app.py

import streamlit as st
import os
from transformers import pipeline, set_seed
from huggingface_hub import HfFolder

import transformers
import torch


# Ensure the HF_TOKEN environment variable is set correctly
HF_TOKEN = os.getenv('HF_TOKEN')
if HF_TOKEN:
    HfFolder.save_token(HF_TOKEN)
else:
    st.warning("HF_TOKEN is not set. Proceeding without a token.")

# Use a valid model identifie

#generator = pipeline("text-generation", model="openai-community/gpt2")

generator = pipeline('text-generation', model='gpt2-large')

st.title("Text Generation")
st.write("Enter your text below.")
text = st.text_area("Your input")

st.write("Enter seed.")
seed_input = st.text_area("Set seed")


st.write("Enter max length.")
maxLength = st.text_area("max length")

# Convert seed input to integer
try:
    seed = int(seed_input)
    max_length = int(maxLength)
except ValueError:
    seed = 1
    max_length = 100
    
set_seed(seed)


if st.button("Generate Text"):
    # Use default values or handle None appropriately
    if seed is not None:
        set_seed(seed)

    if text and max_length:
        # Generate text
        out = generator(text, max_length=max_length, num_return_sequences=1)
        st.json(out)
        st.write(f"Reply: {out[0]['generated_text']}")