Update app.py

app.py

@@ -3,17 +3,19 @@ import streamlit as st
 from transformers import pipeline
 from sentence_transformers import SentenceTransformer
 
-PINECONE_KEY = st.secrets["PINECONE_KEY"]  # app.pinecone.io
+PINECONE_KEY = st.secrets["PINECONE_API_KEY"]  # app.pinecone.io
+PINE_CONE_ENVIRONMENT = st.secrets["PINE_CONE_ENVIRONMENT"]  # app.pinecone.io
 
 @st.experimental_singleton
 def init_pinecone():
-    pinecone.init(api_key=PINECONE_KEY, environment="us-west1-gcp")  # get a free api key from app.pinecone.io
-    return pinecone.Index("extractive-question-answering")
+    pinecone.init(api_key=PINECONE_KEY, environment=PINE_CONE_ENVIRONMENT)  # get a free api key from app.pinecone.io
+    return pinecone.Index("dompany-description")
     
 @st.experimental_singleton
 def init_models():
-    retriever = SentenceTransformer("multi-qa-MiniLM-L6-cos-v1")
-    model_name = 'deepset/electra-base-squad2'
+    #retriever = SentenceTransformer("multi-qa-MiniLM-L6-cos-v1")
+    retriever = SentenceTransformer('Xenova/text-embedding-ada-002')
+    model_name = 'Xenova/text-embedding-ada-002'
     reader = pipeline(tokenizer=model_name, model=model_name, task='question-answering')
     return retriever, reader
 
@@ -54,15 +56,15 @@ def run_query(query):
         xc = st.session_state.index.query(xq, top_k=3, include_metadata=True)
     except:
         # force reload
-        pinecone.init(api_key=PINECONE_KEY, environment="us-west1-gcp")
-        st.session_state.index = pinecone.Index("extractive-question-answering")
+        pinecone.init(api_key=PINECONE_KEY, environment=PINE_CONE_ENVIRONMENT)
+        st.session_state.index = pinecone.Index("dompany-description")
         xc = st.session_state.index.query(xq, top_k=3, include_metadata=True)
 
     results = []
     for match in xc['matches']:
         answer = reader(question=query, context=match["metadata"]['context'])
-        answer["title"] = match["metadata"]['title']
-        answer["context"] = match["metadata"]['context']
+        answer["title"] = match["metadata"]['name']
+        answer["context"] = match["metadata"]['name']
         results.append(answer)
 
     sorted_result = sorted(results, key=lambda x: x['score'], reverse=True)