app.py

import pickle
import streamlit as st
import requests

def fetch_poster(movie_id):
    url = "https://api.themoviedb.org/3/movie/{}?api_key=f0f74bbb1f845a9488b4e7c9f543e099&language=en-US".format(movie_id)
    data = requests.get(url)
    data = data.json()
    poster_path = data['poster_path']
    full_path = "http://image.tmdb.org/t/p/w500/" + poster_path
    
    return full_path

    

def recommend(movie):
    index = movies[movies['title'] ==  movie].index[0]
    distances = sorted(list(enumerate(similarity[index])),reverse = True, key = lambda x:x[1])
    recommended_movies_name = []
    recommended_movies_poster=[]
    for i in distances[1:6]:
        movie_id = movies.iloc[i[0]]['movie_id']
        recommended_movies_poster.append(fetch_poster(movie_id))
        recommended_movies_name.append (movies.iloc[i[0]].title)
    return recommended_movies_name, recommended_movies_poster


st.header("Movies Recommendation System Using Machine Learning")

movies = pickle.load(open('artificats/movie_list.pkl','rb'))
similarity = pickle.load(open('artificats/similarity.pkl','rb') 
)
movies_list = movies['title'].values
selected_movie = st.selectbox(
    'Type or select a movie to get a recommendation',
    movies_list
)


if st.button('show recommendation'):
    recommended_movies_name,recommended_movies_poster = recommend(selected_movie)
    col1, col2, col3, col4, col5 = st.columns(5)
    with col1:
        st.text(recommended_movies_name[0])
        st.image(recommended_movies_poster[0])
    
    with col2:
        st.text(recommended_movies_name[1])
        st.image(recommended_movies_poster[1])

    with col3:
        st.text(recommended_movies_name[2])
        st.image(recommended_movies_poster[2])


    with col4:
        st.text(recommended_movies_name[3])
        st.image(recommended_movies_poster[3])

    with col5:
        st.text(recommended_movies_name[4])
        st.image(recommended_movies_poster[4])