main.py

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



def fetch_poster(movie_id):
    response = requests.get("https://api.themoviedb.org/3/movie/{}?api_key=873d82e67af936f345c85f349e80dc7c&language=en-US".format(movie_id))
    data=response.json()
    return "https://image.tmdb.org/t/p/original" + data['poster_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)
        recommended_movies_posters.append(fetch_poster(movie_id))

    return recommended_movies, recommended_movies_posters


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

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

st.title('Movie Recommender System')

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

if st.button('Recommend'):
    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])