Update app.py

app.py

@@ -25,26 +25,26 @@ config = AutoConfig.from_pretrained(MODEL)
 
 # create classifier function
 def classify_compliant(text):
-  if len(text)==0:
-      return "Cannot Categorize the complaint"
-  else:
       text = cleaned_complaints(text)
-      text = preprocess(text)
-      encoded_input = tokenizer(text, return_tensors='pt')
-      output = model(**encoded_input)
-      scores = output[0][0].detach().numpy()
-      scores = softmax(scores)
+      if len(text)==0:
+          return "Cannot Categorize the Complaint"
+      else:
+          text = preprocess(text)
+          encoded_input = tokenizer(text, return_tensors='pt')
+          output = model(**encoded_input)
+          scores = output[0][0].detach().numpy()
+          scores = softmax(scores)
 
-      # Print labels and scores
-      probs = {}
-      ranking = np.argsort(scores)
-      ranking = ranking[::-1]
+          # Print labels and scores
+          probs = {}
+          ranking = np.argsort(scores)
+          ranking = ranking[::-1]
 
   
-      l = config.id2label[ranking[0]]
+          l = config.id2label[ranking[0]]
         #s = scores[ranking[i]]
     #probs[l] = np.round(float(s), 4)
-      return l
+          return l
 
 
 #build the Gradio app