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import streamlit as st
import pickle
import pandas as pd
import requests
def fetch_poster(movie_id):
response = requests.get('https://api.themoviedb.org/3/movie/{}?api_key=8265bd1679663a7ea12ac168da84d2e8&language=en-US'.format(movie_id))
data = response.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]
recomended_movies = []
recommended_movies_posters = []
for i in movies_list:
movie_id= movies.iloc[i[0]].movie_id
recomended_movies.append(movies.iloc[i[0]].title)
recommended_movies_posters.append(fetch_poster(movie_id))
return recomended_movies, recommended_movies_posters
movies_dict = pickle.load(open('movie_dict.pkl', 'rb'))
movies = pd.DataFrame(movies_dict)
similarity = pickle.load(open('similarity.pkl', 'rb'))
st.title('Movie Recommender System')
selected_movie_name = st.selectbox(
'How',
movies['title'].values
)
if st.button('Recommend'):
recommended_movie_names, recommended_movie_posters = recommend(selected_movie_name)
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])
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