Update app.py

app.py

@@ -22,25 +22,37 @@ model.config.id2label={
     "4": "Openness",}
 
 def Personality_Detection_from_reviews_submitted (model_input: str) -> Dict[str, float]:
-    # Encoding input data
-    dict_custom={}
-    Preprocess_part1=model_input[:len(model_input)]
-    Preprocess_part2=model_input[len(model_input):]
-    dict1=tokenizer.encode_plus(Preprocess_part1,max_length=1024,padding=True,truncation=True)
-    dict2=tokenizer.encode_plus(Preprocess_part2,max_length=1024,padding=True,truncation=True)
-    dict_custom['input_ids']=[dict1['input_ids'],dict1['input_ids']]
-    dict_custom['token_type_ids']=[dict1['token_type_ids'],dict1['token_type_ids']]
-    dict_custom['attention_mask']=[dict1['attention_mask'],dict1['attention_mask']]
-    outs = model(torch.tensor(dict_custom['input_ids']), token_type_ids=None, attention_mask=torch.tensor(dict_custom['attention_mask']))
-    b_logit_pred = outs[0]
-    pred_label = torch.sigmoid(b_logit_pred)
-    ret ={
-        "Extroversion": float(pred_label[0][0]),
-        "Neuroticism": float(pred_label[0][1]),
-        "Agreeableness": float(pred_label[0][2]),
-        "Conscientiousness": float(pred_label[0][3]),
-        "Openness": float(pred_label[0][4]),}
-    return ret
+    if len(model_input)<20:
+        ret ={
+            "Extroversion": float(0),
+            "Neuroticism": float(0),
+            "Agreeableness": float(0),
+            "Conscientiousness": float(0),
+            "Openness": float(0),}
+        return ret
+        
+
+
+    else:
+        # Encoding input data
+        dict_custom={}
+        Preprocess_part1=model_input[:len(model_input)]
+        Preprocess_part2=model_input[len(model_input):]
+        dict1=tokenizer.encode_plus(Preprocess_part1,max_length=1024,padding=True,truncation=True)
+        dict2=tokenizer.encode_plus(Preprocess_part2,max_length=1024,padding=True,truncation=True)
+        dict_custom['input_ids']=[dict1['input_ids'],dict1['input_ids']]
+        dict_custom['token_type_ids']=[dict1['token_type_ids'],dict1['token_type_ids']]
+        dict_custom['attention_mask']=[dict1['attention_mask'],dict1['attention_mask']]
+        outs = model(torch.tensor(dict_custom['input_ids']), token_type_ids=None, attention_mask=torch.tensor(dict_custom['attention_mask']))
+        b_logit_pred = outs[0]
+        pred_label = torch.sigmoid(b_logit_pred)
+        ret ={
+            "Extroversion": float(pred_label[0][0]),
+            "Neuroticism": float(pred_label[0][1]),
+            "Agreeableness": float(pred_label[0][2]),
+            "Conscientiousness": float(pred_label[0][3]),
+            "Openness": float(pred_label[0][4]),}
+        return ret
 model_input = gr.Textbox("Input text here (Note: This model is trained to classify Big Five Personality Traits From Expository text features)", show_label=False)
 model_output = gr.Label(" Big-Five personality traits Result", num_top_classes=6, show_label=True, label="Big-Five personality traits Labels assigned to this text based on its features")
 examples = [