Update app.py

app.py

@@ -25,23 +25,26 @@ config = AutoConfig.from_pretrained(MODEL)
 
 # create classifier function
 def classify_compliant(text):
-  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 = 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)
 
-  # 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]]
-    #s = scores[ranking[i]]
+      l = config.id2label[ranking[0]]
+        #s = scores[ranking[i]]
     #probs[l] = np.round(float(s), 4)
-  return l
+      return l
 
 
 #build the Gradio app