app.py

#check streamlit documentation
import streamlit as st
st.title("Movie Recommender System")

import pickle
import pandas as pd
movies_dict = pickle.load(open('movies_dict.pkl','rb'))
movies = pd.DataFrame(movies_dict)

similarity = pickle.load(open('similarity.pkl','rb'))


selected_movies_name = st.selectbox('Enter your Movie Name',
                                    (movies['title'].values))


import requests
def fetch_poster(movie_id):
    response = requests.get('https://api.themoviedb.org/3/movie/{}?api_key=7b38c796ef22316b087ff1006d1d567e'.format(movie_id))
    data = response.json()
    return "https://image.tmdb.org/t/p/w500/"+data['poster_path']




def recommend(movie):
    # we will fetch the movie index
    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_poster = []
    # i[0] means the index value for the list of tuples
    for i in movies_list:
        # print(i[0]) we will get movie index
        movies_id = movies.iloc[i[0]].id
        recommended_movies.append(movies.iloc[i[0]].title)
        recommended_movies_poster.append(fetch_poster(movies_id))
    return recommended_movies, recommended_movies_poster




if st.button("Recommend Movies"):
    names,posters = recommend(selected_movies_name)
    col1, col2, col3, col4, col5 = st.columns(5)

    with col1:
        st.header(names[0])
        st.image(posters[0])

    with col2:
        st.header(names[1])
        st.image(posters[1])

    with col3:
        st.header(names[2])
        st.image(posters[2])

    with col4:
        st.header(names[3])
        st.image(posters[3])

    with col5:
        st.header(names[4])
        st.image(posters[4])