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Recommend_Movies..py
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Recommend_Movies..py
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import streamlit as st
import pandas as pd
import numpy as np
import requests
import pickle
import time
from send_email import send_email
from datetime import datetime
st.set_page_config(layout="wide", page_title='The Movie Recommender')
if 'movie_index' not in st.session_state:
st.session_state['movie_index'] = 101
st.sidebar.title("The Movie Recommender.")
with st.sidebar.expander("About"):
st.write(f"The Movie Recommender uses cosine similarity to suggest "
f"movies based on user input. The system "
f"is built using TMDB's 5000 movie dataset. Additional "
f"information is "
f"retrieved from TMDB's API."
f" This project was initiated for a course at my university"
f" and is still a work in progress. If you would like to give"
f" feedback or contribute, the source code and documentation "
f"for the project can be found "
f"[here](https://github.com/YohanV1/TheMovieRecommender)."
f" If you have any suggestions or questions, "
f"please don't hesitate to reach out.")
def fetch_poster(movie_id):
url = f"https://api.themoviedb.org/3/movie/{movie_id}?" \
"api_key=433ff6eeee0036e8693c029787f184c5&language=en-US"
data = requests.get(url)
data = data.json()
poster_path = data['poster_path']
full_path = "https://image.tmdb.org/t/p/w500/" + poster_path
return full_path
def fetch_actor_image(actor_id):
url = f"https://api.themoviedb.org/3/person/{actor_id}?" \
f"api_key=433ff6eeee0036e8693c029787f184c5&language=en-US"
data = requests.get(url)
data = data.json()
actor_path = data['profile_path']
full_path = "https://image.tmdb.org/t/p/original/" + str(actor_path)
return full_path
def fetch_reviews(movie_id):
url = f"https://api.themoviedb.org/3/movie/{movie_id}/reviews?" \
f"api_key=433ff6eeee0036e8693c029787f184c5&language=en-US&page=1"
data = requests.get(url)
data = data.json()
text = data['results']
review_details = []
for reviews in text:
d = {'author': reviews['author'],
'author_path': reviews['author_details']['avatar_path'],
'rating': reviews['author_details']['rating'],
'content': reviews['content'],
'review_date': reviews['created_at']}
review_details.append(d)
return review_details
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])[1:10]
recommended_movies = []
recommended_movie_posters = []
recommended_movies_summaries = []
recommended_movies_ids = []
for i in movies_list:
movie_id = movies.iloc[i[0]].id
recommended_movies_ids.append(movie_id)
recommended_movies.append(movies.iloc[i[0]].title)
recommended_movies_summaries.append(' '
.join(movies.iloc[i[0]].overview))
recommended_movie_posters.append(fetch_poster(movie_id))
return recommended_movies_ids, recommended_movies, \
recommended_movie_posters, \
recommended_movies_summaries
@st.cache_resource
def load_data():
movies = pd.read_pickle('models/movies.pkl')
movies_data = pd.read_pickle('models/movies_data.pkl')
similarity = pd.read_pickle('models/similarity.pkl')
return movies, movies_data, similarity
movies, movies_data, similarity = load_data()
titles = list(movies['title'])
st.title('The Movie Recommender.')
option = st.selectbox('Select a movie.'
'', titles, index=st.session_state['movie_index'])
if titles.index(option) != st.session_state['movie_index']:
st.session_state['movie_index'] = titles.index(option)
st.experimental_rerun()
movie_id = movies.iloc[st.session_state['movie_index']].id
movie_details = movies_data[movies_data['id'] == movie_id]
release_date = \
movie_details['release_date'].values[
0].replace('-', '/')
production_area = movie_details[
'production_countries'].values[
0]
genres = movie_details['genres'].values[
0]
runtime = int(
movie_details['runtime'].values[0])
crew = movie_details['crew'].values[0]
crew = crew[0:4]
l_crew = len(crew)
col1, col2, ecol1 = st.columns([0.9, 1.3, 0.3])
with col1:
st.image(fetch_poster(movie_id), width=325)
with col2:
st.header(option)
text = f"{release_date} ({production_area[17:19]}) • " \
f"{', '.join(genres)} • {runtime // 60}h {runtime % 60}m"
st.write(text)
st.success(
f"Rating: {(movie_details['vote_average'].values[0] + 0.6).round(1)}"
f" ({movie_details['vote_count'].values[0]} votes)")
if not pd.isna(
movie_details['tagline'].values[
0]):
st.markdown(
f"_{movie_details['tagline'].values[0]}_")
st.subheader("Overview")
text = movies[movies['id'] == movie_id]['overview'].values[0]
if len(text) > 0:
text = ' '.join(text)
st.write(text)
else:
st.write("Overview unavailable.")
col1a, col1b, col1c, col1d = st.columns(4)
if l_crew >= 1:
with col1a:
st.markdown(
f"**{list(crew[0].values())[0]}** "
f"\n{list(crew[0].keys())[0]}")
if l_crew >= 2:
with col1b:
st.markdown(
f"**{list(crew[1].values())[0]}** "
f"\n{list(crew[1].keys())[0]}")
if l_crew >= 3:
with col1c:
st.markdown(
f"**{list(crew[2].values())[0]}** "
f"\n{list(crew[2].keys())[0]}")
if l_crew >= 4:
with col1d:
st.markdown(
f"**{list(crew[3].values())[0]}** "
f"\n{list(crew[3].keys())[0]}")
with st.spinner(text='Loading actors...'):
actors = movie_details['cast'].values[0]
actors = actors[0:8]
l_actors = len(actors)
actor_details = []
for i in range(l_actors):
d = {'character': actors[i]['character'],
'image': fetch_actor_image(actors[i]['id']),
'name': actors[i]['name']}
actor_details.append(d)
time.sleep(1)
st.markdown('##')
st.subheader("Top billed cast.")
st.markdown('######')
if l_actors > 0:
num_columns = 4
num_actors_per_column = (l_actors + num_columns - 1) // num_columns
columns = st.columns(num_columns)
for col_idx in range(num_columns):
with columns[col_idx]:
for i in range(col_idx * num_actors_per_column,
min((col_idx + 1) * num_actors_per_column,
l_actors)):
if 'None' not in actor_details[i]['image']:
st.image(actor_details[i]['image'], width=170)
st.write(actor_details[i]['name'])
st.markdown(f"_{actor_details[i]['character']}_")
else:
st.write("Actor details unavailable.")
with st.spinner(text='Loading recommendations...'):
ids, names, posters, summaries = recommend(option)
time.sleep(0.01)
st.markdown('##')
st.subheader('Movies you might like.')
st.markdown('######')
col1, ecol1, col2, ecol2, col3 = st.columns([1.5, 0.5, 1.5, 0.5, 1.5])
with col1:
if st.button(names[0], key=1):
st.session_state['movie_index'] = titles.index(names[0])
st.experimental_rerun()
st.image(posters[0], width=250)
st.write(
f"Rating: {(movies_data[movies_data['id'] == ids[0]]['vote_average'].values[0] + 0.6).round(1)}")
st.caption(f'{summaries[0][:200]}...')
with col2:
if st.button(names[1], key=2):
st.session_state['movie_index'] = titles.index(names[1])
st.experimental_rerun()
st.image(posters[1], width=250)
st.write(
f"Rating: {(movies_data[movies_data['id'] == ids[1]]['vote_average'].values[0] + 0.6).round(1)}")
st.caption(f'{summaries[1][:200]}...')
with col3:
if st.button(names[2], key=3):
st.session_state['movie_index'] = titles.index(names[2])
st.experimental_rerun()
st.image(posters[2], width=250)
st.write(
f"Rating: {(movies_data[movies_data['id'] == ids[2]]['vote_average'].values[0] + 0.6).round(1)}")
st.caption(f'{summaries[2][:200]}...')
col4, ecol3, col5, ecol4, col6 = st.columns([1.5, 0.5, 1.5, 0.5, 1.5])
with col4:
if st.button(names[3], key=4):
st.session_state['movie_index'] = titles.index(names[3])
st.experimental_rerun()
st.image(posters[3], width=250)
st.write(
f"Rating: {(movies_data[movies_data['id'] == ids[3]]['vote_average'].values[0] + 0.6).round(1)}")
st.caption(f'{summaries[3][:200]}...')
with col5:
if st.button(names[4], key=5):
st.session_state['movie_index'] = titles.index(names[4])
st.experimental_rerun()
st.image(posters[4], width=250)
st.write(
f"Rating: {(movies_data[movies_data['id'] == ids[4]]['vote_average'].values[0] + 0.6).round(1)}")
st.caption(f'{summaries[4][:200]}...')
with col6:
if st.button(names[5], key=6):
st.session_state['movie_index'] = titles.index(names[5])
st.experimental_rerun()
st.image(posters[5], width=250)
st.write(
f"Rating: {(movies_data[movies_data['id'] == ids[5]]['vote_average'].values[0] + 0.6).round(1)}")
st.caption(f'{summaries[5][:200]}...')
col7, ecol5, col8, ecol6, col9 = st.columns([1.5, 0.5, 1.5, 0.5, 1.5])
with col7:
if st.button(names[6], key=7):
st.session_state['movie_index'] = titles.index(names[6])
st.experimental_rerun()
st.image(posters[6], width=250)
st.write(
f"Rating: {(movies_data[movies_data['id'] == ids[6]]['vote_average'].values[0] + 0.6).round(1)}")
st.caption(f'{summaries[6][:200]}...')
with col8:
if st.button(names[7], key=8):
st.session_state['movie_index'] = titles.index(names[7])
st.experimental_rerun()
st.image(posters[7], width=250)
st.write(
f"Rating: {(movies_data[movies_data['id'] == ids[7]]['vote_average'].values[0] + 0.6).round(1)}")
st.caption(f'{summaries[7][:200]}...')
with col9:
if st.button(names[8], key=9):
st.session_state['movie_index'] = titles.index(names[8])
st.experimental_rerun()
st.image(posters[8], width=250)
st.write(
f"Rating: {(movies_data[movies_data['id'] == ids[8]]['vote_average'].values[0] + 0.6).round(1)}")
st.caption(f'{summaries[8][:200]}...')
with st.spinner("Loading reviews..."):
reviews = fetch_reviews(movie_id)
time.sleep(0.01)
st.markdown('##')
st.subheader("Reviews.")
st.markdown('######')
if len(reviews) != 0:
reviews = reviews[:10]
date_str = reviews[0]['review_date'][0:10]
date_obj = datetime.strptime(date_str, '%Y-%m-%d')
new_date_str = date_obj.strftime('%B %d, %Y')
with st.container():
col, cole = st.columns([10, 1])
with col:
st.write(f"#### **A review by {reviews[0]['author']}** "
f"{'- ' + str(reviews[0]['rating']) + '/10' if reviews[0]['rating'] is not None else ''} \n"
f"###### Written by {reviews[0]['author']} on {new_date_str}",
unsafe_allow_html=True)
st.caption(f"{reviews[0]['content']}")
button_placeholder = st.empty()
if button_placeholder.button("Load more reviews."):
button_placeholder.empty()
for review in reviews[1:]:
date_str = review['review_date'][0:10]
date_obj = datetime.strptime(date_str, '%Y-%m-%d')
new_date_str = date_obj.strftime('%B %d, %Y')
with st.container():
col1, col1e = st.columns([10, 1])
with col1:
st.write(
f"#### **A review by {review['author']}** "
f"{'- ' + str(review['rating']) + '/10' if review['rating'] is not None else ''} \n"
f"###### Written by {review['author']} on {new_date_str}",
unsafe_allow_html=True)
st.caption(f"{review['content']}")
button_placeholder2 = st.empty()
if button_placeholder2.button("Show less."):
button_placeholder2.empty()
st.experimental_rerun()
else:
st.write("Reviews for this movie are currently unavailable.")
st.markdown('##')
st.subheader("Watched this movie? Leave a rating and a review.")
st.markdown('######')
with st.form(key="review_form", clear_on_submit=True):
user_name = st.text_input("Your name")
user_email = st.text_input("Your email address")
rating = st.slider('Rating', 0.0, 10.0, 5.0, 0.5)
message = st.text_area("Your review")
subject = f"New Review from {user_name}"
text = f"From: {user_email}\nUsername - {user_name}\nMovie - " \
f"{option}\nRating - {rating}\nReview - {message}"
button = st.form_submit_button("Submit")
if button:
send_email(text, subject)
st.info("Your review was submitted successfully.")
st.markdown("#")
icol1, iecol, icol2, iecol1, icol3 = st.columns([1.5, 0.5, 1.5, 0.5, 1.5])
with icol1:
st.info(icon="💡", body="LinkedIn: [yohanvinu](https://www.linkedin.com/"
"in/yohanvinu/)")
with icol2:
st.info(icon="💻" , body="GitHub: [YohanV1](https://github.com/YohanV1)")
with icol3:
st.info(icon="🧠", body="Data: [TMDB](https://www.kaggle.com/datasets/tmdb/"
"tmdb-movie-metadata)")