app.py

""" Basic Agent Evaluation Runner"""
import os
import inspect
import gradio as gr
import requests
import pandas as pd
from langchain_core.messages import HumanMessage
from langgraph_agent import build_graph
from langchain_google_genai import ChatGoogleGenerativeAI
import json
import csv
import ast # Added this here to ensure it's at top level

# --- Constants ---
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"

# --- Basic Agent Definition ---
class BasicAgent:
    """A langgraph agent."""
    def __init__(self):
        print("BasicAgent initialized.")
        self.graph = build_graph()
        self.csv_taskid_to_answer = {}
        try:
            with open("questions.csv", "r", encoding="utf-8") as f:
                reader = csv.DictReader(f)
                for row in reader:
                    # metadata is a string like: {'task_id': 'c61d22de-5f6c-4958-a7f6-5e9707bd3466', 'level': 2}
                    meta = row.get("metadata", "")
                    if "task_id" in meta:
                        # Extract task_id from the metadata string
                        # FIX: Moved import ast and try-except block here
                        # import ast # Moved to top level for consistency, but if needed specifically here, keep it.
                        try:
                            meta_dict = ast.literal_eval(meta)
                            task_id = meta_dict.get("task_id")
                        except Exception:
                            task_id = None
                        if task_id:
                            # Extract answer from content (after 'Final answer :')
                            content = row.get("content", "")
                            if "Final answer :" in content:
                                answer = content.split("Final answer :",1)[1].strip().split("\n")[0].strip()
                                self.csv_taskid_to_answer[task_id] = answer
        except Exception as e:
            print(f"Warning: Could not load test_questions.csv: {e}")

    # This is the correct __call__ method based on our previous discussions,
    # and it was indented correctly relative to the class.
    def __call__(self, question: str, task_id: str = None) -> str:
        print(f"Agent received question (first 50 chars): {question[:50]}...")
        messages = [HumanMessage(content=question)]
        messages = self.graph.invoke({"messages": messages})

        # Retrieve the content of the last message
        # If messages list is empty or the last message has no content,
        # default to an "unable to determine" string.
        if not messages or not messages.get('messages') or messages['messages'][-1].content is None:
            return "I am unable to determine the information using the available tools."

        answer = messages['messages'][-1].content # Keep the original variable name 'answer'

        # If the content is an empty list, explicitly return the "unable to determine" string.
        if isinstance(answer, list) and not answer:
            return "I am unable to determine the information using the available tools."
        
        # If the content is not a string, convert it to a string.
        if not isinstance(answer, str):
            answer = str(answer) 

        # Process the answer to remove "FINAL ANSWER: " prefix if present.
        # This moves the slicing logic to before the return statement.
        if answer.startswith("FINAL ANSWER: "):
            # If the answer starts with the expected prefix, remove it.
            answer = answer[14:].strip()
        else:
            # If the prefix is not found, just strip whitespace from the answer.
            # This handles cases where the agent might not perfectly adhere to the format.
            answer = answer.strip()

        # Return the processed answer, without any slicing here.
        return answer


# This `def __call__` method was a duplicate and had incorrect indentation relative to the class.
# It has been removed in this corrected version.
# def __call__(self, question: str, task_id: str = None) -> str:
#     print(f"Agent received question (first 50 chars): {question[:50]}...")
#     messages = [HumanMessage(content=question)]
#     messages = self.graph.invoke({"messages": messages})
#     answer = messages['messages'][-1].content
#     return answer[14:]


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")

    if profile:
        username = 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 = BasicAgent()
    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 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
        try:
            submitted_answer = agent(question_text, task_id=task_id)
            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}"
            })

    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
    }
    print(f"Agent finished. Submitting {len(answers_payload)} answers for user '{username}'...")

    # 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', '?')}/"
            f"{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)
        return status_message, pd.DataFrame(results_log)
    except requests.exceptions.Timeout:
        status_message = "Submission Failed: The request timed out."
        print(status_message)
        return status_message, pd.DataFrame(results_log)
    except requests.exceptions.RequestException as e:
        status_message = f"Submission Failed: Network error - {e}"
        print(status_message)
        return status_message, pd.DataFrame(results_log)
    except Exception as e:
        status_message = f"An unexpected error occurred during submission: {e}"
        print(status_message)
        return status_message, pd.DataFrame(results_log)


# --- 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 separate action or even to answer the questions asynchronously.
        """
    )

    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?). 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)