Update app.py

app.py

@@ -2,9 +2,16 @@ import pandas as pd
 from sklearn.feature_extraction.text import TfidfVectorizer
 from sklearn.metrics.pairwise import linear_kernel
 import gradio as gr
-
-# Load the MovieLens dataset
-movies = pd.read_csv('https://files.grouplens.org/datasets/movielens/ml-latest-small.zip', compression='zip')
+import zipfile
+import requests
+import io
+
+# Download and extract the MovieLens dataset
+url = 'https://files.grouplens.org/datasets/movielens/ml-latest-small.zip'
+response = requests.get(url)
+with zipfile.ZipFile(io.BytesIO(response.content)) as z:
+    with z.open('ml-latest-small/movies.csv') as f:
+        movies = pd.read_csv(f)
 
 # Define a TF-IDF Vectorizer Object. Remove all english stop words such as 'the', 'a'
 tfidf = TfidfVectorizer(stop_words='english')