Update app.py

app.py

@@ -12,45 +12,47 @@ model = AutoModelForQuestionAnswering.from_pretrained(model_name)
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 
 def bullete(text,wikipedia_language="en"):
-  question_words = STOPWORDS.union(set(['likes','play','.',',','like',"don't",'?','use','choose','important','better','?']))
-  lower_text = text.lower()
-  lower_text = word_tokenize(lower_text)
-  new_text = [i for i in lower_text if i not in question_words]
-  new_txt = "".join(new_text)
-  if wikipedia_language:
-    wikipedia.set_lang(wikipedia_language)
-
-  et_page = wikipedia.page(new_txt.replace(" ", ""))
-  title = et_page.title
-  content = et_page.content
-  page_url = et_page.url
-  linked_pages = et_page.links
-
-  text1 = content
-  final_out = re.sub(r'\=.+\=', '', text1)
-  result = list(filter(lambda x: x != '', final_out.split('\n\n')))
-
-  answer = []
   try:
-      for i in range(len(result[0].split('.'))):
-          nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
-          QA_input = {
-              'question': text,
-              'context': result[0].split('.')[i]
-          }
-          res = nlp(QA_input)
-          print(QA_input)
-          values = list(res.values())[3]
-          answer.append(values)
-  except:
-      gen_output = []
-      for i in range(len(answer)):
-          gen_output.append("* " + answer[i] + ".")
-      paraphrase = "\n".join(gen_output)
-      final_answer = paraphrase.replace(" ", " ")
-      return final_answer
-
+    question_words = STOPWORDS.union(set(['likes','play','.',',','like',"don't",'?','use','choose','important','better','?']))
+    lower_text = text.lower()
+    lower_text = word_tokenize(lower_text)
+    new_text = [i for i in lower_text if i not in question_words]
+    new_txt = "".join(new_text)
+    if wikipedia_language:
+      wikipedia.set_lang(wikipedia_language)
+  
+    et_page = wikipedia.page(new_txt.replace(" ", ""))
+    title = et_page.title
+    content = et_page.content
+    page_url = et_page.url
+    linked_pages = et_page.links
+  
+    text1 = content
+    final_out = re.sub(r'\=.+\=', '', text1)
+    result = list(filter(lambda x: x != '', final_out.split('\n\n')))
+  
+    answer = []
+    try:
+        for i in range(len(result[0].split('.'))):
+            nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
+            QA_input = {
+                'question': text,
+                'context': result[0].split('.')[i]
+            }
+            res = nlp(QA_input)
+            print(QA_input)
+            values = list(res.values())[3]
+            answer.append(values)
+    except:
+        gen_output = []
+        for i in range(len(answer)):
+            gen_output.append("* " + answer[i] + ".")
+        paraphrase = "\n".join(gen_output)
+        final_answer = paraphrase.replace(" ", " ")
+        return final_answer
 
+  except:
+      return "Please write correct wikipedia article name OR question"
 interface = gr.Interface(fn=bullete,
                          inputs="text",
                          outputs="text",