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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"
)
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