Update app.py

app.py

@@ -13,6 +13,7 @@ tokenizer1 = RobertaTokenizer.from_pretrained('roberta-base')
 model1 = RobertaModel.from_pretrained('roberta-base')
 #pipe = pipeline("text-classification", model="thugCodeNinja/robertatemp")
 pipe = pipeline("text-classification",model=model,tokenizer=tokenizer)
+threshold = 0.5
 def process_text(input_text):
     if input_text:
         text = input_text
@@ -71,7 +72,7 @@ def process_text(input_text):
                 if similarity > threshold:
                     similar_articles.append({'Link': link, 'Similarity': similarity})
         similar_articles = sorted(similar_articles, key=lambda x: x['Similarity'], reverse=True)
-        threshold = 0.5  # Adjust the threshold as needed
+          # Adjust the threshold as needed
         return similar_articles[:5]
 
     # prediction = pipe([text])