app.py

import streamlit as st

import pickle5
import pandas as pd
import requests

def fetch_poster(movie_id):
    response= requests.get('https://api.themoviedb.org/3/movie/{}?api_key=07674ed66167ca018775f24e992c9ac7&language=en-US'.format(movie_id))
    data= response.json()
   
    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]
    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)
         #fetch poster from api
        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'))
movies = pd.DataFrame(movies_dict)
st.title('Movie Recommender System')


selected_movie_name = st.selectbox(
'Which movie to be recommended?',
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])