Updated weaviate.Client ()

# Initialize Weaviate client for the embedded instance
client = weaviate.Client(
embedded_options=EmbeddedOptions()
)

app.py

@@ -4,14 +4,16 @@ import pandas as pd
 from io import StringIO
 from transformers import AutoTokenizer, AutoModelForTableQuestionAnswering
 import numpy as np
-import weaviate
+import weaviate import EmbeddedOptions
 
 # Initialize TAPAS model and tokenizer
 tokenizer = AutoTokenizer.from_pretrained("google/tapas-large-finetuned-wtq")
 model = AutoModelForTableQuestionAnswering.from_pretrained("google/tapas-large-finetuned-wtq")
 
 # Initialize Weaviate client for the embedded instance
-client = weaviate.Client("http://localhost:8080")
+client = weaviate.Client(
+  embedded_options=EmbeddedOptions()
+)
 
 # Function to ingest data into Weaviate
 def ingest_data_to_weaviate(dataframe):