app.py

import streamlit as st
import pickle
import pandas as pd
import requests

def fetch_poster(movie_id):
    response = requests.get('https://api.themoviedb.org/3/movie/{}?api_key=44f0fa1913413a1261cfb9fda276f78a&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]
    distance = similarity[movie_index]
    movie_list = sorted(list(enumerate(distance)),reverse=True, key=lambda x:x[1])[1:6]

    recommended_movie = []
    recommended_movie_poster = []

    for i in movie_list:
        movie_id = movies.iloc[i[0]].movie_id
        recommended_movie.append(movies.iloc[i[0]].title)
        # fetch poster from tmdb website
        recommended_movie_poster.append(fetch_poster(movie_id))
    return recommended_movie, recommended_movie_poster

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

# Streamlit UI
st.title('Movie Recommendation System')

selected_movie_name = st.selectbox(
    'Select a movie',
    movies['title'].values
)

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

    cols = [col1, col2, col3, col4, col5]
    for i in range(len(names)):
            with cols[i]:
                st.text(names[i])
                st.image(posters[i])
    # 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])