app.py

import os
import streamlit as st
from transformers import pipeline

# ... (other setup or imports you may need)

# Pre-Configuration & Model Loading
openchat_preprompt = """
... (same content as before) ... 
""" 

def get_usernames(model: str):
    # ... (Implementation remains identical) ... 
    return preprompt, user_name, assistant_name, sep

# Text Generation API Interaction (Modify as needed)
def predict(model, inputs, typical_p, top_p, temperature, 
            top_k, repetition_penalty, watermark, chatbot, history):
    # ... (Logic adapted for your text generation API) ...

# Streamlit UI 
st.title("Large Language Model Chat API")
st.markdown("**Description:** ... (Your description)")

# Model Selection 
model_name = st.radio("Model", [
    "OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5",
    "OpenAssistant/oasst-sft-1-pythia-12b",
    # ...  (other supported models) 
])

# Parameter Controls (Conditional display as in Gradio)
# ...  (Streamlit controls for typical_p, top_p, etc.)

# Chat Interface
chatbot = st.empty()  # Placeholder for the chatbot display
user_input = st.text_input("Type an input and press Enter", "")
history = []

if user_input:
    history.append(user_input)
    with st.spinner("Generating response..."):
        chat_output, history = predict(model_name, user_input, # ... other parameters)
        chatbot.text("\n".join([f"{data[0]}{data[1]}" for data in chat_output]))

# ... (Rest of the UI, if needed) ...