import streamlit as st import openai import os import base64 import glob from datetime import datetime from openai import ChatCompletion from xml.etree import ElementTree as ET from bs4 import BeautifulSoup import json # from dotenv import load_dotenv # load_dotenv() openai.api_key = os.getenv('OPENAI_KEY') def chat_with_model(prompts): model = "gpt-3.5-turbo" #model = "gpt-4-32k" 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_date_time = datetime.now().strftime("%m%d_%I_%M_%p") safe_prompt = "".join(x for x in prompt if x.isalnum())[:30] return f"{safe_date_time}_{safe_prompt}.txt" def create_file(filename, prompt, response): with open(filename, 'w') as file: file.write(f"

Prompt:

{prompt}

Response:

{response}

") def get_table_download_link_old(file_path): with open(file_path, 'r') as file: data = file.read() b64 = base64.b64encode(data.encode()).decode() href = f'{os.path.basename(file_path)}' return href def get_table_download_link(file_path): import os import base64 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'{file_name}' 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") return ET.tostring(root, encoding='unicode', method="xml")[:16000] 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": tree = ET.parse(file) root = tree.getroot() #return ET.tostring(root, encoding='unicode') return CompressXML(ET.tostring(root, encoding='unicode')) 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("*.txt") for file in htm_files: st.sidebar.markdown(get_table_download_link(file), unsafe_allow_html=True) if st.sidebar.button(f"🗑Delete {file}"): #if st.sidebar.button("🗑 Delete"): os.remove(file) st.experimental_rerun() if __name__ == "__main__": main()