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app.py

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+import gradio as gr
+from transformers import pipeline
+
+# Use the same text-generation model as the existing project
+generator = pipeline("text-generation", model="gpt2")
+
+
+def generate_text(prompt: str, max_length: int = 150):
+    prompt = prompt or ""
+    if not prompt.strip():
+        return "Please enter a prompt to generate text."
+
+    result = generator(
+        prompt,
+        max_length=max_length,
+        do_sample=False,
+        truncation=True,
+    )
+    return result[0]["generated_text"]
+
+
+demo = gr.Blocks()
+
+with demo:
+    gr.Markdown("# Text Generator")
+    gr.Markdown(
+        "Enter a prompt below and click Generate Text to produce output using GPT-2."
+    )
+
+    with gr.Row():
+        prompt_input = gr.Textbox(
+            label="Prompt",
+            placeholder="Enter your prompt here...",
+            lines=5,
+        )
+        max_length = gr.Slider(
+            minimum=50,
+            maximum=500,
+            step=10,
+            value=150,
+            label="Max generated length",
+        )
+
+    output_text = gr.Textbox(label="Generated Text", lines=12)
+
+    generate_button = gr.Button("Generate Text")
+    generate_button.click(
+        generate_text,
+        inputs=[prompt_input, max_length],
+        outputs=output_text,
+    )
+
+    gr.Markdown(
+        "---\nThis Gradio app keeps the same model functionality as the current text generation backend."
+    )
+
+
+demo.launch()

requirements.txt

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+gradio
+transformers
+torch