app.py

import streamlit as st
from transformers import AutoModelForCausalLM, AutoTokenizer

# Title of the Streamlit app
st.title("Neo Scalinglaw 250M Model")

# Text input for user prompt
user_input = st.text_input("Enter your prompt:")

# Load the tokenizer and model
@st.cache_resource
def load_model():
    model_path = 'm-a-p/neo_scalinglaw_250M'
    tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, trust_remote_code=True)
    model = AutoModelForCausalLM.from_pretrained(model_path, device_map="auto", torch_dtype='auto').eval()
    return tokenizer, model

tokenizer, model = load_model()

# Generate text when the user inputs a prompt and presses the button
if st.button("Generate"):
    if user_input:
        with st.spinner("Generating response..."):
            input_ids = tokenizer(user_input, add_generation_prompt=True, return_tensors='pt').to(model.device)
            output_ids = model.generate(**input_ids, max_new_tokens=20)
            response = tokenizer.decode(output_ids[0], skip_special_tokens=True)
            st.success("Generated response:")
            st.write(response)
    else:
        st.error("Please enter a prompt.")