agent.py

# agent.py — full GAIA-ready agent with tools for web, audio, Excel, Python

import os
import asyncio
from llama_index.llms.openai import OpenAI
from llama_index.core.agent.react.base import ReActAgent
from llama_index.core.tools import FunctionTool

from langchain_community.tools.wikipedia.tool import WikipediaTool
from langchain_experimental.tools.python.tool import PythonREPLTool
from langchain_community.document_loaders import YoutubeLoader

import openai_whisper as whisper
import openpyxl

# Confirm OpenAI API key
if os.getenv("OPENAI_API_KEY"):
    print("✅ Detected OPENAI_API_KEY in environment")
else:
    print("⚠️  Missing OPENAI_API_KEY — LLM may not work")

# --- Web tools ---
def wikipedia_search(query: str) -> str:
    return WikipediaTool().run(query)

# --- Python with output ---
def run_python_with_output(code: str) -> str:
    try:
        if "print(" not in code:
            code = f"print({code})" if not code.strip().endswith("\n") else f"print({code.strip()})"
        return PythonREPLTool().run(code)
    except Exception as e:
        return f"[PYTHON ERROR] {e}"

# --- YouTube (fallback placeholder) ---
def get_youtube_transcript(url: str) -> str:
    try:
        loader = YoutubeLoader.from_youtube_url(url, add_video_info=False)
        docs = loader.load()
        return " ".join(doc.page_content for doc in docs)
    except Exception as e:
        return f"[YOUTUBE ERROR] {e}"

# --- Whisper transcription ---
def transcribe_audio(file_path: str) -> str:
    try:
        model = whisper.load_model("base")
        result = model.transcribe(file_path)
        return result['text']
    except Exception as e:
        return f"[AUDIO ERROR] {e}"

# --- Excel sales extraction ---
def extract_excel_total_food_sales(file_path: str) -> str:
    try:
        wb = openpyxl.load_workbook(file_path)
        sheet = wb.active
        total = 0
        for row in sheet.iter_rows(min_row=2, values_only=True):
            category, amount = row[1], row[2]
            if isinstance(category, str) and 'food' in category.lower():
                total += float(amount)
        return f"${total:.2f}"
    except Exception as e:
        return f"[EXCEL ERROR] {e}"

# --- Tool list ---
TOOLS = [
    FunctionTool.from_defaults(wikipedia_search),
    FunctionTool.from_defaults(run_python_with_output),
    FunctionTool.from_defaults(get_youtube_transcript),
    FunctionTool.from_defaults(transcribe_audio),
    FunctionTool.from_defaults(extract_excel_total_food_sales),
]

# --- LLM and Agent ---
llm = OpenAI(model="gpt-4")

agent = ReActAgent.from_tools(
    tools=TOOLS,
    llm=llm,
    verbose=True,
    system_prompt="""
You are an expert AI assistant participating in the GAIA benchmark.

Your goal is to answer 20 diverse questions using available tools:
- Wikipedia search
- Python code runner
- YouTube transcript
- MP3 transcription (Whisper)
- Excel analysis

Rules:
1. Output only the FINAL answer. No explanations.
2. Format must match expected output exactly: comma-separated lists, plain names, numeric values, algebraic notation.
3. Use tools smartly. Don't guess when tools can help.
4. If tools fail (e.g., YouTube blocked), say clearly: "Tool not available".
"""
)

# --- Run function ---
def answer_question_sync(question: str) -> str:
    try:
        response = agent.chat(question)
        if hasattr(response, "response") and hasattr(response.response, "content"):
            return response.response.content.strip()
        elif isinstance(response, str):
            return response.strip()
        else:
            return str(response)
    except Exception as e:
        print(f"❌ Exception while answering: {e}")
        return f"[ERROR] {e}"

async def answer_question(question: str) -> str:
    return answer_question_sync(question)