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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
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