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app.py

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+import gradio as gr
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+
+# Load model and tokenizer
+model_name = "t5-small"
+tokenizer = T5Tokenizer.from_pretrained(model_name)
+model = T5ForConditionalGeneration.from_pretrained(model_name)
+
+# Function to summarize text
+def summarize_text(text, max_length=100):
+    input_text = "summarize: " + text
+    inputs = tokenizer.encode(input_text, return_tensors="pt", max_length=512, truncation=True)
+    
+    summary_ids = model.generate(inputs, max_length=max_length, min_length=30, length_penalty=2.0, num_beams=4)
+    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+
+    return summary
+
+# Gradio UI
+iface = gr.Interface(
+    fn=summarize_text,
+    inputs=[gr.Textbox(label="Enter Text to Summarize"), gr.Slider(50, 200, step=10, label="Max Length")],
+    outputs="text",
+    title="Text Summarization App",
+    description="This app summarizes long texts using the T5 Transformer model.",
+)
+
+# Launch the app
+if __name__ == "__main__":
+    iface.launch()

requirements.txt

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+transformers
+torch
+gradio