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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() |