quick_specific_agent_test.py

import os
import sys
import tempfile
import requests
import asyncio
from dotenv import load_dotenv

# Load environment variables
load_dotenv()

# Add the current directory to Python path
sys.path.append(os.path.dirname(os.path.abspath(__file__)))

# Import the latest agent system
from langgraph_agent_system import run_agent_system
from observability import flush_traces, shutdown_observability

# Default API URL and Task ID - Using the same URL as the original basic_agent.py
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
DEFAULT_TASK_ID = "f918266a-b3e0-4914-865d-4faa564f1aef"

def fetch_question_by_id(task_id: str, api_base: str = DEFAULT_API_URL):
    """Return JSON of a question for a given task_id.

    The scoring API does not (yet) expose an explicit /question/{id} endpoint,
    so we fetch the full /questions list and filter locally. This works fine
    because the list is small (<100 items).
    """
    try:
        resp = requests.get(f"{api_base}/questions", timeout=30)
        resp.raise_for_status()
        questions = resp.json()
    except Exception as e:
        raise RuntimeError(f"Failed to fetch questions list: {e}") from e

    for q in questions:
        if str(q.get("task_id")) == str(task_id):
            return q

    raise ValueError(f"Task ID {task_id} not found in /questions list.")


def maybe_download_file(task_id: str, api_base: str = DEFAULT_API_URL) -> str | None:
    """Try to download the file associated with a given task id. Returns local path or None."""
    url = f"{api_base}/files/{task_id}"
    try:
        resp = requests.get(url, timeout=60)
        if resp.status_code != 200:
            print(f"No file associated with task {task_id} (status {resp.status_code}).")
            return None
        # Create temp file with same name from headers if available
        filename = resp.headers.get("content-disposition", "").split("filename=")[-1].strip("\"") or f"{task_id}_attachment"
        tmp_path = os.path.join(tempfile.gettempdir(), filename)
        with open(tmp_path, "wb") as f:
            f.write(resp.content)
        print(f"Downloaded attachment to {tmp_path}")
        return tmp_path
    except requests.HTTPError as e:
        print(f"Could not download file for task {task_id}: {e}")
    except Exception as e:
        print(f"Error downloading file: {e}")
    return None


async def main():
    print("Specific Agent Test - Latest LangGraph Multi-Agent System")
    print("=" * 60)
    
    try:
        # Determine the task ID (CLI arg > env var > default)
        task_id = (
            sys.argv[1] if len(sys.argv) > 1 else os.environ.get("TASK_ID", DEFAULT_TASK_ID)
        )
        print(f"Using task ID: {task_id}")

        # Fetch specific question
        q = fetch_question_by_id(task_id)
        question_text = q["question"]

        print("\n=== Specific Question ===")
        print(f"Task ID : {task_id}")
        print(f"Question: {question_text}")

        # Attempt to get attachment if any
        attachment_path = maybe_download_file(task_id)
        if attachment_path:
            question_text += f"\n\nAttachment available at: {attachment_path}"

        # Run the latest agent system
        print("\n=== Running Latest LangGraph Multi-Agent System ===")
        
        result = await run_agent_system(
            query=question_text,
            user_id="test_user",
            session_id=f"session_{task_id}"
        )
        
        print("\n=== Agent Answer ===")
        print(result)
        
    except Exception as e:
        print(f"Error in main execution: {e}")
        import traceback
        traceback.print_exc()
    
    finally:
        # Cleanup
        try:
            flush_traces(background=False)
            shutdown_observability()
            print("\n✅ Agent cleanup completed")
        except Exception as e:
            print(f"⚠️ Cleanup warning: {e}")


if __name__ == "__main__":
    asyncio.run(main())