Update app.py

app.py

@@ -86,25 +86,26 @@ parameters = {
 }
 
 project_id = os.getenv("IBM_PROJECT_ID")
-space_id = os.getenv("IBM_SPACE_ID")
+#space_id = os.getenv("IBM_SPACE_ID")
 
 from ibm_watsonx_ai.foundation_models import ModelInference
 
-model = ModelInference(
-	model_id = model_id,
-	params = parameters,
-	credentials = get_credentials(),
-	project_id = project_id,
-	space_id = space_id
-	)
+#model = ModelInference(
+#	model_id = model_id,
+#	params = parameters,
+#	credentials = get_credentials(),
+#	project_id = project_id,
+#	space_id = space_id
+#	)
 
 from ibm_watsonx_ai.client import APIClient
 
-wml_credentials = get_credentials()
-client = APIClient(credentials=wml_credentials, project_id=project_id)  #, space_id=space_id)
+if "client" not in st.session_state:
+    wml_credentials = get_credentials()
+    st.session_state.client = APIClient(credentials=wml_credentials, project_id=project_id) 
 
 vector_index_id = VECTOR_DB
-vector_index_details = client.data_assets.get_details(vector_index_id)
+vector_index_details = st.session_state.client.data_assets.get_details(vector_index_id)
 vector_index_properties = vector_index_details["entity"]["vector_index"]
 
 top_n = 20 if vector_index_properties["settings"].get("rerank") else int(vector_index_properties["settings"]["top_k"])
@@ -145,7 +146,7 @@ import random
 import string
 
 def hydrate_chromadb():
-    data = client.data_assets.get_content(vector_index_id)
+    data = st.session_state.client.data_assets.get_content(vector_index_id)
     content = gzip.decompress(data)
     stringified_vectors = str(content, "utf-8")
     vectors = json.loads(stringified_vectors)
@@ -192,11 +193,12 @@ def hydrate_chromadb():
     )
     return collection
 
-chroma_collection = hydrate_chromadb()
+if "chroma_collection" not in st.session_state:
+    st.session_state.chroma_collection = hydrate_chromadb()
 
 def proximity_search( question ):
     query_vectors = emb.embed_query(question)
-    query_result = chroma_collection.query(
+    query_result = st.session_state.chroma_collection.query(
         query_embeddings=query_vectors,
         n_results=top_n,
         include=["documents", "metadatas", "distances"]
@@ -205,7 +207,7 @@ def proximity_search( question ):
     documents = list(reversed(query_result["documents"][0]))
 
     if vector_index_properties["settings"].get("rerank"):
-        documents = rerank(client, documents, question, vector_index_properties["settings"]["top_k"])
+        documents = rerank(st.session_state.client, documents, question, vector_index_properties["settings"]["top_k"])
 
     return "\n".join(documents)