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


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})
    if len(document_section)>0:
        conversation.append({'role': 'assistant', 'content': document_section})
    response = openai.ChatCompletion.create(model=model, messages=conversation)
    #return response
    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

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 == '.py':
        mime_type = 'text/plain'
    elif ext == '.xlsx':
        mime_type = 'text/plain'
    elif ext == '.csv':
        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 ""


# Sidebar and global
openai.api_key = os.getenv('OPENAI_KEY')
st.set_page_config(page_title="GPT Streamlit Document Reasoner",layout="wide")
menu = ["htm", "txt", "xlsx", "csv", "md", "py"]  #619
choice = st.sidebar.selectbox("Output File Type:", menu)
model_choice = st.sidebar.radio("Select Model:", ('gpt-3.5-turbo', 'gpt-3.5-turbo-0301'))

# Audio, transcribe, GPT:
filename = save_and_play_audio(audio_recorder)
if filename is not None:
    transcription = transcribe_audio(openai.api_key, filename, "whisper-1")
    st.write(transcription)
    gptOutput = chat_with_model(transcription, '') # *************************************
    filename = generate_filename(transcription, choice)
    create_file(filename, transcription, gptOutput)
    st.sidebar.markdown(get_table_download_link(filename), unsafe_allow_html=True)


def chat_with_file_contents(prompt, file_content):
    conversation = [{'role': 'system', 'content': 'You are a helpful assistant.'}]
    conversation.append({'role': 'user', 'content': prompt})
    if len(file_content)>0:
        conversation.append({'role': 'assistant', 'content': file_content})
    response = openai.ChatCompletion.create(model=model_choice, messages=conversation)
    return response['choices'][0]['message']['content']

# Sidebar and global
openai.api_key = os.getenv('OPENAI_KEY')
st.set_page_config(page_title="GPT Streamlit Document Reasoner",layout="wide")
menu = ["htm", "txt", "xlsx", "csv", "md", "py"]  #619
choice = st.sidebar.selectbox("Output File Type:", menu)
model_choice = st.sidebar.radio("Select Model:", ('gpt-3.5-turbo', 'gpt-3.5-turbo-0301'))

# Audio, transcribe, GPT:
filename = save_and_play_audio(audio_recorder)
if filename is not None:
    transcription = transcribe_audio(openai.api_key, filename, "whisper-1")
    st.write(transcription)
    gptOutput = chat_with_model(transcription, '') 
    filename = generate_filename(transcription, choice)
    create_file(filename, transcription, gptOutput)
    st.sidebar.markdown(get_table_download_link(filename), unsafe_allow_html=True)

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 = 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", "xlsx","csv","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)

    # ... document_responses logic remains the same
    
    all_files = [file for file in glob.glob("*.{}".format(choice))]
    
    for file in all_files:
        col1, col2, col3 = st.sidebar.columns([5,1,1])  # adjust the ratio as needed
        with col1:
            st.markdown(get_table_download_link(file), unsafe_allow_html=True)
        with col2:
            if st.button("🔍", key="read_"+file):  # search emoji button
                with open(file, 'r') as f:
                    file_contents = f.read()
                file_content_area = st.text_area("File Contents:", file_contents, height=100)
                if st.button('💬 Chat with file content'):
                    st.write('Reasoning with your inputs...')
                    response = chat_with_file_contents(user_prompt, file_contents)
                    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)
        with col3:
            if st.button("🗑", key="delete_"+file):
                os.remove(file)
                st.experimental_rerun()

if __name__ == "__main__":
    main()