new query working

app.py

@@ -123,7 +123,7 @@ if 'is_initialized' not in st.session_state:
     st.session_state.bert_model = BertModel.from_pretrained("bert-base-uncased", ).to('cuda')
     st.session_state.paraphrase_tokenizer = AutoTokenizer.from_pretrained("Vamsi/T5_Paraphrase_Paws")
     st.session_state.paraphrase_model = AutoModelForSeq2SeqLM.from_pretrained("Vamsi/T5_Paraphrase_Paws").to('cuda')
-
+    print(str(st.session_state.paraphrase_model ))
 if 'list_count' in st.session_state:
     st.write(f'The number of elements at the top level of the hierarchy: {st.session_state.list_count }')
     if 'paragraph_sentence_encodings' not in st.session_state:
@@ -157,7 +157,10 @@ if 'paragraph_sentence_encodings' in st.session_state:
     query = st.text_input("Enter your query")
 
     if query:
-        if 'paragraph_scores' not in st.session_state:
+        if 'prev_query' not in st.session_state or st.session_state.prev_query != query:
+            st.session_state.prev_query = query
+            if 'paraphrased_paragrpahs' in st.session_state:
+                del st.session_state['paraphrased_paragrpahs']
             query_tokens = st.session_state.bert_tokenizer(query, return_tensors="pt", padding=True, truncation=True).to(
                 'cuda')
             with torch.no_grad():  # Disable gradient calculation for inference