Create app.py

app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+# Load the model and tokenizer from Hugging Face Model Hub
+tokenizer = AutoTokenizer.from_pretrained("zeyadusf/FlanT5Summarization-samsum")
+model = AutoModelForSeq2SeqLM.from_pretrained("zeyadusf/FlanT5Summarization-samsum")
+
+def summarize(text):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True)
+    summary_ids = model.generate(inputs.input_ids, max_length=512, min_length=64, length_penalty=2.0, num_beams=4, early_stopping=True)
+    return tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+
+# Define the Gradio interface
+iface = gr.Interface(fn=summarize, inputs="text", outputs="text", title="Summarization with PEFT")
+iface.launch()