Update app.py

app.py

@@ -5,6 +5,7 @@ import joblib
 kmeans = joblib.load('kmeans_model.joblib')
 
 movies_pred = pd.read_csv("movies_nonnull.csv")
+movies_pred['title'] = movies_pred['title'].apply(lambda x: x.lower().replace(")","").replace("(","").strip())
 with open("movies_df.pkl", "rb") as f:
     movies_df = pickle.load(f)
 
@@ -15,7 +16,7 @@ def recommend_movies(name):
 
     try:
         
-        idx = movies_pred[movies_pred['title'] == name].index[0]
+        idx = movies_pred[movies_pred['title'] == name.lower().strip()].index[0]
     
         prediction = kmeans.predict(movies_df.iloc[idx,:-1].to_numpy().reshape(1,-1))
         ans = list(movies_pred[movies_df['KmeansCluster']==prediction[0]].index)