app.py

import streamlit as st
from transformers import GPT2LMHeadModel, GPT2Tokenizer

# Load the pre-trained GPT-2 model and tokenizer
model_name = "gpt2"
tokenizer = GPT2Tokenizer.from_pretrained(model_name)
model = GPT2LMHeadModel.from_pretrained(model_name)

# Define a text prompt
prompt = st.text_area('Enter the prompt!')

# Encode the prompt text and convert to tensor
if prompt:
    input_ids = tokenizer.encode(prompt, return_tensors='pt')
    
    # Generate text using the GPT-2 model
    output = model.generate(
        input_ids,
        max_length=100,  # Maximum length of the generated text
        num_return_sequences=1,  # Number of sequences to generate
        no_repeat_ngram_size=2,  # Avoid repeating n-grams
        top_k=50,  # Top-K sampling
        top_p=0.95,  # Top-p (nucleus) sampling
        temperature=0.7  # Sampling temperature
    )
    
    # Decode the generated text to string
    generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
    
    # Print the generated text
    st.write(generated_text)