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import streamlit as st
import pickle
import pandas as pd
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.feature_extraction.text import CountVectorizer
from imdb import IMDb
similarity = pickle.load(open('cosine_sim.pkl', 'rb'))
movie_dict = pickle.load(open('movie_dict.pkl', 'rb'))
movies = pd.DataFrame(movie_dict)
programme_list=movies['title'].to_list()
imdb = IMDb()
def get_movie_id(movie_title):
"""Get the IMDb ID of the movie using the IMDbPY library."""
try:
movies = imdb.search_movie(movie_title)
movie_id = movies[0].getID() # get the ID of the first search result
return movie_id
except Exception as e:
st.error("Error: Failed to retrieve IMDb ID for the selected movie. Please try again with a different movie.")
st.stop()
def get_poster_url(imdb_id):
"""Get the URL of the poster image of the movie using the IMDbPY library."""
try:
movie = imdb.get_movie(imdb_id)
poster_url = movie['full-size cover url']
return poster_url
except Exception as e:
st.error("Error: Failed to retrieve poster URL for the selected movie. Please try again with a different movie.")
st.stop()
def recommend(movie):
index = programme_list.index(movie)
sim_score = list(enumerate(similarity[index])) #creates a list of tuples containing the similarity score and index between the input title and all other programmes in the dataset.
#position 0 is the movie itself, thus exclude
sim_score = sorted(sim_score, key= lambda x: x[1], reverse=True)[1:6] #sorts the list of tuples by similarity score in descending order.
recommend_index = [i[0] for i in sim_score]
rec_movie = movies['title'].iloc[recommend_index]
rec_movie_ids = [get_movie_id(title) for title in rec_movie]
return rec_movie, rec_movie_ids
st.set_page_config(page_title='Movie Recommender System', page_icon=':clapper:', layout='wide')
st.title('Movie Recommender System')
selected_movie_name = st.selectbox('Please select a Movie',
sorted(movies['title'].values))
if st.button('Recommend Me'):
try:
recommendations, rec_movie_ids = recommend(selected_movie_name)
# st.write(recommendations, rec_movie_ids)
# st.write(recommendations[6195])
final_movie_names = []
for i, rec_id in zip(recommendations, rec_movie_ids):
final_movie_names.append(i)
# st.write(i)
# poster_url = get_poster_url(rec_id)
# st.image(poster_url)
col1, col2, col3, col4, col5 = st.columns(5)
cols = [col1, col2, col3, col4, col5]
with col1:
st.text(final_movie_names[0])
poster_url = get_poster_url(rec_movie_ids[0])
st.image(poster_url)
with col2:
st.text(final_movie_names[1])
poster_url = get_poster_url(rec_movie_ids[1])
st.image(poster_url)
with col3:
st.text(final_movie_names[2])
poster_url = get_poster_url(rec_movie_ids[2])
st.image(poster_url)
with col4:
st.text(final_movie_names[3])
poster_url = get_poster_url(rec_movie_ids[3])
st.image(poster_url)
with col5:
st.text(final_movie_names[4])
poster_url = get_poster_url(rec_movie_ids[4])
st.image(poster_url)
except Exception as e:
st.write('An error occurred while generating recommendations:', e)
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