app.py

# app.py
import os
import gradio as gr
import requests
import pandas as pd
import tempfile
from pathlib import Path

from tools import AnswerTool, SpeechToTextTool, ExcelToTextTool
from smolagents import CodeAgent, OpenAIServerModel
from smolagents import DuckDuckGoSearchTool, WikipediaSearchTool

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


def download_file_if_any(base_api_url: str, task_id: str) -> str | None:
    """
    Try GET /files/{task_id}.
    • On HTTP 200 → save to a temp dir and return local path.
    • On 404 → return None.
    • On other errors → raise so caller can log / handle.
    """
    url = f"{base_api_url}/files/{task_id}"
    try:
        resp = requests.get(url, timeout=30)
        if resp.status_code == 404:
            return None
        resp.raise_for_status()
    except requests.exceptions.HTTPError as e:
        raise e

    # Determine filename from headers or default to task_id
    cd = resp.headers.get("content-disposition", "")
    filename = task_id
    if "filename=" in cd:
        import re
        m = re.search(r'filename="([^"]+)"', cd)
        if m:
            filename = m.group(1)

    # Save to temp dir
    tmp_dir = Path(tempfile.gettempdir()) / "gaia_files"
    tmp_dir.mkdir(exist_ok=True)
    file_path = tmp_dir / filename
    with open(file_path, "wb") as f:
        f.write(resp.content)
    return str(file_path)


class BasicAgent:
    def __init__(self):
        # Initialize CodeAgent with GPT-4o, file/audio/excel, web and wiki tools, plus final answer
        model = OpenAIServerModel(model_id="gpt-4o")
        answer_tool = AnswerTool()
        speech_tool = SpeechToTextTool()
        excel_tool = ExcelToTextTool()
        web_tool = DuckDuckGoSearchTool()
        wiki_tool = WikipediaSearchTool()

        self.agent = CodeAgent(
            model=model,
            tools=[speech_tool, excel_tool, wiki_tool, web_tool, answer_tool],
            add_base_tools=False,
            additional_authorized_imports=["pandas", "openpyxl"],
            max_steps=4,
            planning_interval=1,
            verbosity_level=1
        )

    def __call__(self, task_id: str, question: str) -> str:
        # Pre-fetch any file for this task
        file_path = None
        try:
            file_path = download_file_if_any(DEFAULT_API_URL, task_id)
        except Exception:
            pass

        # Build prompt including file context if any
        if file_path:
            prompt = f"{question}\n\n---\nA file for this task was downloaded and saved at: {file_path}\n---"
        else:
            prompt = question

        return self.agent.run(prompt)


def run_and_submit_all(username):
    if not username:
        return "Please enter your Hugging Face username.", None

    # 1. Fetch questions
    try:
        resp = requests.get(f"{DEFAULT_API_URL}/questions", timeout=15)
        if resp.status_code == 429:
            return "Server rate limited the requests. Please wait a moment and try again.", None
        resp.raise_for_status()
        questions = resp.json()
    except Exception as e:
        return f"Error fetching questions: {e}", None

    # 2. Instantiate agent
    agent = BasicAgent()
    results = []
    payload = []

    # 3. Run agent on all questions
    for q in questions:
        tid = q.get("task_id")
        text = q.get("question")
        if not (tid and text):
            continue
        try:
            ans = agent(tid, text)
        except Exception as e:
            ans = f"ERROR: {e}"
        results.append({"Task ID": tid, "Question": text, "Answer": ans})
        payload.append({"task_id": tid, "submitted_answer": ans})

    if not payload:
        return "Agent returned no answers.", pd.DataFrame(results)

    # 4. Submit answers
    submission = {
        "username": username,
        "agent_code": f"https://huggingface.co/spaces/{os.getenv('SPACE_ID')}/tree/main",
        "answers": payload
    }
    try:
        sub_resp = requests.post(f"{DEFAULT_API_URL}/submit", json=submission, timeout=60)
        sub_resp.raise_for_status()
        data = sub_resp.json()
        status = (
            f"Submission Successful!\n"
            f"User: {data.get('username')}\n"
            f"Score: {data.get('score')}% ({data.get('correct_count')}/{data.get('total_attempted')})\n"
            f"Message: {data.get('message')}"
        )
    except Exception as e:
        status = f"Submission Failed: {e}"

    return status, pd.DataFrame(results)


def test_random_question(username):
    if not username:
        return "Please enter your Hugging Face username.", ""
    try:
        q = requests.get(f"{DEFAULT_API_URL}/random-question", timeout=15).json()
        question = q.get("question", "")
        ans = BasicAgent()(q.get('task_id'), question)
        return question, ans
    except Exception as e:
        return f"Error during test: {e}", ""

# --- Gradio UI ---
with gr.Blocks() as demo:
    gr.Markdown("# Basic Agent Evaluation Runner")
    gr.Markdown(
        """
        **Instructions:**
        1. Enter your Hugging Face username.
        2. Use **Test Random Question** to check a single question.
        3. Use **Run Evaluation & Submit All Answers** to evaluate on all questions.
        """
    )

    username_input = gr.Textbox(label="Hugging Face Username", placeholder="your-username")
    run_btn = gr.Button("Run Evaluation & Submit All Answers")
    test_btn = gr.Button("Test Random Question")

    status_out = gr.Textbox(label="Status / Result", lines=5, interactive=False)
    table_out = gr.DataFrame(label="Full Results Table", wrap=True)
    question_out = gr.Textbox(label="Random Question", lines=3, interactive=False)
    answer_out = gr.Textbox(label="Agent Answer", lines=3, interactive=False)

    run_btn.click(fn=run_and_submit_all, inputs=[username_input], outputs=[status_out, table_out])
    test_btn.click(fn=test_random_question, inputs=[username_input], outputs=[question_out, answer_out])

if __name__ == "__main__":
    demo.launch(debug=True, share=False)