Create app.py

app.py

@@ -0,0 +1,77 @@
+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")
+            def set_example(e): return gr.Textbox.update(value=e)
+            example.change(fn=set_example, 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()