new

app.py

@@ -1,9 +1,11 @@
 import gradio as gr
 import os
-os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
-os.environ["CUDA_VISIBLE_DEVICES"]="0"
+
+os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
+os.environ["CUDA_VISIBLE_DEVICES"] = "0"
 from transformers import AutoTokenizer, TFAutoModelForSequenceClassification, TextClassificationPipeline
 
+
 tokenizer = AutoTokenizer.from_pretrained("dipesh/Intent-Classification-Bert-Base-Cased")
 
 model = TFAutoModelForSequenceClassification.from_pretrained("dipesh/Intent-Classification-Bert-Base-Cased")
@@ -13,8 +15,17 @@ intent_classifier = TextClassificationPipeline(model=model, tokenizer=tokenizer,
 
 
 def predict(input_text):
+    ans = "intent_classifier(input_text)"
+    # list of questions words
+    question_words = ['will', 'is', 'when', 'may', 'should', 'would', 'which', 'shall', 'does', 'why', 'can', 'whose',
+                      'do', 'was', 'where', 'who', 'might', 'how', 'must', 'whom', 'are', 'did', 'were', 'what',
+                      'could']
+    question_words = set(question_words)
+    if ans.split()[0] in question_words:
+        ans += "?"
     ans = intent_classifier(input_text)
-    return ans[0]['label']
+
+    return ans[0]['label'], ans[0]['score']
 
 
 iface = gr.Interface(fn=predict, inputs="text", outputs="text", title="Intent Classifier",