app.py

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

from smolagents import ToolCallingAgent, tool
from duckduckgo_search import DDGS
from openai import OpenAI

# Load OpenAI API key
client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))

# ------------------------
# Define Tools
# ------------------------

@tool
def web_search(query: str) -> str:
    """Search the web using DuckDuckGo.

    Args:
        query: The search query to look up.

    Returns:
        A summary of the top web results.
    """
    try:
        with DDGS() as ddgs:
            results = ddgs.text(query, max_results=3)
            if not results:
                return "No results found."
            return "\n\n".join(
                f"Title: {r['title']}\nSnippet: {r['body']}\nURL: {r['href']}"
                for r in results
            )
    except Exception as e:
        return f"Search error: {str(e)}"

@tool
def calculate(expression: str) -> str:
    """Evaluate a mathematical expression.

    Args:
        expression: The math expression to evaluate (e.g. '2 + 3 * 5').

    Returns:
        Result of the calculation.
    """
    try:
        safe_math = {k: v for k, v in math.__dict__.items() if not k.startswith("__")}
        result = eval(expression, {"__builtins__": None}, safe_math)
        return str(result)
    except Exception as e:
        return f"Calculation error: {str(e)}"

# ------------------------
# Define Agent
# ------------------------

class GAIAAgent:
    def __init__(self):
        self.agent = ToolCallingAgent(
            name="GAIA Agent",
            description="""You are an AI assistant that answers questions using tools:
- Use 'web_search' for looking up facts and recent information.
- Use 'calculate' for evaluating math expressions.
Be accurate and concise.""",
            tools=[web_search, calculate],
            model=client.chat.completions
        )

    def __call__(self, question: str) -> str:
        try:
            response = self.agent.run(question)
            return str(response)
        except Exception as e:
            return f"Agent error: {str(e)}"

# ------------------------
# Gradio App Logic
# ------------------------

def run_agent_and_submit(profile: gr.OAuthProfile | None):
    if not profile:
        return "⚠️ Please log in to Hugging Face.", None

    try:
        agent = GAIAAgent()
        response = requests.get("https://agents-course-unit4-scoring.hf.space/questions", timeout=20)
        questions = response.json()
    except Exception as e:
        return f"❌ Error fetching questions: {e}", None

    results = []
    answers = []

    for q in questions:
        task_id = q.get("task_id")
        question_text = q.get("question")
        if not task_id or not question_text:
            continue
        try:
            answer = agent(question_text)
        except Exception as e:
            answer = f"Agent error: {e}"

        answers.append({
            "task_id": task_id,
            "submitted_answer": answer[:1000]
        })
        results.append({
            "Task ID": task_id,
            "Question": question_text,
            "Answer": answer
        })

    # Submit answers
    try:
        submit_url = "https://agents-course-unit4-scoring.hf.space/submit"
        payload = {
            "username": profile.username,
            "agent_code": f"https://huggingface.co/spaces/{os.getenv('SPACE_ID')}/tree/main",
            "answers": answers
        }
        submit_resp = requests.post(submit_url, json=payload, timeout=60)
        result_data = submit_resp.json()
        summary = (
            f"✅ Submitted {len(answers)} answers\n"
            f"📊 Score: {result_data.get('score', 'N/A')}%\n"
            f"✔️ Correct: {result_data.get('correct_count', '?')}/{len(answers)}"
        )
    except Exception as e:
        summary = f"❌ Submission error: {e}"

    return summary, pd.DataFrame(results)

# ------------------------
# Gradio Interface
# ------------------------

with gr.Blocks() as demo:
    gr.Markdown("# 🤖 GAIA Tool Agent")
    gr.Markdown("This agent answers GAIA benchmark questions using tool-calling with search and math.")
    gr.LoginButton()
    run_btn = gr.Button("🔍 Run Agent & Submit")
    status = gr.Textbox(label="Status", lines=4)
    results_df = gr.DataFrame(label="Results")

    run_btn.click(fn=run_agent_and_submit, outputs=[status, results_df])

if __name__ == "__main__":
    demo.launch()