Update app.py

app.py

@@ -11,6 +11,7 @@ embedding_model = SentenceTransformer("thenlper/gte-large")
 
 # Example dataset with genres (replace with your actual data)
 dataset = load_dataset("hugginglearners/netflix-shows")
+dataset = dataset[0].filter(lambda x: x['description'] is not None and x['listed_in'] is not None and x['title'] is not None)
 data = dataset['train']  # Accessing the 'train' split of the dataset
 
 # Convert the dataset to a list of dictionaries for easier indexing
@@ -29,7 +30,7 @@ def vector_search(query):
     query_embedding = get_embedding(query)
     
     # Generate embeddings for the combined description and genre
-    embeddings = np.array([get_embedding(combine_description_title_and_genre(item["description"], item["listed_in"],item["title"])) for item in data_list])
+    embeddings = np.array([get_embedding(combine_description_title_and_genre(item["description"], item["listed_in"],item["title"])) for item in data_list[0]])
 
     # Calculate cosine similarity between the query and all embeddings
     similarities = cosine_similarity([query_embedding], embeddings)