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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()
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