Add Gradio app for LoRA GPT-2

app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+
+# Load base GPT-2 + your LoRA adapter from Hugging Face Hub
+base_model = AutoModelForCausalLM.from_pretrained("gpt2")
+tokenizer = AutoTokenizer.from_pretrained("n-sudheer/ns-lora-gpt2-demo")
+model = PeftModel.from_pretrained(base_model, "n-sudheer/ns-lora-gpt2-demo")
+
+def generate_text(prompt, max_length=50):
+    inputs = tokenizer(prompt, return_tensors="pt")
+    outputs = model.generate(
+        **inputs,
+        max_length=max_length,
+        do_sample=True,
+        top_k=50
+    )
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+demo = gr.Interface(
+    fn=generate_text,
+    inputs=[
+        gr.Textbox(label="Prompt"),
+        gr.Slider(10, 200, value=50, step=5, label="Max length")
+    ],
+    outputs=gr.Textbox(label="Generated Text"),
+    title="LoRA GPT-2 Demo",
+    description="A GPT-2 model fine-tuned with LoRA, deployed on Hugging Face Spaces."
+)
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+
+fastapi==0.117.1
+huggingface-hub==0.35.1
+pydantic==2.11.9
+pydantic_core==2.33.2
+torch==2.8.0
+transformers==4.56.2
+uvicorn==0.37.0
+peft==0.17.1
+gradio