Create app.py

app.py

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+import gradio as gr
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+
+# Load the model and tokenizer
+model_name = "t5-small"
+tokenizer = T5Tokenizer.from_pretrained(model_name)
+model = T5ForConditionalGeneration.from_pretrained(model_name)
+
+# Define the summarization function
+def summarize_text(text):
+    input_text = "summarize: " + text.strip()
+    input_ids = tokenizer.encode(input_text, return_tensors="pt", max_length=512, truncation=True)
+    summary_ids = model.generate(input_ids, max_length=150, min_length=40, length_penalty=2.0, num_beams=4, early_stopping=True)
+    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+    return summary
+
+# Gradio interface
+iface = gr.Interface(fn=summarize_text,
+                     inputs=gr.Textbox(lines=15, placeholder="Paste your text here..."),
+                     outputs=gr.Textbox(label="Summary"),
+                     title="T5 Text Summarizer",
+                     description="Enter any long English text to get a summarized version using the T5 model.")
+
+# Launch
+iface.launch()