app.py

import streamlit as st
import pickle 
import requests
import pandas as pd


def recommend(movies, movie_name):
    movie_index = movies[movies['title'] == movie_name].index[0]
    distances = similarity[movie_index]
    rec_movies_list = sorted(list(enumerate(distances)), 
                       reverse=True,
                       key = lambda x: x[1])[1:6]
    rec_movie_names = []
    rec_movie_posters = []

    for i in rec_movies_list:
        # st.write(movies.iloc[i[0]].title)
        rec_movie_names.append(movies.iloc[i[0]].title)
        rec_movie_posters.append(fetch_poster(movies.iloc[i[0]].movie_id))

    return rec_movie_names, rec_movie_posters

def fetch_poster(movie_id):
    url = f"https://api.themoviedb.org/3/movie/{movie_id}?api_key=95516cdf5c8762bc6bb90eef4d45be3c"
    data = requests.get(url)
    data = data.json()
    # st.write(data)
    poster_path = data['poster_path']
    full_path = "https://image.tmdb.org/t/p/w500/" + poster_path
    return full_path

# Read the movies dict file and convert it into a dataframe
movies_dict = pickle.load(open("./model/movies_dict.pkl", "rb"))
movies = pd.DataFrame(movies_dict)

# Similarity matrix of the movies with each other
similarity = pickle.load(open("./model/similarity.pkl", "rb"))

movies_list = movies['title'].values
# print(movies_list[:5])

# StreamLit application
st.set_page_config(layout='wide',
                   page_title="Movie_RecSys")
st.header('Movie Recommender System')
st.markdown("#")
selected_movie = st.selectbox(
    "Type or select a movie from the dropdown",
    movies_list
)

st.markdown("###")

if st.button('Recommend'):
    rec_movie_names, rec_movie_posters = recommend(movies, selected_movie)
    # st.text(rec_movie_names)
    # st.text(rec_movie_posters)
    st.markdown("#")
    col1, col2, col3, col4, col5 = st.columns(5, gap = "small")
    with col1:
        st.image(rec_movie_posters[0])
        st.markdown(rec_movie_names[0])
    with col2:
        st.image(rec_movie_posters[1])
        st.markdown(rec_movie_names[1])
    with col3:
        st.image(rec_movie_posters[2])
        st.markdown(rec_movie_names[2])
    with col4:
        st.image(rec_movie_posters[3])
        st.markdown(rec_movie_names[3])
    with col5:
        st.image(rec_movie_posters[4])
        st.markdown(rec_movie_names[4])