Update app.py

app.py

@@ -1,15 +1,25 @@
-from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
+from transformers import AutoTokenizer, AutoModelWithLMHead
 import gradio as grad
-import ast
+text2text_tkn = AutoTokenizer.from_pretrained("deep-learning-analytics/wikihow-t5-small")
+mdl = AutoModelWithLMHead.from_pretrained("deep-learning-analytics/wikihow-t5-small")
 
-mdl_name = "distilbert-base-cased-distilled-squad"
-my_pipeline = pipeline('question-answering', model=mdl_name, tokenizer=mdl_name)
 
+def text2text_summary(para):
+    initial_txt = para.strip().replace("\n","")
+    tkn_text = text2text_tkn.encode(initial_txt, return_tensors="pt")
 
-def answer_question(question,context):
-    text= "{"+"'question': '"+question+"','context': '"+context+"'}"
-    
-    di=ast.literal_eval(text)
-    response = my_pipeline(di)
+    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
-grad.Interface(answer_question, inputs=["text","text"], outputs="text").launch() 
+
+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()