app.py

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

title = "GPT2"
description = "Gradio Demo for OpenAI GPT2. To use it, simply add your text, or click one of the examples to load them."
article = "<p style='text-align: center'><a href='https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf' target='_blank'>Language Models are Unsupervised Multitask Learners</a></p>"

examples = [
    ['Paris is the capital of', "gpt2-medium"]
]

# Initialize models dictionary to cache loaded models
models = {}

def load_model(model_name):
    if model_name not in models:
        tokenizer = GPT2Tokenizer.from_pretrained(model_name)
        model = GPT2LMHeadModel.from_pretrained(model_name)
        models[model_name] = pipeline("text-generation", model=model, tokenizer=tokenizer)
    return models[model_name]

def inference(text, model_name):
    # Map the model names to their Hugging Face identifiers
    model_map = {
        "distilgpt2": "distilgpt2",
        "gpt2-medium": "gpt2-medium",
        "gpt2-large": "gpt2-large",
        "gpt2-xl": "gpt2-xl"
    }
    
    # Get the correct model identifier
    hf_model_name = model_map.get(model_name, "distilgpt2")
    
    # Load the model (will be cached after first load)
    generator = load_model(hf_model_name)
    
    # Generate text
    generated = generator(text, max_length=50, num_return_sequences=1)
    return generated[0]['generated_text']

iface = gr.Interface(
    inference, 
    [
        gr.Textbox(label="Input"),
        gr.Dropdown(
            choices=["distilgpt2", "gpt2-medium", "gpt2-large", "gpt2-xl"], 
            value="gpt2-medium", 
            label="Model"
        )
    ], 
    gr.Textbox(label="Output"),
    examples=examples,
    article=article,
    title=title,
    description=description
)

iface.launch(enable_queue=True)