app.py

import streamlit as st
import pickle
import pandas as pd

import requests

def fetch_poster(movie_id):
    url = 'https://api.themoviedb.org/3/movie/{}?api_key=719577c8cc42e26742b9c91679acfe32&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 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 = []
    recommend_movies_posters = []

    
    for i in movies_list:
       movie_id = movies.iloc[i[0]].movie_id
       recommend_movies.append(movies.iloc[i[0]].title)
       recommend_movies_posters.append(fetch_poster(movie_id))
    return recommend_movies,recommend_movies_posters

movies_dict = pickle.load(open('movie_dict.pkl','rb'))
movies = pd.DataFrame(movies_dict)

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

movie_list = movies['title'].values

st.title('Movie Recommendation System')

selected_movie_name = st.selectbox(
    'Type a name of a movie to recommend',
    movie_list
)

if st.button('Show Recommendation'):
    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])