import streamlit as st
import pickle
import pandas as pd
import requests #to fetch images

def fetch_poster(movie_id):
    url = "https://api.themoviedb.org/3/movie/{}?api_key=8265bd1679663a7ea12ac168da84d2e8&language=en-US".format(movie_id)
    data = requests.get(url,verify=False)
    data = data.json()
    poster_path = data['poster_path']
    full_path = "https://image.tmdb.org/t/p/w500/" + poster_path
    return full_path

def recommend(movie):
    movie_index = movies[movies['title'] == movie].index[0]  # fetching the index of movie
    distances = similarity[movie_index]
    movie_list = sorted(list(enumerate(distances)), reverse=True, key=lambda x: x[1])[1:6]
    recommended_movies=[]
    recommended_movies_posters=[]
    for i in movie_list:
        movie_id = movies.iloc[i[0]].movie_id
        recommended_movies.append(movies.iloc[i[0]].title)
        recommended_movies_posters.append(fetch_poster(movie_id))
    return recommended_movies,recommended_movies_posters

movies_dict=pickle.load(open('movie_dict.pkl','rb')) #read binary
movies=pd.DataFrame(movies_dict)

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

st.title('Movie Nutz - A recommender')

selected_movie_name = st.selectbox(
    'Type or select a movie from the dropdown menu of your choice:',
    movies['title'].values
)
if st.button('Show Recommendation'):
    recommended_movie_names,recommended_movie_posters = recommend(selected_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])