Upload 4 files

app.py

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+import requests
+import streamlit as st
+import pickle
+import pandas as pd
+import numpy as np
+st.title('Movie Recommendation System')
+
+movies_dict = pickle.load(open('movies_data.pkl', 'rb'))
+movies=pd.DataFrame(movies_dict)
+similarity=pickle.load(open('similarity.pkl', 'rb'))
+
+
+
+
+def recommend(movie):
+
+    index=movies[movies['Title']==movie].index[0]
+    distances=similarity[index]
+    movie_list=sorted(list(enumerate(distances)),reverse=True,key=lambda x:x[1])
+    recommended_movie_names = []
+
+
+    for i in movie_list[1:6]:
+        recommended_movie_names.append(movies.iloc[i[0]].Title)
+    return recommended_movie_names
+
+
+
+movie_list = movies['Title'].values
+selected_movie = st.selectbox(
+    "Type or select a movie from the dropdown",
+    movie_list
+)
+
+if st.button('Show Recommendation'):
+    recommended_movie_names= recommend(selected_movie)
+    col1, col2, col3, col4, col5 = st.columns(5)
+    with col1:
+        st.text(recommended_movie_names[0])
+
+    with col2:
+        st.text(recommended_movie_names[1])
+
+
+    with col3:
+        st.text(recommended_movie_names[2])
+
+    with col4:
+        st.text(recommended_movie_names[3])
+
+    with col5:
+        st.text(recommended_movie_names[4])
+

movies_data.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2748228f796545180333b1c5666eb2a186b7eb929e95e3b46ef49e7e16aacfa6
+size 10588035

similarity.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0575ab4e172980c3449f0f21ff4393e9ffca775958ae28ea318d01acef326d04
+size 871280931