app.py

import os
import gradio as gr
import requests
import inspect
import pandas as pd
# from agents import LlamaIndexAgent
from langgraph_agent_system import run_agent_system  # Updated: use the latest multi-agent system
from observability import flush_traces, shutdown_observability  # Add cleanup functions
import asyncio
import aiohttp
from langfuse.langchain import CallbackHandler
from langchain_core.messages import HumanMessage
import tempfile
import uuid

# Initialize Langfuse CallbackHandler for LangGraph/Langchain (tracing)
langfuse_handler = CallbackHandler()

# (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:
    """Wrapper that executes the latest multi-agent LangGraph system."""

    def __init__(self):
        print("BasicAgent (latest multi-agent system) initialized.")

    async def aquery(self, question: str) -> str:
        """Run the latest async multi-agent system directly."""
        print(f"Agent received question (first 50 chars): {question[:50]}...")
        try:
            # Generate unique session ID for this query
            session_id = f"app_session_{uuid.uuid4().hex[:8]}"
            
            # Call the latest async agent system directly
            answer = await run_agent_system(
                query=question,
                user_id="gradio_app_user",
                session_id=session_id,
                max_iterations=3
            )
            print(f"Agent returning answer: {answer}")
            return answer
        except Exception as e:
            print(f"Exception in aquery: {e}")
            return f"AGENT ERROR: {e}"

# 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 asynchronously, and returns the answers and log.
    """
    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,
            gr.update(interactive=False),  # Disable submit button
            gr.update(value=None, visible=False),  # Hide download button
        )
    api_url = DEFAULT_API_URL
    questions_url = f"{api_url}/questions"
    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,
                gr.update(interactive=False),
                gr.update(value=None, visible=False),
            )
        print(f"Fetched {len(questions_data)} questions.")
    except Exception as e:
        print(f"Error fetching questions: {e}")
        return (
            f"Error fetching questions: {e}",
            None,
            gr.update(interactive=False),
            gr.update(value=None, visible=False),
        )
    agent = BasicAgent()
    results_log = []
    answers_payload = []
    cached_questions = questions_data
    total = len(questions_data)
    progress(0, desc="Starting answer generation...")
    semaphore = asyncio.Semaphore(3)  # Limit concurrency to 3
    async def answer_one(item):
        async with semaphore:
            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}")
                return {"Task ID": task_id, "Question": question_text, "Submitted Answer": "SKIPPED"}, None
            try:
                submitted_answer = await agent.aquery(question_text)
                # Ensure consistent data types for payload
                safe_task_id = str(task_id)
                safe_answer = str(submitted_answer)
                return {"Task ID": safe_task_id, "Question": question_text, "Submitted Answer": safe_answer}, {"task_id": safe_task_id, "submitted_answer": safe_answer}
            except Exception as e:
                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)

    # Save answers to a temporary CSV so user can download
    try:
        with tempfile.NamedTemporaryFile(delete=False, suffix=".csv", mode="w", newline="", encoding="utf-8") as tmp_csv:
            results_df.to_csv(tmp_csv.name, index=False)
            csv_path = tmp_csv.name
            print(f"CSV saved to {csv_path}")
    except Exception as e:
        print(f"Failed to write CSV: {e}")
        csv_path = None

    return (
        "Answer generation complete. Review and submit.",
        results_df,
        gr.update(interactive=True),  # Enable submit button
        gr.update(value=csv_path, visible=bool(csv_path)),  # Show download button if csv written
    )

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"
    # Detailed logging for the submission payload (only first 3 answers to avoid clutter)
    preview_answers = cached_answers[:3] if cached_answers else []
    print("\n--- Submission Payload Preview ---")
    print(f"Username: {username.strip()}")
    print(f"Agent Code: {agent_code}")
    print(f"Total Answers: {len(cached_answers)}")
    print(f"First Answers Sample: {preview_answers}")
    print("----------------------------------\n")

    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()
        print(f"Submit endpoint status code: {response.status_code}")
        print(f"Raw response text: {response.text[:500]}")
        # Attempt to parse JSON regardless of status for troubleshooting
        try:
            result_data = response.json()
        except Exception as json_err:
            print(f"Error parsing JSON response: {json_err}")
            raise
        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.')}"
        )
        results_df = pd.DataFrame(cached_results_log)
        return final_status, results_df
    except Exception as e:
        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:
    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 'Generate Answers' to fetch questions and run your agent. Review the answers, then click 'Submit Answers' to submit them and see your score.

        ---
        **Disclaimers:**
        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()

    with gr.Row():
        generate_button = gr.Button("Generate Answers")
        submit_button = gr.Button("Submit Answers", interactive=False)

    status_output = gr.Textbox(label="Status / Submission Result", lines=5, interactive=False)
    results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)

    # Download button appears after answers are generated
    download_button = gr.DownloadButton(label="Download Answers CSV", visible=False)

    generate_button.click(
        fn=generate_answers,
        inputs=[],
        outputs=[status_output, results_table, submit_button, download_button],
        api_name="generate_answers"
    )
    submit_button.click(
        fn=submit_answers,
        inputs=[],
        outputs=[status_output, results_table],
        api_name="submit_answers",
    )

def cleanup_agent_system():
    """Cleanup function for the agent system."""
    try:
        flush_traces(background=False)
        shutdown_observability()
        print("✅ Agent system cleanup completed")
    except Exception as e:
        print(f"⚠️ Cleanup warning: {e}")

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}.hf.space")
    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 with Latest Multi-Agent System...")
    print("🤖 Using: LangGraph Multi-Agent System (Lead → Research → Code → Formatter)")
    print("📊 Features: Langfuse v3 observability, iterative workflows, GAIA compliance")
    
    try:
        demo.launch(debug=True, share=False)
    except KeyboardInterrupt:
        print("\n🛑 Shutting down gracefully...")
        cleanup_agent_system()
    except Exception as e:
        print(f"❌ Error during app execution: {e}")
        cleanup_agent_system()
        raise