import streamlit as st
import openai
import os
import base64
import glob
import json
from xml.etree import ElementTree as ET
from datetime import datetime
from dotenv import load_dotenv
from openai import ChatCompletion

load_dotenv()

openai.api_key = os.getenv('OPENAI_KEY')

def chat_with_model(prompts):
    model = "gpt-3.5-turbo"

    conversation = [{'role': 'system', 'content': 'You are a helpful assistant.'}]
    conversation.extend([{'role': 'user', 'content': prompt} for prompt in prompts])

    response = openai.ChatCompletion.create(model=model, messages=conversation)
    return response['choices'][0]['message']['content']

def generate_filename(prompt):
    safe_date_time = datetime.now().strftime("%m_%d_%H_%M")
    safe_prompt = "".join(x for x in prompt if x.isalnum())[:50]
    return f"{safe_date_time}_{safe_prompt}.htm"

def create_file(filename, prompt, response):
    with open(filename, 'w') as file:
        file.write(f"<h1>Prompt:</h1> <p>{prompt}</p> <h1>Response:</h1> <p>{response}</p>")

def get_table_download_link(file_path):
    with open(file_path, 'r') as file:
        data = file.read()
    b64 = base64.b64encode(data.encode()).decode()
    href = f'<a href="data:file/htm;base64,{b64}" target="_blank" download="{os.path.basename(file_path)}">{os.path.basename(file_path)}</a>'
    return href

def CompressXML(xml_text):
    tree = ET.ElementTree(ET.fromstring(xml_text))
    for elem in tree.iter():
        if isinstance(elem.tag, ET.Comment):
            elem.getparent().remove(elem)
    return ET.tostring(tree.getroot(), encoding='unicode')

def read_file_content(file):
    if file.type == "application/json":
        content = json.load(file)
        return str(content)
    elif file.type == "text/html":
        content = BeautifulSoup(file, "html.parser")
        return content.text
    elif file.type == "application/xml" or file.type == "text/xml":
        xml_text = file.getvalue().decode()
        compressed_text = CompressXML(xml_text)
        return compressed_text
    elif file.type == "text/plain":
        return file.getvalue().decode()
    else:
        return ""

def main():
    st.title("Chat with AI")

    prompts = ['']

    user_prompt = st.text_area("Your question:", '', height=120)
    uploaded_file = st.file_uploader("Choose a file", type=["xml", "json", "htm", "txt"])

    if user_prompt:
        prompts.append(user_prompt)

    if uploaded_file is not None:
        file_content = read_file_content(uploaded_file)
        st.markdown(f"**Content Added to Prompt:**\n{file_content}")
        prompts.append(file_content)

    if st.button('Chat'):
        st.write('Chatting with GPT-3...')
        response = chat_with_model(prompts)
        st.write('Response:')
        st.write(response)

        filename = generate_filename(user_prompt)
        create_file(filename, user_prompt, response)

        st.sidebar.markdown(get_table_download_link(filename), unsafe_allow_html=True)

    htm_files = glob.glob("*.htm")
    for file in htm_files:
        st.sidebar.mark




User
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ChatGPT
Here is the full Python script:

python
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import streamlit as st
import openai
import os
import base64
import glob
import json
from xml.etree import ElementTree as ET
from datetime import datetime
from dotenv import load_dotenv
from openai import ChatCompletion

load_dotenv()

openai.api_key = os.getenv('OPENAI_KEY')

def chat_with_model(prompts):
    model = "gpt-3.5-turbo"

    conversation = [{'role': 'system', 'content': 'You are a helpful assistant.'}]
    conversation.extend([{'role': 'user', 'content': prompt} for prompt in prompts])

    response = openai.ChatCompletion.create(model=model, messages=conversation)
    return response['choices'][0]['message']['content']

def generate_filename(prompt):
    safe_date_time = datetime.now().strftime("%m_%d_%H_%M")
    safe_prompt = "".join(x for x in prompt if x.isalnum())[:50]
    return f"{safe_date_time}_{safe_prompt}.htm"

def create_file(filename, prompt, response):
    with open(filename, 'w') as file:
        file.write(f"<h1>Prompt:</h1> <p>{prompt}</p> <h1>Response:</h1> <p>{response}</p>")

def get_table_download_link(file_path):
    with open(file_path, 'r') as file:
        data = file.read()
    b64 = base64.b64encode(data.encode()).decode()
    href = f'<a href="data:file/htm;base64,{b64}" target="_blank" download="{os.path.basename(file_path)}">{os.path.basename(file_path)}</a>'
    return href

def CompressXML(xml_text):
    tree = ET.ElementTree(ET.fromstring(xml_text))
    for elem in tree.iter():
        if isinstance(elem.tag, ET.Comment):
            elem.getparent().remove(elem)
    return ET.tostring(tree.getroot(), encoding='unicode')

def read_file_content(file):
    if file.type == "application/json":
        content = json.load(file)
        return str(content)
    elif file.type == "text/html":
        content = BeautifulSoup(file, "html.parser")
        return content.text
    elif file.type == "application/xml" or file.type == "text/xml":
        xml_text = file.getvalue().decode()
        compressed_text = CompressXML(xml_text)
        return compressed_text
    elif file.type == "text/plain":
        return file.getvalue().decode()
    else:
        return ""


def main():
    st.title("Chat with AI")

    prompts = ['']

    user_prompt = st.text_area("Your question:", '', height=120)
    uploaded_file = st.file_uploader("Choose a file", type=["xml", "json", "htm", "txt"])

    if user_prompt:
        prompts.append(user_prompt)

    if uploaded_file is not None:
        file_content = read_file_content(uploaded_file)
        prompts.append(file_content)

    if st.button('Chat'):
        st.write('Chatting with GPT-3...')
        response = chat_with_model(prompts)
        st.write('Response:')
        st.write(response)

        filename = generate_filename(user_prompt)
        create_file(filename, user_prompt, response)

        st.sidebar.markdown(get_table_download_link(filename), unsafe_allow_html=True)

    htm_files = glob.glob("*.htm")
    for file in htm_files:
        st.sidebar.markdown(get_table_download_link(file), unsafe_allow_html=True)
        if st.sidebar.button(f"Delete {file}"):
            os.remove(file)
            st.experimental_rerun()

if __name__ == "__main__":
    main()