app.py

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

# Load models and tokenizers
models = {
    "GPT From Scratch (benchaffe/shakespeare-gpt-mini)": "benchaffe/shakespeare-gpt-mini",
    "Fine-tuned DistilGPT2 (benchaffe/shakespeare-distilgpt2)": "benchaffe/shakespeare-distilgpt2"
}

model_objects = {}
tokenizer_objects = {}

device = "cuda" if torch.cuda.is_available() else "cpu"

for name, path in models.items():
    tokenizer = AutoTokenizer.from_pretrained(path)
    model = AutoModelForCausalLM.from_pretrained(path).to(device)
    model.eval()
    model_objects[name] = model
    tokenizer_objects[name] = tokenizer

# Generation function
def generate_comparison(prompt, max_length, temperature, top_p, top_k, do_sample):
    results = {}
    for name in models:
        tokenizer = tokenizer_objects[name]
        model = model_objects[name]

        inputs = tokenizer(prompt, return_tensors="pt").to(device)
        output = model.generate(
            **inputs,
            max_length=max_length,
            temperature=temperature,
            top_p=top_p,
            top_k=top_k,
            do_sample=do_sample,
            pad_token_id=tokenizer.eos_token_id,
        )
        generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
        results[name] = generated_text
    return [results[name] for name in models]

# Examples
example_prompts = [
    "What light through yonder window breaks",
    "To be or not to be, that is the question",
    "Hark! Who goes there?",
    "My love is deep; the more I give to thee",
    "Thou art more lovely and more temperate"
]

with gr.Blocks(title="Shakespeare Model Comparison") as demo:
    gr.Markdown("# 🧠 Shakespeare GPT Model Comparison")
    gr.Markdown("Compare outputs from two models: one trained from scratch and one fine-tuned from DistilGPT2. Adjust generation parameters below.")

    with gr.Row():
        with gr.Column():
            prompt = gr.Textbox(label="Input Prompt", placeholder="Enter a Shakespearean prompt here...")
            example = gr.Dropdown(example_prompts, label="Select an Example Prompt")
            example.change(fn=lambda e: gr.update(value=e), inputs=example, outputs=prompt)

            max_length = gr.Slider(32, 256, value=80, step=8, label="Max Length")
            temperature = gr.Slider(0.1, 1.5, value=0.8, step=0.1, label="Temperature")
            top_p = gr.Slider(0.1, 1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)")
            top_k = gr.Slider(0, 100, value=50, step=5, label="Top-k")
            do_sample = gr.Checkbox(value=True, label="Use Sampling (if unchecked = greedy decoding)")

            submit_btn = gr.Button("Generate")
        
        with gr.Column():
            outputs = [gr.Textbox(label=name) for name in models]

    submit_btn.click(fn=generate_comparison, inputs=[prompt, max_length, temperature, top_p, top_k, do_sample], outputs=outputs)

demo.launch()