stream.py

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=27d3aadd2350167b44cb4e1f0b4d0622&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]
    distances=similarity[movie_index]
    movies_list=sorted(list(enumerate(distances)),reverse=True,key=lambda x:x[1])[1:6]

    recommended_movies=[]
    recommended_movies_poster=[]

    for i in movies_list:
        movie_id=movies.iloc[i[0]].movie_id   
        #fetching movie poster from api
        
        recommended_movies.append(movies.iloc[i[0]].title)
        recommended_movies_poster.append(fetch_poster(movie_id))
    return recommended_movies,recommended_movies_poster




#loading movies dictionary from pickle library
movies_dict=pickle.load(open('movie_dict.pkl','rb'))




#creating new data frame from movies dictionary
movies=pd.DataFrame(movies_dict)

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

st.title("Movie recommender")


movie_name=st.selectbox('enter movie name',
movies['title'].values
)
if st.button('Recommend'):
    names,poster=recommend(movie_name)
    
    col1,col2,col3,col4,col5=st.beta_columns(5)
    with col1:
        st.text(names[0])
        st.image(poster[0])

    with col2:
        st.text(names[1])
        st.image(poster[1])

    with col3:
        st.text(names[2])
        st.image(poster[2])        

    with col4:
        st.text(names[3])
        st.image(poster[3]) 


    with col5:
        st.text(names[4])
        st.image(poster[4])