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app.py

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+from transformers import AutoTokenizer, AutoModelWithLMHead
+import gradio as grad
+text2text_tkn = AutoTokenizer.from_pretrained("deep-learning-analytics/wikihow-t5-small")
+mdl = AutoModelWithLMHead.from_pretrained("deep-learning-analytics/wikihow-t5-small")
+
+
+def text2text_summary(para):
+    initial_txt = para.strip().replace("\n","")
+    tkn_text = text2text_tkn.encode(initial_txt, return_tensors="pt")
+
+    tkn_ids = mdl.generate(
+            tkn_text,
+            max_length=250, 
+            num_beams=5,
+            repetition_penalty=2.5, 
+           
+            early_stopping=True
+        )
+
+    response = text2text_tkn.decode(tkn_ids[0], skip_special_tokens=True)
+    return response
+
+para=grad.Textbox(lines=10, label="Paragraph", placeholder="Copy paragraph")
+out=grad.Textbox(lines=1, label="Summary")
+grad.Interface(text2text_summary, inputs=para, outputs=out).launch()

requirements.txt

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+gradio
+transformers
+torch
+transformers[sentencepiece]