import streamlit as st
import openai
import os
import json
import requests
from datetime import datetime
from collections import deque
from openai import ChatCompletion
from audio_recorder_streamlit import audio_recorder

# Initialize configurations
configurations = {}
config_file = "configurations.json"
if os.path.exists(config_file):
    with open(config_file, "r") as file:
        configurations = json.load(file)

openai.api_key = os.getenv('OPENAI_KEY')
st.set_page_config(page_title="GPT Streamlit Document Reasoner", layout="wide")

model_choice = st.sidebar.radio("Select Model:", ('gpt-3.5-turbo', 'gpt-3.5-turbo-0301'))

user_prompt = st.text_area(
    "Enter prompts, instructions & questions:",
    configurations.get("user_prompt", ""),
    height=100
)
system_prompt = configurations.get("system_prompt", "You are a helpful assistant.")

def generate_filename(prompt, file_type):
    safe_date_time = datetime.now().strftime("%m%d_%I%M")
    safe_prompt = "".join(x for x in prompt if x.isalnum())[:45]
    return f"{safe_date_time}_{safe_prompt}.{file_type}"

def chat_with_model(prompt, document_section):
    conversation = [{'role': 'system', 'content': system_prompt}]
    conversation.append({'role': 'user', 'content': prompt})
    if document_section:
        conversation.append({'role': 'assistant', 'content': document_section})
    response = openai.ChatCompletion.create(model=model_choice, messages=conversation)
    return response

def save_and_play_audio():
    audio_bytes = audio_recorder()
    if audio_bytes:
        filename = generate_filename("Recording", "wav")
        with open(filename, 'wb') as f:
            f.write(audio_bytes)
        st.audio(audio_bytes, format="audio/wav")
        return filename
    return None

def create_file(filename, prompt, response):
    with open(filename, 'w') as file:
        file.write(f"Prompt:\n{prompt}\nResponse:\n{response}")

def divide_document(document, max_length):
    return [document[i:i+max_length] for i in range(0, len(document), max_length)]

def handle_uploaded_file(uploaded_file, max_length):
    file_content = uploaded_file.read().decode()
    return divide_document(file_content, max_length)

def main():
    max_length = st.slider("File section length for large files", min_value=1000, max_value=128000, value=12000, step=1000)
    uploaded_file = st.file_uploader("Add a file for context:", type=["txt"])

    document_sections = deque()
    if uploaded_file is not None:
        document_sections.extend(handle_uploaded_file(uploaded_file, max_length))

    document_responses = {}
    for i, section in enumerate(document_sections):
        if st.button(f"Chat about Section {i+1}"):
            response = chat_with_model(user_prompt, section)
            document_responses[i] = response
            filename = generate_filename(f"{user_prompt}_section_{i+1}", "txt")
            create_file(filename, user_prompt, response)

    if st.button('Chat'):
        response = chat_with_model(user_prompt, ''.join(document_sections))
        filename = generate_filename(user_prompt, "txt")
        create_file(filename, user_prompt, response)

    configurations["user_prompt"] = user_prompt
    configurations["system_prompt"] = system_prompt
    with open(config_file, "w") as file:
        json.dump(configurations, file)

if __name__ == "__main__":
    main()