app.py

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

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", "csv", "xlsx"]
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}"

TEMPERATURE = st.sidebar.slider("Adjust Creativity:", min_value=0.1, max_value=1.0, value=0.5, step=0.1)
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, temperature=TEMPERATURE)
    return response['choices'][0]['message']['content']
    
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}")
    elif filename.endswith(".csv"):
        response_df = pd.DataFrame({"Prompt": [prompt], "Response": [response]})
        response_df.to_csv(filename, index=False)
    elif filename.endswith(".xlsx"):
        response_df = pd.DataFrame({"Prompt": [prompt], "Response": [response]})
        response_df.to_excel(filename, index=False)
    
# Updated to auto process transcript to chatgpt in AI pipeline from Whisper to ChatGPT
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('Reasoning with your transcription..')
        transcript=response.json().get('text')
        st.write(transcript)
        gptResponse = chat_with_model(transcript, '') # send transcript to ChatGPT
        filename = generate_filename(transcript, choice) # auto name file with date and prompt per output file type
        create_file(filename, transcript, gptResponse) # write output file 
        return gptResponse
    else:
        st.write(response.json())
        st.error("Error in API call.")
        return None

# Updated to call direct from transcription to chat inference.
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
USEAUDIO=False
if USEAUDIO:
    if st.sidebar.checkbox('Use Audio Input'):
        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.markdown('### Transcription:')
            st.write(transcription)
        

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 == '.wav':
        mime_type = 'audio/x-wav'
    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()
    elif file.type == "text/csv":
        df = pd.read_csv(file)
        return df.to_string(index=False)
    elif file.type == "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet":
        df = pd.read_excel(file)
        return df.to_string(index=False)
    else:
        return ""

def main():
    # max_length = 12000 - optimal for gpt35 turbo. 2x=24000 for gpt4.  8x=96000 for gpt4-32k.
    max_length = st.sidebar.slider("File section length for large files", min_value=1000, max_value=128000, value=12000, step=1000)
    
    colprompt, colupload = st.columns([5,2])  # adjust the ratio as needed
    with colprompt:
        user_prompt = st.text_area("Enter prompts, instructions & questions:", '', height=150)
    with colupload:
        uploaded_file = st.file_uploader("Add a file for context:", type=["xml", "json", "html", "htm", "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:
            try:
                st.markdown(get_table_download_link(file), unsafe_allow_html=True)
            except Exception as e:
                st.error(f"Error occurred while processing file {file}: {str(e)}")
        with col3:
            if st.button("🗑", key="delete_"+file):
                os.remove(file)
                st.experimental_rerun()

if __name__ == "__main__":
    main()