app.py

import streamlit as st
import openai
import os
import base64
import glob
import json
import mistune
import pytz
import math
import requests

from datetime import datetime
from openai import ChatCompletion
from xml.etree import ElementTree as ET
from bs4 import BeautifulSoup
from collections import deque
from audio_recorder_streamlit import audio_recorder

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

menu = ["txt", "htm", "md", "py"]
choice = st.sidebar.selectbox("Output File Type:", menu)
model_choice = st.sidebar.radio("Select Model:", ('gpt-3.5-turbo', 'gpt-3.5-turbo-0301'))

def generate_filename(prompt, file_type):
    central = pytz.timezone('US/Central')
    safe_date_time = datetime.now(central).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):
    model = model_choice
    conversation = [{'role': 'system', 'content': 'You are a helpful assistant.'}]
    conversation.append({'role': 'user', 'content': prompt})
    conversation.append({'role': 'assistant', 'content': document_section})
    response = openai.ChatCompletion.create(model=model, messages=conversation)
    return response['choices'][0]['message']['content']

def transcribe_audio(openai_key, file_path, model):
    OPENAI_API_URL = "https://api.openai.com/v1/audio/transcriptions"
    headers = {
        "Authorization": f"Bearer {openai_key}",
    }
    with open(file_path, 'rb') as f:
        data = {'file': f}
        response = requests.post(OPENAI_API_URL, headers=headers, files=data, data={'model': model})
    if response.status_code == 200:
        st.write(response.json())
        response2 = chat_with_model(response.json().get('text'), '')
        st.write('Responses:')
        #st.write(response)
        st.write(response2)
        return response.json().get('text')
    else:
        st.write(response.json())
        st.error("Error in API call.")
        return None

def save_and_play_audio(audio_recorder):
    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

filename = save_and_play_audio(audio_recorder)
if filename is not None:
    if st.button("Transcribe"):
        transcription = transcribe_audio(openai.api_key, filename, "whisper-1")
        st.write(transcription)
        chat_with_model(transcription, '') # push transcript through as prompt

def create_file(filename, prompt, response):
    if filename.endswith(".txt"):
        with open(filename, 'w') as file:
            file.write(f"Prompt:\n{prompt}\nResponse:\n{response}")
    elif filename.endswith(".htm"):
        with open(filename, 'w') as file:
            file.write(f"<h1>Prompt:</h1> <p>{prompt}</p> <h1>Response:</h1> <p>{response}</p>")
    elif filename.endswith(".md"):
        with open(filename, 'w') as file:
            file.write(f"# Prompt:\n{prompt}\n# Response:\n{response}")

def truncate_document(document, length):
    return document[:length]

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

def get_table_download_link(file_path):
    with open(file_path, 'r') as file:
        data = file.read()
    b64 = base64.b64encode(data.encode()).decode()  
    file_name = os.path.basename(file_path)
    ext = os.path.splitext(file_name)[1]  # get the file extension
    if ext == '.txt':
        mime_type = 'text/plain'
    elif ext == '.htm':
        mime_type = 'text/html'
    elif ext == '.md':
        mime_type = 'text/markdown'
    else:
        mime_type = 'application/octet-stream'  # general binary data type
    href = f'<a href="data:{mime_type};base64,{b64}" target="_blank" download="{file_name}">{file_name}</a>'
    return href

def CompressXML(xml_text):
    root = ET.fromstring(xml_text)
    for elem in list(root.iter()):
        if isinstance(elem.tag, str) and 'Comment' in elem.tag:
            elem.parent.remove(elem)
    return ET.tostring(root, encoding='unicode', method="xml")
    
def read_file_content(file,max_length):
    if file.type == "application/json":
        content = json.load(file)
        return str(content)
    elif file.type == "text/html" or file.type == "text/htm":
        content = BeautifulSoup(file, "html.parser")
        return content.text
    elif file.type == "application/xml" or file.type == "text/xml":
        tree = ET.parse(file)
        root = tree.getroot()
        xml = CompressXML(ET.tostring(root, encoding='unicode'))
        return xml
    elif file.type == "text/markdown" or file.type == "text/md":
        md = mistune.create_markdown()
        content = md(file.read().decode())
        return content
    elif file.type == "text/plain":
        return file.getvalue().decode()
    else:
        return ""

def main():
    user_prompt = st.text_area("Enter prompts, instructions & questions:", '', height=100)

    collength, colupload = st.columns([2,3])  # adjust the ratio as needed
    with collength:
        #max_length = 12000 - optimal for gpt35 turbo. 2x=24000 for gpt4.  8x=96000 for gpt4-32k.
        max_length = st.slider("File section length for large files", min_value=1000, max_value=128000, value=12000, step=1000)
    with colupload:
        uploaded_file = st.file_uploader("Add a file for context:", type=["xml", "json", "html", "htm", "md", "txt"])
    
    document_sections = deque()
    document_responses = {}

    if uploaded_file is not None:
        file_content = read_file_content(uploaded_file, max_length)
        document_sections.extend(divide_document(file_content, max_length))

    if len(document_sections) > 0:
        
        if st.button("👁️ View Upload"):
            st.markdown("**Sections of the uploaded file:**")
            for i, section in enumerate(list(document_sections)):
                st.markdown(f"**Section {i+1}**\n{section}")
        
        st.markdown("**Chat with the model:**")
        for i, section in enumerate(list(document_sections)):
            if i in document_responses:
                st.markdown(f"**Section {i+1}**\n{document_responses[i]}")
            else:
                if st.button(f"Chat about Section {i+1}"):
                    st.write('Reasoning with your inputs...')
                    response = chat_with_model(user_prompt, section)
                    st.write('Response:')
                    st.write(response)
                    document_responses[i] = response
                    filename = generate_filename(f"{user_prompt}_section_{i+1}", choice)
                    create_file(filename, user_prompt, response)
                    st.sidebar.markdown(get_table_download_link(filename), unsafe_allow_html=True)

    if st.button('💬 Chat'):
        st.write('Reasoning with your inputs...')
        response = chat_with_model(user_prompt, ''.join(list(document_sections)))
        st.write('Response:')
        st.write(response)
        
        filename = generate_filename(user_prompt, choice)
        create_file(filename, user_prompt, response)
        st.sidebar.markdown(get_table_download_link(filename), unsafe_allow_html=True)

    all_files = glob.glob("*.*")
    all_files = [file for file in all_files if len(os.path.splitext(file)[0]) >= 20]  # exclude files with short names
    all_files.sort(key=lambda x: (os.path.splitext(x)[1], x), reverse=True)  # sort by file type and file name in descending order
    
    for file in all_files:
        col1, col3 = st.sidebar.columns([5,1])  # adjust the ratio as needed
        with col1:
            st.markdown(get_table_download_link(file), unsafe_allow_html=True)
        with col3:
            if st.button("🗑", key="delete_"+file):
                os.remove(file)
                st.experimental_rerun()
            
if __name__ == "__main__":
    main()