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app.py

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+
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+from peft import PeftModel
+import torch
+
+# Load the tokenizer
+tokenizer = AutoTokenizer.from_pretrained("rxpbtn21/t5-small-lora-summarizer")
+
+# Load the base model and then the LoRA adapter
+base_model = AutoModelForSeq2SeqLM.from_pretrained("t5-small", device_map="auto")
+model = PeftModel.from_pretrained(base_model, "rxpbtn21/t5-small-lora-summarizer")
+model.eval()
+
+def summarize(text):
+    inputs = tokenizer(text, max_length=512, truncation=True, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model.generate(inputs["input_ids"].to(model.device), num_beams=4, max_new_tokens=128, early_stopping=True)
+    summary = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return summary
+
+# Create Gradio interface
+iface = gr.Interface(fn=summarize, inputs="text", outputs="text", title="LoRA Fine-tuned T5-small Summarizer")
+
+# Launch the interface
+iface.launch(share=False)

requirements.txt

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+
+torch
+transformers
+peft
+accelerate
+gradio
+sentencepiece