app.py

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

def poster(movie_id):
      response=requests.get('https://api.themoviedb.org/3/movie/{}?api_key=b453b9cc79800c0b1208a896939a12e1'.format(movie_id))
      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):

        index = movies[movies['title'] == movie].index[0]
        distances=similarity[index]
        movies_list=sorted(list(enumerate(distances)), reverse=True, key=lambda x: x[1])[1:6]

        recommended_movies=[]
        recommended_movie_posters = []
        for i in movies_list:
            movie_id=movies.iloc[i[0]].movie_id
            recommended_movies.append(movies.iloc[i[0]].title)
            recommended_movie_posters.append(poster(movie_id))
        return recommended_movies,recommended_movie_posters

similarity=pickle.load(open('similarity.pkl','rb'))
st.title('Movie Recommendation System')

movies_dict=pickle.load(open('movie_dict.pkl','rb'))
movies=pd.DataFrame(movies_dict)
movie_name=st.selectbox('Select a movie',movies['title'].values)

if st.button('Recommend'):
    recommended_movie_names, recommended_movie_posters = recommend(movie_name)
    col1, col2, col3, col4, col5 = st.columns(5)
    with col1:
        st.text(recommended_movie_names[0])
        st.image(recommended_movie_posters[0])
    with col2:
        st.text(recommended_movie_names[1])
        st.image(recommended_movie_posters[1])

    with col3:
        st.text(recommended_movie_names[2])
        st.image(recommended_movie_posters[2])
    with col4:
        st.text(recommended_movie_names[3])
        st.image(recommended_movie_posters[3])
    with col5:
        st.text(recommended_movie_names[4])
        st.image(recommended_movie_posters[4])