app.py

import gradio as gr
import torch
from transformers import GPT2Tokenizer, GPT2LMHeadModel

# Load the pre-trained GPT-2 model and tokenizer
model_name = "gpt2"  # You can change this to another model if needed
tokenizer = GPT2Tokenizer.from_pretrained(model_name)
model = GPT2LMHeadModel.from_pretrained(model_name)

def generate_text(prompt):
    # Encode the input text and generate a continuation
    input_ids = tokenizer.encode(prompt, return_tensors="pt")
    output = model.generate(input_ids, max_length=100, num_return_sequences=1, pad_token_id=50256)
    generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
    
    # Save the generated text to a text file
    with open("generated_text.txt", "w") as file:
        file.write(generated_text)
    
    return generated_text

iface = gr.Interface(fn=generate_text, inputs="text", outputs="text")
iface.launch()