Update app.py

app.py

@@ -103,10 +103,11 @@ def generateDistlGPT2(starting_text):
 # grad.Interface(generateDistlGPT2, inputs=txt, outputs=out).launch()
 
 #Text Generation
-from transformers import AutoModelWithLMHead, AutoTokenizer
-import gradio as grad
-text2text_tkn = AutoTokenizer.from_pretrained("mrm8488/t5-base-finetuned-question-generation-ap")
-mdl = AutoModelWithLMHead.from_pretrained("mrm8488/t5-base-finetuned-question-generation-ap")
+#Question Generation
+# from transformers import AutoModelWithLMHead, AutoTokenizer
+# import gradio as grad
+# text2text_tkn = AutoTokenizer.from_pretrained("mrm8488/t5-base-finetuned-question-generation-ap")
+# mdl = AutoModelWithLMHead.from_pretrained("mrm8488/t5-base-finetuned-question-generation-ap")
 def text2text(context,answer):
     input_text = "answer: %s  context: %s </s>" % (answer, context)
     features = text2text_tkn ([input_text], return_tensors='pt')
@@ -115,7 +116,28 @@ def text2text(context,answer):
                max_length=64)
     response=text2text_tkn.decode(output[0])
     return response
-context=grad.Textbox(lines=10, label="English", placeholder="Context")
-ans=grad.Textbox(lines=1, label="Answer")
-out=grad.Textbox(lines=1, label="Genereated Question")
-grad.Interface(text2text, inputs=[context,ans], outputs=out).launch()
+# context=grad.Textbox(lines=10, label="English", placeholder="Context")
+# ans=grad.Textbox(lines=1, label="Answer")
+# out=grad.Textbox(lines=1, label="Genereated Question")
+# grad.Interface(text2text, inputs=[context,ans], outputs=out).launch()
+
+#T5 summaryzer
+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()