:
            with open(cache_path, "r", encoding="utf-8") as f:
                cached = json.load(f)
            if isinstance(cached, list) and cached:
                questions_data = cached
                print(
                    f"Loaded {len(questions_data)} questions from cache: {cache_path}"
                )
            else:
                print(f"Cache file found but empty/invalid format: {cache_path}")
        except json.JSONDecodeError as e:
            print(f"Cache JSON is invalid ({cache_path}): {e}. Falling back to API.")
        except OSError as e:
            print(
                f"Could not read cache file ({cache_path}): {e}. Falling back to API."
            )

    # 2.b Fetch from API only if cache missing/invalid/empty
    if questions_data is None:
        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 isinstance(questions_data, list) or not questions_data:
                print("Fetched questions list is empty or invalid format.")
                return "Fetched questions list is empty or invalid format.", None

            print(f"Fetched {len(questions_data)} questions from API.")

            # Save cache for next runs
            try:
                with open(cache_path, "w", encoding="utf-8") as f:
                    json.dump(questions_data, f, ensure_ascii=False, indent=2)
                print(f"Questions cached to: {cache_path}")
            except OSError as e:
                print(f"Warning: unable to write cache file ({cache_path}): {e}")

        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.c Retrieve the data files provided for the test ( in the case of the test on Hugging Face, the files are in data/2023_level1/validation/)
    base_dir = Path(__file__).resolve().parent
    ok, error_message = ensure_validation_data(base_dir)
    if not ok:
        return error_message, None

    # 3. Run your Agent (answers cache by task_id)
    results_log = []
    answers_payload = []

    answers_cache_path = Path(__file__).resolve().parent / "cached_answers.json"
    answers_cache = {}

    # 3.a Load answers cache
    if answers_cache_path.exists():
        try:
            with open(answers_cache_path, "r", encoding="utf-8") as f:
                loaded_cache = json.load(f)
            if isinstance(loaded_cache, dict):
                answers_cache = loaded_cache
                print(
                    f"Loaded {len(answers_cache)} cached answers from: {answers_cache_path}"
                )
            else:
                print(
                    f"Answers cache has invalid format (expected object): {answers_cache_path}"
                )
        except json.JSONDecodeError as e:
            print(
                f"Answers cache JSON is invalid ({answers_cache_path}): {e}. Starting with empty cache."
            )
        except OSError as e:
            print(
                f"Could not read answers cache ({answers_cache_path}): {e}. Starting with empty cache."
            )

    cache_updated = False

    print(f"Running agent on {len(questions_data)} questions...")
    for item in questions_data:
        task_id = item.get("task_id")
        # task_id = "a1e91b78-d3d8-4675-bb8d-62741b4b68a6"  # TEMPORARY HARDCODED TASK_ID FOR TESTING
        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

        task_key = str(task_id)

        # Use cached answer if available
        if task_key in answers_cache:
            submitted_answer = answers_cache[task_key]
            print(f"Using cached answer for task_id={task_id}")
        else:
            try:
                submitted_answer = _invoke_react_graph(task_key)
                answers_cache[task_key] = submitted_answer
                cache_updated = True
                print(f"Computed and cached answer for task_id={task_id}")
            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}",
                    }
                )
                continue

        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,
            }
        )

    # 3.b Save answers cache only if updated
    if cache_updated:
        try:
            with open(answers_cache_path, "w", encoding="utf-8") as f:
                json.dump(answers_cache, f, ensure_ascii=False, indent=2)
            print(f"Answers cache updated: {answers_cache_path}")
        except OSError as e:
            print(f"Warning: unable to write answers cache ({answers_cache_path}): {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}")
    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


# --- Build Gradio Interface using Blocks ---
with gr.Blocks() as demo:
    gr.Markdown("# Basic 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.

        ---
        **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.
        """
    )

    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(fn=run_and_submit_all, outputs=[status_output, results_table])

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

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

    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"
        )
    else:
        print(
            "ℹ️  SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined."
        )

    print("-" * (60 + len(" App Starting ")) + "\n")

    print("Launching Gradio Interface for Basic Agent Evaluation...")
    demo.launch(debug=True, share=False, ssr_mode=False)