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import streamlit as st | |
import pickle | |
import pandas as pd | |
import requests | |
def fetch_poster(movie_id): | |
data = requests.get('https://api.themoviedb.org/3/movie/{}?api_key=3da50835eefad1461468d6eedd91edf7&language=en-US'.format(movie_id)) | |
data = data.json() | |
return "https://image.tmdb.org/t/p/w500/" + data['poster_path'] | |
def recommend(movie): | |
movie_index = movies[movies['title'] == movie].index[0] | |
distances = similarity[movie_index] | |
movies_list = sorted(list(enumerate(distances)), reverse=True, key=lambda x: x[1])[1:6] | |
recommended_movies = [] | |
recommended_movies_posters = [] | |
for i in movies_list: | |
movie_id = movies.iloc[i[0]].movie_id | |
recommended_movies.append(movies.iloc[i[0]].title) | |
recommended_movies_posters.append(fetch_poster(movie_id)) | |
return recommended_movies,recommended_movies_posters | |
movies_dict = pickle.load(open('movies.pkl','rb')) | |
movies = pd.DataFrame(movies_dict) | |
similarity = pickle.load(open('similarity.pkl','rb')) | |
st.title('Movie Recommender System') | |
selected_movie_name = st.selectbox('Name of Movies', movies['title'].values) | |
if st.button('Recommend'): | |
names,posters = recommend(selected_movie_name) | |
col1, col2, col3, col4 ,col5 = st.columns(5) | |
with col1: | |
st.text(names[0]) | |
st.image(posters[0]) | |
with col2: | |
st.text(names[1]) | |
st.image(posters[1]) | |
with col3: | |
st.text(names[2]) | |
st.image(posters[2]) | |
with col4: | |
st.text(names[3]) | |
st.image(posters[3]) | |
with col5: | |
st.text(names[4]) | |
st.image(posters[4]) |