Update app.py

app.py

@@ -1,6 +1,6 @@
 import streamlit as st
 
-from transformers import pipeline
+#from transformers import pipeline
 from pinecone import Pinecone, ServerlessSpec
 from sentence_transformers import SentenceTransformer, util
 
@@ -10,7 +10,7 @@ bi_encoder.max_seq_length = 256     # Truncate long documents to 256 tokens
 
 # Store the index as a variable
 INDEX_NAME = 'cl-search-idx'
-NAMESPACE = 'webpages'
+
 pc_api_key= '3f916d01-2a69-457d-85eb-966c5d1849a8'  #AWS
 pc = Pinecone(api_key=pc_api_key)
 index = pc.Index(name=INDEX_NAME)
@@ -25,11 +25,18 @@ def query_from_pinecone(index,namespace, question_embedding, top_k=3):
       include_metadata=True   # gets the metadata (dates, text, etc)
     ).get('matches')
 
-
 QUESTION=st.text_area('Ask a question -e.g How to prepare for Verbal section for CAT?')  ##' How to prepare for Verbal section ?'
-question_embedding = bi_encoder.encode(QUESTION, convert_to_tensor=True)
-resp= query_from_pinecone(index,NAMESPACE, question_embedding.tolist(), 3)
-out= resp[0]['metadata']['text'] 
-#+ '\n*************\n'+  resp[1]['metadata']['text'] + '\n*************\n'+ resp[2]['metadata']['text']
 
-st.write(out)
+if QUESTION:
+    question_embedding = bi_encoder.encode(QUESTION, convert_to_tensor=True)
+
+    ns='full'
+    resp= query_from_pinecone(index,ns, question_embedding.tolist(), 3)
+    if len(resp)>0:
+        out= resp[0]['metadata']['data']
+        url= "Matching url "+resp[0]['id']
+        #+ '\n*************\n'+  resp[1]['metadata']['text'] + '\n*************\n'+ resp[2]['metadata']['text']
+        st.write(url)
+        st.write(out)
+    else:
+        st.write("No matches for query")