First

app.py

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+import pickle
+import streamlit as st
+import requests
+import pandas as pd
+from sklearn.metrics.pairwise import linear_kernel
+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']
+    full_path = "https://image.tmdb.org/t/p/w500/" + poster_path
+    return full_path
+
+def get_popular_recommendations(title, linear_sim, df):
+    indices = pd.Series(df.index, index=df['title']).drop_duplicates()
+    idx = indices[title]
+
+    sim_scores = list(enumerate(linear_sim[idx]))
+    sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)
+    top_movies_indices = [i[0] for i in sim_scores[1:31]]
+    top_movies = df[['title','popularity_score']].iloc[top_movies_indices]
+    top_movies = list(top_movies.sort_values('popularity_score',ascending = False).head(5)['title'])
+    top_movies_posters = [fetch_poster(mapping[title]) for title in top_movies ]
+    return top_movies, top_movies_posters
+
+st.header('Movie Recommender System')
+movies = pickle.load(open('df_popularity.pkl','rb'))
+df_popularity = pd.DataFrame(movies)
+tfidf_matrix = pickle.load(open('tfidf.pkl','rb'))
+linear_sim = linear_kernel(tfidf_matrix, tfidf_matrix)
+mapping = pickle.load(open('mapping.pkl','rb'))
+movie_list = df_popularity['title'].values
+selected_movie = st.selectbox(
+    "Type or select a movie from the dropdown",
+    movie_list
+)
+
+if st.button('Show Recommendation'):
+    recommended_movie_names,recommended_movie_posters = get_popular_recommendations(selected_movie ,linear_sim ,df_popularity)
+    col1, col2, col3, col4, col5 = st.columns(5)
+    with col1:
+        st.text(recommended_movie_names[0])
+        st.image(recommended_movie_posters[0])
+    with col2:
+        st.text(recommended_movie_names[1])
+        st.image(recommended_movie_posters[1])
+
+    with col3:
+        st.text(recommended_movie_names[2])
+        st.image(recommended_movie_posters[2])
+    with col4:
+        st.text(recommended_movie_names[3])
+        st.image(recommended_movie_posters[3])
+    with col5:
+        st.text(recommended_movie_names[4])
+        st.image(recommended_movie_posters[4])
+

df_popularity.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:069a76c21ea30c73af03256510a6b43170b3cf324cd3c41dbeef80bd77534b04
+size 10316460

mapping.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3258fd12a04f6322a3a7df5f7c4cf69b1e5245c0a95f1391bc4df39d89657f69
+size 1020592

requirements.txt

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+scikit-learn

tfidf.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6a4abd058c42eff9247e280c6e356c1af0a97ff2ea4cc812b0ecff4d34d24dfb
+size 4574514