import pickle
import numpy as np
import pandas as pd
import streamlit as st



def recommend(movie):
  index = movies[movies["title"]== movie].index[0]
  distances = sorted(list(enumerate(similarity[index])), reverse = True, key = lambda x: x[1])
  recommended_movies_names =[]
  #recommended_movies_poster =[]
  for i in distances[1:6]:
     movie_id = movies.iloc[i[0]].movie_id
     # recommended_movies_poster.append(fetch_poster(movie_id))
     recommended_movies_names.append(movies.iloc[i[0]].title)
  return recommended_movies_names
    
st.header("Movie Recommendation System Project ")
movies = pickle.load(open(r"movielist.pkl",'rb'))
similarity = pickle.load(open(r"similarity.pkl",'rb'))

movie_list = movies['title'].values
selected_movie = st.selectbox(
    "Type or select a movie to get recommendations",
    movie_list
)
if st.button("Show Recommendation"):
    recommended_movies_names = recommend(selected_movie)
    st.text(recommended_movies_names[0])
    st.text(recommended_movies_names[1])
    st.text(recommended_movies_names[2])
    st.text(recommended_movies_names[3])
    st.text(recommended_movies_names[4])