import streamlit as st
from utils import Recorder, record_audio, play_mp3
import os
import requests
import ast
import json
from openai import OpenAI
import random
from dotenv import load_dotenv
import time

load_dotenv()
api_key = os.getenv("OPENAI_API_KEY")

# st.title("PinocchioLand!")

# # Display an image
# st.image(
#     "https://i.pinimg.com/736x/30/e9/36/30e936e18912e9a5670b88ec94630b4a.jpg",
#     use_column_width=True,
# )


client = OpenAI(api_key=api_key)

st.title("Chat with Pinocchio")

if "count1" not in st.session_state:
    st.session_state.count1 = 0

if "openai_model" not in st.session_state:
    st.session_state["openai_model"] = "gpt-3.5-turbo"
if "messages" not in st.session_state:
    st.session_state.messages = []

for message in st.session_state.messages:
    with st.chat_message(message["role"]):
        st.markdown(message["content"])

n = len(st.session_state.messages)

if prompt := st.chat_input("What is up?"):
    st.session_state.messages.append({"role": "user", "content": prompt})
    with st.chat_message("user"):
        st.markdown(prompt)

    with st.chat_message("assistant"):
        # LLM
        url = "http://localhost:8000/llm_query/"
        # Append the query parameter to the URL
        llm_response = requests.post(url=url, params={"llm_query": str(prompt)})
        data = llm_response.json()
        inner_data = json.loads(data["response_text"])

        # Now, you can access the data from the inner JSON
        risposta = inner_data.get("risposta")
        stato = inner_data.get("stato")
        tipologia = inner_data.get("tipologia")
        st.write(inner_data)
        st.session_state.messages.append({"role": "assistant", "content": inner_data})
        # st.session_state.count1 += 1