app.py

import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline

def load_model(model_name="gpt2"):
    """Load a GPT-2 model and tokenizer from Hugging Face."""
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    model = AutoModelForCausalLM.from_pretrained(model_name)
    return pipeline("text-generation", model=model, tokenizer=tokenizer)

# Initialize the pipeline outside the function so it's loaded only once
generator = load_model()

def generate_text(prompt, max_length=100, temperature=1.0, top_p=0.9):
    """
    Generates text based on the prompt using a GPT-2 model.
    Args:
        prompt (str): Input text from the user.
        max_length (int): Max tokens in the prompt + generation.
        temperature (float): Controls randomness.
        top_p (float): Nucleus sampling hyperparameter.
    Returns:
        str: Generated text from GPT-2.
    """
    results = generator(
        prompt,
        max_length=max_length,
        temperature=temperature,
        top_p=top_p,
        num_return_sequences=1,
        # GPT-2 may not have a dedicated pad token, so eos_token_id used:
        pad_token_id=generator.tokenizer.eos_token_id 
    )
    return results[0]["generated_text"]

# Build the Gradio interface
with gr.Blocks() as demo:
    gr.Markdown(
        """
        # Educational GPT-2 Demo
        This demo demonstrates how a smaller Large Language Model (GPT-2) predicts text. 
        Change the parameters below to see how the model's output is affected:
        - **Max Length** controls the total number of tokens in the output.
        - **Temperature** controls randomness (higher means more creative/chaotic).
        - **Top-p** controls the diversity of tokens (lower means more conservative choices).
        """
    )

    with gr.Row():
        with gr.Column():
            prompt = gr.Textbox(
                lines=4,
                label="Prompt",
                placeholder="Type a prompt here",
                value="Once upon a time,"
            )
            max_len = gr.Slider(
                minimum=20, 
                maximum=200, 
                value=100, 
                step=1, 
                label="Max Length"
            )
            temp = gr.Slider(
                minimum=0.1, 
                maximum=2.0, 
                value=1.0, 
                step=0.1, 
                label="Temperature"
            )
            top_p = gr.Slider(
                minimum=0.1, 
                maximum=1.0, 
                value=0.9, 
                step=0.05, 
                label="Top-p"
            )
            generate_button = gr.Button("Generate")

        with gr.Column():
            output_box = gr.Textbox(
                label="Generated Text",
                lines=10
            )

    generate_button.click(
        fn=generate_text,
        inputs=[prompt, max_len, temp, top_p],
        outputs=[output_box]
    )

demo.launch()