app.py

""" Basic Agent Evaluation Runner"""
import os
import inspect
import gradio as gr
import requests
import pandas as pd
import time
from langchain_core.messages import HumanMessage
from agent import build_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 ------


cached_answers = []

class BasicAgent:
    """A langgraph agent."""
    def __init__(self):
        print("BasicAgent initialized.")
        self.graph = build_graph()

    def __call__(self, question: str) -> str:
        print(f"Agent received question (first 50 chars): {question[:50]}...")
        messages = [HumanMessage(content=question)]
        messages = self.graph.invoke({"messages": messages})
        raw_answer = messages['messages'][-1].content
        if raw_answer.startswith("FINAL ANSWER: "):
            return raw_answer[len("FINAL ANSWER: "):].strip()
        return f"Agent response did not follow FINAL ANSWER format: {raw_answer}"

def run_agent_only(profile: gr.OAuthProfile | None):
    global cached_answers
    cached_answers = []
    results_log = []

    if not profile:
        return "Please login first.", None

    try:
        agent = BasicAgent()
    except Exception as e:
        return f"Agent Init Error: {e}", None

    questions_url = f"{DEFAULT_API_URL}/questions"

    try:
        response = requests.get(questions_url, timeout=15)
        questions_data = response.json()
    except Exception as e:
        return f"Error fetching questions: {e}", None

    with open("system_prompt.txt", "r", encoding="utf-8") as f:
        system_prompt = f.read().strip()

    for item in questions_data:
        task_id = item.get("task_id")
        question = item.get("question")
        file_name = item.get("file_name")

        if not task_id or question is None:
            continue

        try:
            user_message = question
            if file_name:
                user_message += f"\n\nFile to use: {file_name}"

            full_input = system_prompt + "\n\n" + user_message
            answer = agent(full_input)
            cached_answers.append({"task_id": task_id, "submitted_answer": answer})
            results_log.append({"Task ID": task_id, "Question": question, "Submitted Answer": answer})
        except Exception as e:
            results_log.append({"Task ID": task_id, "Question": question, "Submitted Answer": f"AGENT ERROR: {e}"})

    return "Agent finished. Now click 'Submit Cached Answers'", pd.DataFrame(results_log)

def submit_cached_answers(profile: gr.OAuthProfile | None):
    global cached_answers
    if not profile or not cached_answers:
        return "No cached answers to submit. Run the agent first.", None

    space_id = os.getenv("SPACE_ID")
    username = profile.username
    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"

    payload = {
        "username": username,
        "agent_code": agent_code,
        "answers": cached_answers
    }

    submit_url = f"{DEFAULT_API_URL}/submit"

    try:
        response = requests.post(submit_url, json=payload, timeout=60)
        result = response.json()
        final_status = (
            f"Submission Successful!\nUser: {result.get('username')}\n"
            f"Score: {result.get('score', 'N/A')}% ({result.get('correct_count', '?')}/{result.get('total_attempted', '?')})"
        )
        return final_status, None
    except Exception as e:
        return f"Submission failed: {e}", None

# --- Gradio UI ---
with gr.Blocks() as demo:
    gr.Markdown("# Basic Agent Evaluation Runner")
    gr.Markdown("""
    **Instructions:**
    1. Run the Agent to generate answers to all questions.
    2. Then click 'Submit Cached Answers' to submit them for scoring.
    """)

    gr.LoginButton()

    run_button = gr.Button("🧠 Run Agent Only")
    submit_button = gr.Button("📤 Submit Cached 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_agent_only, outputs=[status_output, results_table])
    submit_button.click(fn=submit_cached_answers, 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: https://{space_host_startup}.hf.space")
    else:
        print("ℹ️  No SPACE_HOST found.")

    if space_id_startup:
        print(f"✅ SPACE_ID found: {space_id_startup}")
        print(f"   Repo URL: https://huggingface.co/spaces/{space_id_startup}")
    else:
        print("ℹ️  No SPACE_ID found.")

    print("Launching Gradio Interface...")
    demo.launch(debug=True, share=False)