main.py

import pickle
import pandas as pd

import streamlit as st
import requests

def Fetch_poster(movie_id):
    response = requests.get('https://api.themoviedb.org/3/movie/{}?api_key=610de5896dd9dbcc16f63d0f3252dd56&language=en-US'.format(movie_id))
    data = response.json()
    # print(data)
    # st.text(data)
    return "https://image.tmdb.org/t/p/w500/" + 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]

    recommend_movies = []
    recommend_movies_poster= []

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

        recommend_movies.append(movies.iloc[i[0]].title)
        # for poster fetch
        recommend_movies_poster.append(Fetch_poster(movie_id))
    return recommend_movies, recommend_movies_poster




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

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

st.title('Movie Recommender System')
selected_movie_name = st.selectbox(
    'hello sir , How can i help you ?',
    movies['title'].values)
if st.button('Recommended Movies'):
    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])