Update app.py

app.py

@@ -7,30 +7,6 @@ from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 from transformers import pipeline
 from langchain.llms import HuggingFacePipeline
  
-# Load sample data (a restaurant menu of items)
-# with open('./data.csv') as file:
-#     lines = csv.reader(file)
-
-#     # Store the name of the menu items in this array. In Chroma, a "document" is a string i.e. name, sentence, paragraph, etc.
-#     documents = []
-
-#     # Store the corresponding menu item IDs in this array.
-#     metadatas = []
-
-#     # Each "document" needs a unique ID. This is like the primary key of a relational database. We'll start at 1 and increment from there.
-#     ids = []
-#     id = 1
-
-#     # Loop thru each line and populate the 3 arrays.
-#     for i, line in enumerate(lines):
-#         if i == 0:
-#             # Skip the first row (the column headers)
-#             continue
-
-#         documents.append(line[0])
-#         metadatas.append({"item_id": line[1]})
-#         ids.append(str(id))
-#         id += 1
 
 # Instantiate chromadb instance. Data is stored on disk (a folder named 'my_vectordb' will be created in the same folder as this file).
 chroma_client = chromadb.PersistentClient(path="vector_db")