app.py

import streamlit as st
from langchain_huggingface import HuggingFaceEndpoint
from langchain_core.prompts import PromptTemplate
import os

# Set up your Hugging Face API token
os.environ["HUGGINGFACEHUB_API_TOKEN"] = st.secrets["HF_TOKEN"]

# Define the models
models = {
    "Mistral-7B-Instruct-v0.2": "mistralai/Mistral-7B-Instruct-v0.2",
    "Mistral-7B-Instruct-v0.3": "mistralai/Mistral-7B-Instruct-v0.3",
    "GPT-2": "gpt2",
    "BLOOM": "bigscience/bloom",
    "OPT": "facebook/opt-350m"
}

# Initialize session state
if 'messages' not in st.session_state:
    st.session_state.messages = []

# Streamlit app
st.title("Multi-Model LLM Chat")

# Model selection
selected_model = st.selectbox("Choose a model", list(models.keys()))

# User input
user_input = st.text_input("Your message:")

# Initialize LLM
@st.cache_resource
def get_llm(model_name):
    return HuggingFaceEndpoint(
        repo_id=models[model_name],
        max_length=128,
        temperature=0.7
    )

llm = get_llm(selected_model)

# Chat prompt template
prompt = PromptTemplate(
    template="Human: {human_input}\n\nAssistant: Let's think about this step-by-step:",
    input_variables=["human_input"]
)

# Generate response
if user_input:
    # Add user message to chat history
    st.session_state.messages.append({"role": "user", "content": user_input})
    
    # Generate LLM response
    with st.spinner("Generating response..."):
        full_prompt = prompt.format(human_input=user_input)
        response = llm.invoke(full_prompt)
    
    # Add assistant response to chat history
    st.session_state.messages.append({"role": "assistant", "content": response})

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

# Clear chat button
if st.button("Clear Chat"):
    st.session_state.messages = []
    st.experimental_rerun()