app.py

import streamlit as st
from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM

# Load the pipeline
model_name = "Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2"
pipe = pipeline("text-generation", model=model_name)

# Optionally load the tokenizer and model directly (not used directly in this example)
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

def generate_response(prompt):
    """Generate a response from the model given a prompt."""
    response = pipe(prompt, max_length=100, num_return_sequences=1)
    return response[0]['generated_text']

# Streamlit Interface
st.title("AI Chatbot using Hugging Face")
st.markdown("This app uses the Llama-3.1-8B-Lexi-Uncensored-V2 model to generate responses.")

user_input = st.text_input("Enter your message:", placeholder="Type something here...")

if st.button("Generate Response"):
    if user_input:
        response = generate_response(user_input)
        st.text_area("Response:", value=response, height=200)
    else:
        st.warning("Please enter a message before clicking the button.")