# app.py
import asyncio
import gradio as gr
from typing import List, Tuple
from agents import FileSearchTool, Agent, ModelSettings, TResponseInputItem, Runner, RunConfig, trace
import os
from dotenv import load_dotenv

load_dotenv()

VECTOR_STORE_ID = os.getenv("VECTOR_STORE_ID")
WORKFLOW_ID = os.getenv("WORKFLOW_ID")

# --- Tools & Agent setup ---
file_search = FileSearchTool(
    vector_store_ids=[VECTOR_STORE_ID]
)

my_agent = Agent(
    name="My agent",
    instructions=(
        "Ante la pregunta del alumno sobre su calendario academico, consulta la herramienta de reglamentos_upn y por favor respondele como si fueras Yoda. "
    ),
    model="gpt-4.1-mini",
    tools=[file_search],
    model_settings=ModelSettings(
        temperature=1,
        top_p=1,
        max_tokens=2048,
        store=True
    )
)

def _to_items_from_history(history: List[Tuple[str, str]]) -> List[TResponseInputItem]:
    """Convert Gradio chat history to Agent format."""
    items: List[TResponseInputItem] = []
    for user_msg, assistant_msg in history:
        if user_msg:
            items.append({
                "role": "user",
                "content": [{"type": "input_text", "text": user_msg}],
            })
        if assistant_msg:
            items.append({
                "role": "assistant",
                "content": [{"type": "output_text", "text": assistant_msg}],
            })
    return items

async def agent_reply(message: str, history: List[Tuple[str, str]]):
    """Handler for Gradio ChatInterface."""
    conversation_items = _to_items_from_history(history)
    conversation_items.append({
        "role": "user",
        "content": [{"type": "input_text", "text": message}],
    })
    
    with trace("Gradio message"):
        result = await Runner.run(
            my_agent,
            input=conversation_items,
            run_config=RunConfig(trace_metadata={
                "__trace_source__": "agent-builder",
                "workflow_id": WORKFLOW_ID
            })
        )
    
    return result.final_output_as(str)

# --- Updated Gradio ChatInterface ---
demo = gr.ChatInterface(
    fn=agent_reply,
    title="My agent (FileSearch-powered)",
    description=(
        "Este chat usa un agente con FileSearchTool. "
        "Escribe tu pregunta y el agente buscará primero en el file search antes de responder."
    ),
    examples=["¿Qué me puedes contar del reglamento?", "¿Puedo copiar"],
    theme="soft",
    submit_btn="Enviar"
    # Removed clear_btn as it's not a valid parameter
)

# If you need to run the app
if __name__ == "__main__":
    demo.launch()