Create app.py

app.py

@@ -0,0 +1,91 @@
+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()