app.py

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

# Load the Phi 2 model and tokenizer outside the Streamlit app
tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-2", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("microsoft/phi-2", device_map="auto", trust_remote_code=True)

# Streamlit UI
st.title("Microsoft Phi 2 Streamlit App")

# User input prompt
prompt = st.text_area("Enter your prompt:", "Write a story about Nasa")

# Generate output based on user input
if st.button("Generate Output"):
    with torch.no_grad():
        token_ids = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt")
        output_ids = model.generate(
            token_ids.to(model.device),
            max_new_tokens=512,
            do_sample=True,
            temperature=0.3
        )

    output = tokenizer.decode(output_ids[0][token_ids.size(1):])
    st.text("Generated Output:")
    st.write(output)