Spaces:
Sleeping
Sleeping
| import streamlit as st | |
| import replicate | |
| import os | |
| from dotenv import load_dotenv | |
| from reportlab.pdfgen import canvas | |
| import tempfile | |
| # Load environment variables from .env file | |
| load_dotenv() | |
| # App title | |
| st.set_page_config(page_title="🤖💬 Galactic Assistant QuasarBot") | |
| # Replicate Credentials | |
| with st.sidebar: | |
| st.title('🤖💬 Galactic Assistant: QuasarBot') | |
| st.write('This chatbot is created using the open-source Llama 3 LLM model from Meta.') | |
| replicate_api = os.getenv('REPLICATE_API_TOKEN') | |
| if replicate_api: | |
| st.success('API key already provided!', icon='✅') | |
| else: | |
| replicate_api = st.text_input('Enter Replicate API token:', type='password') | |
| if not (replicate_api.startswith('r8_') and len(replicate_api) == 40): | |
| st.warning('Please enter your credentials!', icon='⚠️') | |
| else: | |
| st.success('Proceed to entering your prompt message!', icon='👉') | |
| os.environ['REPLICATE_API_TOKEN'] = replicate_api | |
| st.subheader('Models and parameters') | |
| selected_model = st.sidebar.selectbox('Choose a Llama3 model', ['Llama3-70B'], key='selected_model') | |
| if selected_model == 'Llama3-70B': | |
| llm = 'meta/meta-llama-3-70b-instruct' | |
| temperature = st.sidebar.slider('temperature', min_value=0.01, max_value=1.0, value=0.6, step=0.01) | |
| top_p = st.sidebar.slider('top_p', min_value=0.01, max_value=1.0, value=0.9, step=0.01) | |
| max_length = st.sidebar.slider('max_length', min_value=32, max_value=512, value=512, step=8) | |
| st.markdown('📖 Learn how to build this app in this [blog](https://replicate.com/meta/meta-llama-3-70b-instruct)!') | |
| # Store LLM generated responses | |
| if "messages" not in st.session_state.keys(): | |
| st.session_state.messages = [{"role": "assistant", "content": "How may I assist you today?"}] | |
| # Display or clear chat messages | |
| for message in st.session_state.messages: | |
| with st.chat_message(message["role"]): | |
| st.write(message["content"]) | |
| def clear_chat_history(): | |
| st.session_state.messages = [{"role": "assistant", "content": "How may I assist you today?"}] | |
| st.sidebar.button('Clear Chat History', on_click=clear_chat_history) | |
| # Function for generating LLaMA3 response | |
| def generate_llama3_response(prompt_input): | |
| string_dialogue = "You are a helpful assistant. You do not respond as 'User' or pretend to be 'User'. You only respond once as 'Assistant'." | |
| for dict_message in st.session_state.messages: | |
| if dict_message["role"] == "user": | |
| string_dialogue += "User: " + dict_message["content"] + "\n\n" | |
| else: | |
| string_dialogue += "Assistant: " + dict_message["content"] + "\n\n" | |
| response = replicate.run( | |
| llm, | |
| input={ | |
| "prompt": f"{string_dialogue} {prompt_input} Assistant: ", | |
| "temperature": temperature, | |
| "top_p": top_p, | |
| "max_tokens": max_length, | |
| "presence_penalty": 1.15, | |
| "frequency_penalty": 0.2 | |
| } | |
| ) | |
| return response | |
| # User-provided prompt | |
| if prompt := st.chat_input(disabled=not replicate_api): | |
| st.session_state.messages.append({"role": "user", "content": prompt}) | |
| with st.chat_message("user"): | |
| st.write(prompt) | |
| # Generate a new response if last message is not from assistant | |
| if st.session_state.messages[-1]["role"] != "assistant": | |
| with st.chat_message("assistant"): | |
| with st.spinner("Processing..."): | |
| response = generate_llama3_response(prompt) | |
| placeholder = st.empty() | |
| full_response = '' | |
| for item in response: | |
| full_response += item | |
| placeholder.markdown(full_response) | |
| placeholder.markdown(full_response) | |
| message = {"role": "assistant", "content": full_response} | |
| st.session_state.messages.append(message) | |
| # Function to convert messages to HTML | |
| def messages_to_html(messages): | |
| html = "<html><body>" | |
| for message in messages: | |
| role = "User" if message["role"] == "user" else "Assistant" | |
| html += f"<p><strong>{role}:</strong> {message['content']}</p>" | |
| html += "</body></html>" | |
| return html | |
| # Function to generate PDF report | |
| def generate_pdf(description): | |
| if description is None: | |
| return None | |
| with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as tmpfile: | |
| c = canvas.Canvas(tmpfile.name) | |
| c.setFont("Helvetica", 12) | |
| text_lines = description.split('\n') # Ensure description is not None before splitting | |
| y = 750 | |
| for line in text_lines: | |
| c.drawString(100, y, line) | |
| y -= 20 | |
| c.save() | |
| tmpfile.close() | |
| return tmpfile.name | |
| # Button to download chat history as PDF | |
| if st.sidebar.button('Download Chat History as PDF'): | |
| html_content = messages_to_html(st.session_state.messages) | |
| pdf_filename = generate_pdf(html_content) | |
| if pdf_filename: | |
| with open(pdf_filename, "rb") as pdf_file: | |
| st.sidebar.download_button( | |
| label="Download PDF", | |
| data=pdf_file, | |
| file_name="chat_history.pdf", | |
| mime="application/pdf" | |
| ) | |