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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) | |
| 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 | |
| show full code listing | |
| ChatGPT | |
| Here is the full Python script: | |
| python | |
| Copy code | |
| 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() | |