Update app.py

app.py

@@ -1,7 +1,6 @@
 from sentence_transformers import SentenceTransformer, util, CrossEncoder
 from datasets import load_dataset
 import pandas as pd
-from IPython.display import display
 
 #Get the netflix dataset
 netflix = load_dataset('hugginglearners/netflix-shows',use_auth_token=True)
@@ -20,6 +19,15 @@ dataset_embeddings = torch.from_numpy(flix_ds["train"].to_pandas().to_numpy()).t
 #load cross-encoder for reranking
 cross_encoder = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-12-v2')
 
+def display_df_as_table(model,top_k,score='score'):
+    # Display the df with text and scores as a table
+    df = pd.DataFrame([(hit[score],passages[hit['corpus_id']]) for hit in model[0:top_k]],columns=['Score','Text'])
+    df['Score'] = round(df['Score'],2)
+    df = df.merge(netflix_df,how='inner',left_on='Text',right_on='description')
+    df.drop('Text',inplace=True,axis=1)
+    
+    return df
+
 #function for generating similarity of query and netflix shows
 def semantic_search(query,embeddings,top_k=top_k):
     '''Encode query and check similarity with embeddings'''