Update app.py

app.py

@@ -89,7 +89,6 @@ from langchain.vectorstores import Chroma
 import gradio as gr
 from transformers import pipeline
 from sentence_transformers import SentenceTransformer
-import numpy as np
 
 __import__('pysqlite3')
 sys.modules['sqlite3'] = sys.modules.pop('pysqlite3')
@@ -120,7 +119,8 @@ embeddings = embedding_model.encode(texts).tolist()  # Convert numpy arrays to l
 
 # Create a Chroma vector store and add documents and their embeddings
 vectorstore = Chroma(persist_directory="./data")
-vectorstore.add_texts(texts, embeddings)
+for text, embedding in zip(texts, embeddings):
+    vectorstore.add_texts([text], [embedding])
 vectorstore.persist()
 
 # Load the Hugging Face model for text generation
@@ -177,3 +177,4 @@ demo.launch(debug=True)
 
 
 
+