Update app.py

Chroma --> FAISS

app.py

@@ -72,7 +72,10 @@ if uploaded_file is not None:
                                        model_kwargs={'device': 'cpu'})
 
     # Load the textual chunks into the Chroma vector store.
-    db = Chroma.from_documents(texts, embeddings)
+    print('texts = ',texts)
+    print('embeddings = ', embeddings)
+    db = FAISS.from_documents(texts,embeddings)
+    # db = Chroma.from_documents(texts, embeddings)
 
     # Custom handler to stream outputs live to the Streamlit application.
     from langchain.callbacks.base import BaseCallbackHandler