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app.py

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+import streamlit as st
+import pickle
+import requests
+import pandas as pd
+
+footer="""<style>
+a:link , a:visited{
+color: black;
+background-color: transparent;
+}
+
+a:hover,  a:active {
+color: red;
+background-color: transparent;
+}
+
+.footer {
+position: fixed;
+left: 0;
+bottom: 0;
+width: 100%;
+background-color: white;
+color: black;
+text-align: center;
+}
+</style>
+<div class="footer">
+<p>Developed with <span style ='color:red'>❤</span> by <a href="https://shrikrishnaparab.tech/" target="_blank">Shrikrishna Parab</a></p>
+</div>
+"""
+
+def fetch_poster(movie_id):
+    url = "https://api.themoviedb.org/3/movie/{}?api_key=8265bd1679663a7ea12ac168da84d2e8&language=en-US".format(movie_id)
+    data = requests.get(url)
+    data = data.json()
+    poster_path = data['poster_path']
+    #homepage_path = data['homepage']
+    full_path = "https://image.tmdb.org/t/p/w500/" + poster_path
+    return full_path
+
+def get_popular(qualified):
+    #print(qualified.head())
+    top_5 = qualified.head(5)
+    return top_5
+
+
+def top_genre_based_movies(genre, percentile=0.95):
+    df = genre_df[genre_df['genres'].str.contains(genre)]
+    vote_counts = df['vote_count'].astype('int')
+    vote_averages = df['vote_average'].astype('int')
+    C = vote_averages.mean()
+    m = vote_counts.quantile(percentile)
+    qualified = df[(df['vote_count'] >= m)][['movie_id', 'title', 'vote_count', 'vote_average', 'genres']]
+    qualified['vote_count'] = qualified['vote_count'].astype('int')
+    qualified['vote_average'] = qualified['vote_average'].astype('int')
+    qualified['wr'] = qualified.apply(
+        lambda x: (x['vote_count'] / (x['vote_count'] + m) * x['vote_average']) + (m / (m + x['vote_count']) * C),
+        axis=1)
+    qualified = qualified.sort_values('wr', ascending=False).head(250)
+    return qualified
+
+def recommend(movie):
+    index = movies[movies['title'] == movie].index[0]
+    distances = sorted(list(enumerate(similarity[index])), reverse=True, key=lambda x: x[1])
+    recommended_movie_names = []
+    recommended_movie_posters = []
+    homepage_path_list = []
+    for i in distances[1:6]:
+        # fetch the movie poster
+        movie_id = movies.iloc[i[0]].movie_id
+        full_path= fetch_poster(movie_id)
+        #homepage_path_list.append(homepage_path)
+        recommended_movie_posters.append(full_path)
+        recommended_movie_names.append(movies.iloc[i[0]].title)
+
+    return recommended_movie_names,recommended_movie_posters
+
+
+st.title("Movie Recommender System")
+
+movies = pickle.load(open('movie_list_hi.pkl','rb'))
+similarity = pickle.load(open('similarity_hi.pkl','rb'))
+all_movies = pickle.load(open('movies_df_hi.pkl','rb'))
+top_popular = pickle.load(open('top_popular_hi.pkl','rb'))
+
+s = all_movies.apply(lambda x: pd.Series(x['genres']),axis=1).stack().reset_index(level=1, drop=True)
+s.name = 'genres'
+genre_df = all_movies.drop('genres', axis=1).join(s)
+
+movie_list = movies['title'].values
+option_selected = st.selectbox(
+    'Type or Select Movie Name from Dropdown',
+    movie_list
+)
+
+genre_list = ['Action','Romance','Adventure','Science Fiction','Comedy']
+genre_selected = st.selectbox(
+    'Type or Select Genre from Dropdown',
+    genre_list
+)
+
+if st.button('Show Recommendation'):
+    recommended_movie_names, recommended_movie_posters = recommend(option_selected)
+    top_popular_movies = get_popular(top_popular)
+    st.header("Movies Based on Content: Similar Movies")
+    col1, col2, col3, col4, col5 = st.columns(5)
+    with col1:
+        st.image(recommended_movie_posters[0], caption=recommended_movie_names[0])
+    with col2:
+        st.image(recommended_movie_posters[1], caption=recommended_movie_names[1])
+
+    with col3:
+        st.image(recommended_movie_posters[2], caption=recommended_movie_names[2])
+    with col4:
+        st.image(recommended_movie_posters[3], caption=recommended_movie_names[3])
+    with col5:
+        st.image(recommended_movie_posters[4], caption=recommended_movie_names[4])
+
+    st.header("Movies Based on Popularity: Top Popular")
+    popular = []
+    for row in top_popular_movies.loc[:,['title','movie_id']].values:
+        popular.append(row)
+    col6, col7, col8, col9, col10 = st.columns(5)
+    with col6:
+        full_path = fetch_poster(popular[0][1])
+        st.image(full_path, caption=popular[0][0])
+    with col7:
+        full_path = fetch_poster(popular[1][1])
+        st.image(full_path, caption=popular[1][0])
+    with col8:
+        full_path = fetch_poster(popular[2][1])
+        st.image(full_path, caption=popular[2][0])
+    with col9:
+        full_path = fetch_poster(popular[3][1])
+        st.image(full_path, caption=popular[3][0])
+    with col10:
+        full_path = fetch_poster(popular[4][1])
+        st.image(full_path, caption=popular[4][0])
+
+
+    st.header("Movies Based on Genre: Top "+str(genre_selected)+" Movies")
+    top_gener_based = top_genre_based_movies(genre_selected).head(5)
+    genre_popular = []
+    for row in top_gener_based.loc[:, ['title', 'movie_id']].values:
+        genre_popular.append(row)
+    col11, col12, col13, col14, col15 = st.columns(5)
+    with col11:
+        full_path = fetch_poster(genre_popular[0][1])
+        st.image(full_path, caption=genre_popular[0][0])
+    with col12:
+        full_path = fetch_poster(genre_popular[1][1])
+        st.image(full_path, caption=genre_popular[1][0])
+    with col13:
+        full_path = fetch_poster(genre_popular[2][1])
+        st.image(full_path, caption=genre_popular[2][0])
+    with col14:
+        full_path = fetch_poster(genre_popular[3][1])
+        st.image(full_path, caption=genre_popular[3][0])
+    with col15:
+        full_path = fetch_poster(genre_popular[4][1])
+        st.image(full_path, caption=genre_popular[4][0])
+
+
+
+
+
+
+
+
+
+st.markdown(footer,unsafe_allow_html=True)

movie_list_hi.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2891c693f283e45aad254aafd947ff13c5364fc8a3501eebac465fd7bf33553b
+size 504725

movies_df_hi.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:efcfb5764e355eff6344530bc0e501d5f7222c98ea4c56ad8723bc9fd0d2f767
+size 11674864

requirements.txt

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+streamlit
+sklearn

similarity_hi.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9373b0aceaef2615c22975d0d4d6a5d1e00ff4e8bf68bdce103650af309d66f8
+size 13025568

top_popular_hi.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:528bd83c7c17adad2edecb2f0f5c896bd7b6011170d96aecb7157c424aa97370
+size 7328