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import streamlit as st
from streamlit_chat import message
from streamlit_extras.colored_header import colored_header
from streamlit_extras.add_vertical_space import add_vertical_space
from hugchat import hugchat
from transformers import AutoModelForCausalLM
st.set_page_config(page_title="HugChat - An LLM-powered Streamlit app")
def check_model(model_name):"bert-base-uncased"
try:
model = AutoModelForCausalLM.from_pretrained(model_name)
st.write(f"Model {model_name} loaded successfully.")
except Exception as e:
st.write(f"Failed to load model {bert-base-uncased}. Error: {str(e)}")
# Sidebar contents
with st.sidebar:
st.title('🤗💬 HugChat App')
st.markdown('''
## About
This app is an LLM-powered chatbot built using:
- [Streamlit](https://streamlit.io/)
- [bert-base-uncased] LLM model
💡 Note: No API key required!
''')
add_vertical_space(5)
st.write('Made withh Love')
# Generate empty lists for generated and past.
## generated stores AI generated responses
if 'generated' not in st.session_state:
st.session_state['generated'] = ["I'm HugChat, How may I help you?"]
## past stores User's questions
if 'past' not in st.session_state:
st.session_state['past'] = ['Hi!']
# Layout of input/response containers
input_container = st.container()
colored_header(label='', description='', color_name='blue-30')
response_container = st.container()
# User input
## Function for taking user provided prompt as input
def get_text():
input_text = st.text_input("You: ", "", key="input")
return input_text
## Applying the user input box
with input_container:
user_input = get_text()
# Response output
## Function for taking user prompt as input followed by producing AI generated responses
def generate_response(prompt):
chatbot = hugchat.ChatBot(model_name="gpt3") # For GPT-3
# chatbot = hugchat.ChatBot(model_name="gpt4") # For GPT-4
response = chatbot.chat(prompt)
return response
## Conditional display of AI generated responses as a function of user provided prompts
with response_container:
if user_input:
response = generate_response(user_input)
st.session_state.past.append(user_input)
st.session_state.generated.append(response)
if st.session_state['generated']:
for i in range(len(st.session_state['generated'])):
message(st.session_state['past'][i], is_user=True, key=str(i) + '_user')
message(st.session_state["generated"][i], key=str(i))
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