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import streamlit as st
import torch
from transformers import BioGptTokenizer, BioGptForCausalLM, set_seed
# Load model and tokenizer
@st.cache_resource
def load_model():
tokenizer = BioGptTokenizer.from_pretrained("microsoft/biogpt")
model = BioGptForCausalLM.from_pretrained("microsoft/biogpt")
return model, tokenizer
# Initialize model and tokenizer
model, tokenizer = load_model()
# Streamlit Interface
st.title("BioGPT Text Generator")
st.markdown("Generate text completions using **BioGPT**. Enter an incomplete sentence below and let BioGPT complete it!")
# User Input
sentence = st.text_area("Enter an incomplete sentence:",
placeholder="Chest pain can be caused by various factors such as"
)
if st.button("Generate Text"):
if not sentence.strip():
st.error("Please enter a valid sentence to generate text.")
else:
with st.spinner("Generating text..."):
# Tokenize input
inputs = tokenizer(sentence, return_tensors="pt")
# Set random seed for reproducibility
set_seed(42)
# Generate text
with torch.no_grad():
beam_output = model.generate(
**inputs,
max_length=1024,
do_sample=True,
top_p=0.9,
top_k=50
)
# Decode and display the result
generated_text = tokenizer.decode(beam_output[0], skip_special_tokens=True)
st.subheader("Generated Text:")
st.write(generated_text)
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