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streamlit_app.py
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streamlit_app.py
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import streamlit as st
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
# Load the Phi 2 model and tokenizer
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)