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from functions import *
# set the title
st.sidebar.title(DASHBOARD_TITLE)
info_section = st.empty()
# add an explanation of what is NER and why it is important for medical tasks
st.sidebar.markdown(
f"""
Meta Llama 3 8B Instruct is part of a family of large language models (LLMs) optimized for dialogue tasks.
This project uses Streamlit to create a simple chatbot interface that allows you to chat with the model using the Hugging Face Inference API.
Ask the model marketing-related questions and see how it responds. Have fun!
Model used: [{MODEL_PATH}]({MODEL_LINK})
"""
)
first_assistant_message = "Hello! I am Marketing expert. What can I help you with today?"
# clear conversation
if st.sidebar.button("Clear conversation"):
chat_history = [{'role':'assistant', 'content':first_assistant_message}]
st.session_state['chat_history'] = chat_history
st.rerun()
# Get the chat history
if "chat_history" not in st.session_state:
chat_history = [{'role':'assistant', 'content':first_assistant_message}]
st.session_state['chat_history'] = chat_history
else:
chat_history = st.session_state['chat_history']
# print the conversation
for message in chat_history:
with st.chat_message(message['role']):
st.write(message['content'])
# keep only last 10 messages
shorter_history = [message for message in chat_history[-10:] if 'content' in message]
# include a system prompt to explain the bot what to do
system_prompt = """For this task, you are a Marketer specialized in E-commerce helping a user with marketing-related questions. Provide insights and recommendations based on the user's questions. Don't write more than 3-4 sentences per response."""
shorter_history = [{'role': 'system', 'content': system_prompt}] + shorter_history
# get the input from user
user_input = st.chat_input("Write something...")
if user_input:
with st.chat_message("user"):
st.write(user_input)
# make the request
with st.spinner("Generating the response..."):
client = InferenceClient(
"meta-llama/Meta-Llama-3-8B-Instruct",
token=HUGGING_FACE_API_KEY,
)
messages = shorter_history + [{'role': 'user', 'content': user_input}]
# query the model
try:
response = client.chat_completion(
messages=messages,
max_tokens = 500,
stream = False,
)
# get the response
message = response.choices[0].message['content']
# append to the history
chat_history.append({'content':user_input, 'role':'user'})
chat_history.append(response.choices[0].message) # append the response
except Exception as e:
st.error(f"An error occurred: {e}")
st.stop()
st.session_state['chat_history'] = chat_history
st.rerun()
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