app.py

import os
import gradio as gr
import requests
import pandas as pd

from langchain_groq import ChatGroq
from langchain_core.tools import tool
from langchain_core.messages import HumanMessage, SystemMessage
from langchain_community.tools import DuckDuckGoSearchRun
from langchain_community.tools import WikipediaQueryRun
from langchain_community.utilities import WikipediaAPIWrapper
from langgraph.prebuilt import create_react_agent

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

# --- System prompt: this is what enforces the EXACT-MATCH formatting ---
SYSTEM_PROMPT = """You are a precise assistant solving questions from the GAIA benchmark.
You have tools: web search, Wikipedia, and a file downloader. Use them to find facts; reason step by step.

Your FINAL message must contain ONLY the answer itself — no explanation, no preamble.
The grader does an EXACT string match, so follow these rules strictly:
- Never write "FINAL ANSWER" or any extra words in the final message.
- If the answer is a number: digits only, no thousands separators, no units (unless the question explicitly asks for a unit).
- If the answer is text: as few words as possible, no leading articles, no trailing period, no abbreviations unless required by the question.
- If the answer is a comma-separated list: apply the rules above to each element.
"""


# --- Tools ---
search_tool = DuckDuckGoSearchRun()
wiki_tool = WikipediaQueryRun(api_wrapper=WikipediaAPIWrapper(top_k_results=2, doc_content_chars_max=3000))


@tool
def fetch_task_file(task_id: str) -> str:
    """Download the file attached to a GAIA task by its task_id and return its text content.
    Use this only when the question refers to an attached file (a spreadsheet, csv, text file, code, etc.)."""
    url = f"{DEFAULT_API_URL}/files/{task_id}"
    try:
        r = requests.get(url, timeout=30)
        r.raise_for_status()
    except Exception as e:
        return f"Could not download file for task {task_id}: {e}"

    content_type = r.headers.get("content-type", "").lower()
    # Spreadsheets
    if "spreadsheet" in content_type or "excel" in content_type or url.endswith((".xlsx", ".xls")):
        try:
            import io
            df = pd.read_excel(io.BytesIO(r.content))
            return f"Spreadsheet contents:\n{df.to_string()}"
        except Exception as e:
            return f"Got a spreadsheet but failed to parse it: {e}"
    # CSV
    if "csv" in content_type or url.endswith(".csv"):
        try:
            import io
            df = pd.read_csv(io.BytesIO(r.content))
            return f"CSV contents:\n{df.to_string()}"
        except Exception as e:
            return f"Got a CSV but failed to parse it: {e}"
    # Try plain text
    try:
        text = r.content.decode("utf-8")
        return f"File contents:\n{text[:5000]}"
    except Exception:
        return (f"The attached file is binary ({content_type}, {len(r.content)} bytes) "
                f"and cannot be read as text in this version of the agent.")


TOOLS = [search_tool, wiki_tool, fetch_task_file]


# --- Agent ---
class BasicAgent:
    def __init__(self):
        api_key = os.getenv("GROQ_API_KEY")
        if not api_key:
            raise ValueError("GROQ_API_KEY is not set. Add it in Settings -> Variables and secrets.")
        llm = ChatGroq(model="llama-3.3-70b-versatile", temperature=0, api_key=api_key)
        self.graph = create_react_agent(llm, TOOLS)
        print("LangGraph agent initialized.")

    def __call__(self, question: str, task_id: str | None = None) -> str:
        print(f"Agent received question (first 60 chars): {question[:60]}...")
        user_content = question
        if task_id:
            user_content += (f"\n\n[If this question refers to an attached file, "
                             f"call fetch_task_file with task_id='{task_id}'.]")
        messages = [SystemMessage(content=SYSTEM_PROMPT), HumanMessage(content=user_content)]
        try:
            result = self.graph.invoke({"messages": messages}, config={"recursion_limit": 25})
            answer = result["messages"][-1].content.strip()
        except Exception as e:
            print(f"Agent error: {e}")
            return f"AGENT ERROR: {e}"
        # Safety net: strip a stray "FINAL ANSWER" prefix if the model added one
        if answer.upper().startswith("FINAL ANSWER"):
            answer = answer.split(":", 1)[-1].strip()
        print(f"Agent answer: {answer[:100]}")
        return answer


def run_and_submit_all(profile: gr.OAuthProfile | None):
    """Fetches all questions, runs the agent, submits answers, and shows results."""
    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 = 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 Exception as e:
        print(f"Error fetching questions: {e}")
        return f"Error fetching questions: {e}", None

    # 3. Run 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)
            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}
    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.")
        return final_status, pd.DataFrame(results_log)
    except requests.exceptions.HTTPError as e:
        error_detail = f"Server responded with status {e.response.status_code}."
        try:
            error_detail += f" Detail: {e.response.json().get('detail', e.response.text)}"
        except requests.exceptions.JSONDecodeError:
            error_detail += f" Response: {e.response.text[:500]}"
        print(f"Submission Failed: {error_detail}")
        return f"Submission Failed: {error_detail}", pd.DataFrame(results_log)
    except Exception as e:
        print(f"An unexpected error occurred during submission: {e}")
        return f"An unexpected error occurred during submission: {e}", pd.DataFrame(results_log)


# --- Gradio Interface ---
with gr.Blocks() as demo:
    gr.Markdown("# Basic Agent Evaluation Runner")
    gr.Markdown(
        """
        **Instructions:**

        1. Log in to your Hugging Face account using the button below.
        2. Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, and see the score.

        ---
        Note: a full run takes a while (the agent goes through all questions one by one).
        """
    )

    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}")
    else:
        print("ℹ️  SPACE_HOST not found (running locally?).")

    if space_id_startup:
        print(f"✅ SPACE_ID found: {space_id_startup}")
    else:
        print("ℹ️  SPACE_ID not found (running locally?).")

    print("-" * 74 + "\n")
    print("Launching Gradio Interface for Basic Agent Evaluation...")
    demo.launch(debug=True, share=False, ssr_mode=False)