Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline, set_seed
+
+# Initialize the text-generation pipeline with DistilGPT2 on CPU.
+generator = pipeline("text-generation", model="distilgpt2", device=-1)
+set_seed(42)
+
+def generate_text(prompt, stop_choice):
+    # Map the dropdown choice to an actual stop character.
+    mapping = {"Period": ".", "Space": " "}
+    stop_token = mapping.get(stop_choice, "")
+    # Generate text – we set max_length relative to the prompt length.
+    output = generator(prompt, max_length=len(prompt.split()) + 50, num_return_sequences=1)[0]["generated_text"]
+    # Look for the stop token in the generated output (after the prompt).
+    idx = output.find(stop_token, len(prompt))
+    if idx != -1:
+        # If found, return the output up to and including the stop token.
+        return output[: idx + len(stop_token)]
+    else:
+        # Otherwise, return the full generated text.
+        return output
+
+# Define a simple Gradio interface:
+iface = gr.Interface(
+    fn=generate_text,
+    inputs=[
+        gr.inputs.Textbox(lines=4, placeholder="Enter your prompt here...", label="Prompt"),
+        gr.inputs.Dropdown(choices=["Period", "Space"], label="Stop Token")
+    ],
+    outputs="text",
+    title="DistilGPT2 Text Generation",
+    description="Enter a prompt and select a stop token (Period or Space) to halt generation."
+)
+
+if __name__ == "__main__":
+    iface.launch()