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app.py
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app.py
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import streamlit as st
import pickle
import pandas as pd
import requests #for fetching API
def fetch_poster(movie_id):
response = requests.get('https://api.themoviedb.org/3/movie/{}?api_key=01e25f94a5ee1e33a8748afc6d191438&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]
recommend_movies = []
recommended_movies_posters = []
for i in movies_list:
movie_id = movies.iloc[i[0]].movie_id
recommend_movies.append(movies.iloc[i[0]].title)
#fetch poster from API
recommended_movies_posters.append(fetch_poster(movie_id))
return recommend_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')
selected_movie = st.selectbox(
'Select the Movie:',
movies['title'].values
)
if st.button('Recdommend'):
names, posters = recommend(selected_movie)
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])