app.py

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

st.title("WhiteRabbitNeo Chatbot")

# Load Model
@st.cache_resource
def load_model():
    model_name = "path/to/chatbot_files"
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16)
    return tokenizer, model

tokenizer, model = load_model()

# Chat Interface
user_input = st.text_input("You:", placeholder="Type your message here...")
if user_input:
    inputs = tokenizer.encode(user_input, return_tensors="pt")
    outputs = model.generate(inputs, max_length=50, num_return_sequences=1)
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    st.write(f"Chatbot: {response}")

def load_model():
    model_name = "C:/Users/DMJ/WhiteRabbitNeo-13B-v1/chatbot_files"  # Use forward slashes for compatibility
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16)
    return tokenizer, model