app.py

import openai
from concurrent.futures import ThreadPoolExecutor
import streamlit as st
import os

# Add your own OpenAI API key
# openai.api_key = "sk-XXXXXXXXXXXXXXXXXXXXX"

openai.api_key = os.environ["OPENAI_API_KEY"]

def load_text(file_path):
    with open(file_path, 'r') as file:
        return file.read()

def save_to_file(responses, output_file):
    with open(output_file, 'w') as file:
        for response in responses:
            file.write(response + '\n')

def call_openai_api(chunk):
    response = openai.ChatCompletion.create(
        model="gpt-4",
        messages=[
            {"role": "system", "content": "PASS IN ANY ARBITRARY SYSTEM VALUE TO GIVE THE AI AN IDENITY"},
            {"role": "user", "content": f"OPTIONAL PREPROMPTING FOLLOWING BY YOUR DATA TO PASS IN: {chunk}."},
        ],
        max_tokens=1750,
        n=1,
        stop=None,
        temperature=0.5,
    )
    return response.choices[0]['message']['content'].strip()

def split_into_chunks(text, tokens=1500):
    words = text.split()
    chunks = [' '.join(words[i:i + tokens]) for i in range(0, len(words), tokens)]
    return chunks

def process_chunks(input_text):
    chunks = split_into_chunks(input_text)

    # Processes chunks in parallel
    with ThreadPoolExecutor() as executor:
        responses = list(executor.map(call_openai_api, chunks))

    return "\n".join(responses)

def main():
    st.title("InfiniteGPT")
    st.write("Input unlimited size text and process it using the GPT-4 OpenAI API.")
    st.write("For more details and credit check this [GitHub repository](https://github.com/emmethalm/infiniteGPT).")
    
    # Define columns for input and output
    col1, col2 = st.columns(2)

    with col1:
        input_text = st.text_area("Enter or paste your text here", height=300)
        process_button = st.button("Process Text")
    

    # Initialize output_text as an empty string
    output_text = ''

    # Process text if the button is pressed
    if process_button and input_text:
        with st.spinner("Processing text..."):
            output_text = process_chunks(input_text)

    with col2:
        st.text_area("Output", output_text, height=300)
        clear_button = st.button("Clear Text")

    if clear_button:
        input_text = ''
        st.experimental_rerun()

if __name__ == "__main__":
    main()