Movie-Recommender-System/app.py at main · rishi-sangare/Movie-Recommender-System · GitHub

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# tmdb api get the link to substitute the value[movie , get]
# json viewer


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=fbff03b3fac11ca12715771695834d4c&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_movies=[]
    recommended_movies_poster=[]
    for i in movie_list:
        # fetch poster using api

        movie_id=movies.iloc[i[0]].movie_id
        recommended_movies.append(movies.iloc[i[0]].title)
        recommended_movies_poster.append(fetch_poster(movie_id))
    return recommended_movies,recommended_movies_poster


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

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

st.title('Movie Recommender System')

selected_movie_name = st.selectbox(
'How would you like to be contacted?',
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])