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app.py

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+import streamlit as st
+import pickle
+import pandas as pd
+
+def recomend(movie):
+    movie_index=movies[movies['title']==movie].index[0]
+    distences=similarity[movie_index]
+    movies_list = sorted(list(enumerate(distences)),reverse=True,key=lambda x:x[1])[1:6]
+    recommended_movies=[]
+    for i in movies_list:
+        recommended_movies.append(movies.iloc[i[0]].title)
+    return recommended_movies
+
+movie_dict=pickle.load(open('movie_dict.pkl','rb'))
+movies=pd.DataFrame(movie_dict)
+similarity=pickle.load(open('similarity1.pkl','rb'))
+
+st.title('Movie Recommender System')
+
+selected_movie_name=st.selectbox(
+    'How Would you like to br contacted?',movies['title'].values
+)
+
+if st.button('Recommend'):
+    recommendations=recomend(selected_movie_name)
+    for i in recommendations:
+        st.write(i)

movie_dict.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c8e3a22628c76af439174f9aa4dae385c5c273990ac57549d4673043f949e436
+size 2216683

movie_list.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c8e3a22628c76af439174f9aa4dae385c5c273990ac57549d4673043f949e436
+size 2216683

movies.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b7eef6c8af9fc759648b9bb71c8c6238f33f7ce0f2c138651581a4af99daaa3e
+size 2235268

requirements.txt

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+streamlit
+pandas
+scikit-learn
+numpy

similarity1.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:070fc0a0727061cb0c851a40466d54291920455112e9ad5414510ca18b994d3c
+size 184781251