Create app.py

app.py

@@ -0,0 +1,33 @@
+# Import Gradio and the Hugging Face Transformers pipeline
+import gradio as gr
+from transformers import pipeline
+
+# Load a text-generation pipeline with the DistilGPT-2 model.
+# DistilGPT-2 is a distilled (compressed) version of GPT-2, so it's faster and lightweight:contentReference[oaicite:3]{index=3}.
+generator = pipeline("text-generation", model="distilgpt2")  # This downloads the model weights if not already available
+
+# Define a function that uses the generator to produce text based on the input prompt.
+def generate_text(prompt):
+    # Use the text-generation pipeline to continue the prompt.
+    # We set a max_length to limit the output length for practicality.
+    result = generator(prompt, max_length=100, num_return_sequences=1)[0]["generated_text"]
+    return result
+
+# Set up the Gradio interface:
+# - Input: a textbox for the prompt (single-line or a short prompt, so we use lines=2).
+# - Output: a textbox for the generated text.
+# - We also add a title and description for user guidance.
+input_prompt = gr.Textbox(lines=2, label="Prompt", placeholder="Enter a text prompt...")
+output_text = gr.Textbox(label="Generated Text")
+
+demo = gr.Interface(
+    fn=generate_text,
+    inputs=input_prompt,
+    outputs=output_text,
+    title="🤖 DistilGPT-2 Text Generator",
+    description="**Description:** Enter a prompt, and the DistilGPT-2 language model will continue the text. "
+                "This demonstrates basic text generation using a small pre-trained GPT-2 model."
+)
+
+# Launch the app (in a Hugging Face Space, this will run automatically).
+demo.launch()