Update app.py

app.py

@@ -64,6 +64,8 @@ for mall, file_name in competitor_files.items():
 
 # ✅ Load Data into ChromaDB Vector Store with File Error Handling
 st.info("🔄 Training AI embeddings on KPI and Competitor data...")
+vectorstore = None  # Initialize vectorstore to prevent NameError
+
 try:
     loader_kpi = UnstructuredExcelLoader(kpi_file, mode="elements")
     kpi_docs = loader_kpi.load()
@@ -79,15 +81,17 @@ try:
 
     all_docs = kpi_docs + competitor_docs
 
-    # ✅ Use RecursiveCharacterTextSplitter for better chunking
-    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
-    split_docs = text_splitter.split_documents(all_docs)
+    if len(all_docs) > 0:
+        # ✅ Use RecursiveCharacterTextSplitter for better chunking
+        text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
+        split_docs = text_splitter.split_documents(all_docs)
 
-    # ✅ Store in ChromaDB
-    embeddings = OpenAIEmbeddings(api_key=openai_api_key)
-    vectorstore = Chroma.from_documents(split_docs, embeddings)
-    
-    st.success("✅ AI embeddings trained and stored in ChromaDB successfully!")
+        # ✅ Store in ChromaDB
+        embeddings = OpenAIEmbeddings(api_key=openai_api_key)
+        vectorstore = Chroma.from_documents(split_docs, embeddings)
+        st.success("✅ AI embeddings trained and stored in ChromaDB successfully!")
+    else:
+        st.warning("⚠️ No valid documents were found to train embeddings.")
 except Exception as e:
     st.error(f"❌ Error training AI embeddings with ChromaDB: {e}")
 
@@ -133,8 +137,11 @@ if selected_mall:
             st.success(f"✅ Retrieved {len(competitor_list)} competitors from dataset.")
         else:
             st.warning("⚠️ No competitor data found. Using AI embeddings to predict competitors.")
-            search_results = vectorstore.similarity_search(f"Find competitors for {store_category} in {selected_mall}")
-            competitor_list = [doc.page_content for doc in search_results]
+            if vectorstore:
+                search_results = vectorstore.similarity_search(f"Find competitors for {store_category} in {selected_mall}")
+                competitor_list = [doc.page_content for doc in search_results]
+            else:
+                competitor_list = ["No AI embeddings available."]
 
         st.subheader("🔎 Competitors in This Mall")
         for competitor in competitor_list: