agent.py

import os
import json
from smolagents import create_agent_executor, Agent, Task
from langgraph.prebuilt import ToolExecutor
from huggingface_hub import InferenceClient
from tools import *

# Load Hugging Face endpoint from environment
HF_API_URL = os.getenv("HF_ENDPOINT_URL")  
HF_API_TOKEN = os.getenv("HF_TOKEN") 

if not HF_API_URL or not HF_API_TOKEN:
    raise ValueError("Missing Hugging Face endpoint URL or token.")

llm = InferenceClient(
    model=HF_API_URL,
    token=HF_API_TOKEN
)

def run_llm(prompt: str) -> str:
    response = llm.text_generation(
        prompt,
        max_new_tokens=512,
        do_sample=False,
        temperature=0.0,
        return_full_text=False,
    )
    return response.strip()

tool_list = [
    GetAttachmentTool(),
    GoogleSearchTool(),
    GoogleSiteSearchTool(),
    ContentRetrieverTool(),
    SpeechRecognitionTool(),
    YouTubeVideoTool(),
    ClassifierTool(),
    ImageToChessBoardFENTool()
]

# Create tool executor for LangGraph
tool_executor = ToolExecutor(tool_list)

# Create agent instance
agent = Agent(
    llm=run_llm,
    tools=tool_list,
)

agent_executor = create_agent_executor(
    agent=agent,
    tool_executor=tool_executor,
    stream=False,
)

def load_tasks(metadata_path="metadata.jsonl") -> list[Task]:
    with open(metadata_path, "r") as f:
        tasks = []
        for line in f:
            data = json.loads(line)
            tasks.append(Task(
                task_id=data["question_id"],
                input=data["answer"]  
            ))
        return tasks

def solve_task(task: Task) -> str:
    result = agent_executor.invoke(task.input)
    return result.get("output", "")