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=fe34a557846e9a676a98fd362f059b28&language=en-US".format(
        movie_id)
    response = requests.get(url)
    data = response.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]

    recommended_movies = []
    recommended_movies_posters = []
    for i in movies_list:
        movie_id = movies.iloc[i[0]].movie_id

        recommended_movies.append(movies.iloc[i[0]].title)
        # using movie_id fetch poster from API
        recommended_movies_posters.append(fetch_poster(movie_id))
    return recommended_movies,recommended_movies_posters


st.title('Movie Recommender System')

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
selected_movie_name = st.selectbox(
    "Type or select a movie from the dropdown",
    movies['title'].values
)

if st.button('Show Recommendation'):
    names,posters = recommend(selected_movie_name)
    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])