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app.py
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import pickle
import streamlit as st
import requests
# 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 recommender_system(movie):
movies_index = movies_tag[movies_tag['original_title'] == movie].index[0]
dist = cos_sim[movies_index]
movies_list = sorted(list(enumerate(dist)), reverse = True, key = lambda x:x[1])[1:5]
for movie in movies_list:
return (movies_tag.iloc[movie[0]].original_title)
st.header('Movie Recommender System')
movies = pickle.load(open('model/movie_tags.pickle','rb'))
similarity = pickle.load(open('model/similarity.pickle','rb'))
movie_list = movies['original_title'].values
selected_movie = st.selectbox(
"Type or select a movie from the dropdown",
movie_list
)
if st.button('Show Recommendation'):
recommended_movie_names = recommend(selected_movie)
col1, col2, col3, col4, col5 = st.beta_columns(5)
with col1:
st.text(recommended_movie_names[0])
with col2:
st.text(recommended_movie_names[1])
with col3:
st.text(recommended_movie_names[2])
with col4:
st.text(recommended_movie_names[3])
with col5:
st.text(recommended_movie_names[4])