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import streamlit as st
import os
from ctransformers import AutoModelForCausalLM
# App title
st.set_page_config(page_title="🦙💬 Llama 2 Chatbot")
@st.cache_resource()
def ChatModel(temperature, top_p):
return AutoModelForCausalLM.from_pretrained(
# 'ggml-llama-2-7b-chat-q4_0.bin',
'Israr-dawar/psychology_chatbot',
# model_type='llama',
temperature=temperature,
top_p = top_p)
# Replicate Credentials
with st.sidebar:
st.title('🦙💬 Llama 2 Chatbot')
# Refactored from <https://github.com/a16z-infra/llama2-chatbot>
st.subheader('Models and parameters')
temperature = st.sidebar.slider('temperature', min_value=0.01, max_value=2.0, value=0.1, 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=64, max_value=4096, value=512, step=8)
chat_model =ChatModel(temperature, top_p)
# st.markdown('📖 Learn how to build this app in this [blog](#link-to-blog)!')
# 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 LLaMA2 response
def generate_llama2_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"
output = chat_model(f"prompt {string_dialogue} {prompt_input} Assistant: ")
return output
# User-provided prompt
if prompt := st.chat_input():
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("Thinking..."):
response = generate_llama2_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)
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