BASIC_MISTRAL_8X7B_PAGE.py

import streamlit as st
import replicate
import os

# App title
st.set_page_config(page_title="🦙💬 Llama 2 Chatbot")

# Initialize session state
if "messages" not in st.session_state:
    st.session_state.messages = [{"role": "assistant", "content": "How may I assist you today?"}]

# Verify API key
api_key = "r8_6C54yX21fa7UOSVTn0k7k1WOtsDGMEB1Hf3NJ"  # Replace with your actual API key
print(f"API key being used: {api_key}")

# Set the API key for the entire session
os.environ["REPLICATE_API_TOKEN"] = api_key

# Initialize Replicate client with the API key
client = replicate.Client(api_token=api_key)

# Function to clear chat history
def clear_chat_history():
    st.session_state.messages = [{"role": "assistant", "content": "How may I assist you today?"}]

# Sidebar for model selection, parameters, and clear chat button
with st.sidebar:
    st.title('🦙💬 Llama 2 Chatbot')
    st.success('API key set!', icon='✅')

    st.subheader('Models and parameters')
    selected_model = st.selectbox('Choose a Llama2 model', ['Llama2-7B', 'Llama2-13B'], key='selected_model')
    if selected_model == 'Llama2-7B':
        llm = 'a16z-infra/llama7b-v2-chat:4f0a4744c7295c024a1de15e1a63c880d3da035fa1f49bfd344fe076074c8eea'
    elif selected_model == 'Llama2-13B':
        llm = 'a16z-infra/llama13b-v2-chat:df7690f1994d94e96ad9d568eac121aecf50684a0b0963b25a41cc40061269e5'
    temperature = st.slider('temperature', min_value=0.01, max_value=5.0, value=0.1, step=0.01)
    top_p = st.slider('top_p', min_value=0.01, max_value=1.0, value=0.9, step=0.01)
    max_length = st.slider('max_length', min_value=32, max_value=128, value=120, step=8)
    
    # Clear chat button
    if st.button('Clear Chat History'):
        clear_chat_history()

# Function to generate 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"
    try:
        output = client.run(
            llm,  # Use the selected model
            input={
                "prompt": f"{string_dialogue} {prompt_input} Assistant: ",
                "temperature": temperature,
                "top_p": top_p,
                "max_length": max_length,
                "repetition_penalty": 1
            }
        )
        return output
    except Exception as e:
        st.error(f"An error occurred: {str(e)}")
        return None

# Display chat messages
for message in st.session_state.messages:
    with st.chat_message(message["role"]):
        st.write(message["content"])

# User-provided prompt
if prompt := st.chat_input(disabled=False):
    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)
            if response:
                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)
            else:
                st.error("Failed to generate response. Please check your API key and try again.")