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app_movies.py
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app_movies.py
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import streamlit as st
import pickle
import pandas as pd
import numpy as np
# Load the movies dictionary and similarity tags
movies_dict = pickle.load(open('notebook/movies_dict.pkl', 'rb'))
movies_dict = pd.DataFrame(movies_dict)
similarity_tags = pickle.load(open('notebook/similarity_tags.pkl', 'rb'))
poster = pickle.load(open('notebook/poster_dict.pkl', 'rb'))
similarity_main_tags = pickle.load(open('notebook/similarity_main_tags.pkl', 'rb'))
# CSS for background image and styling
st.markdown(
"""
<style>
.stApp {
background-image: url("https://img.freepik.com/free-photo/clapperboard-remote-control-space_23-2147681385.jpg");
background-size: cover;
}
.title {
font-size: 36px;
font-weight: bold;
color: #FFFFFF;
text-align: center;
text-shadow: 2px 2px #000000;
}
.subheader {
font-size: 24px;
font-weight: bold;
color: #FFFFFF;
text-align: center;
text-shadow: 1px 1px #000000;
}
.movie-title {
font-size: 16px;
color: #FFFFFF;
text-align: center;
text-shadow: 1px 1px #000000;
padding: 5px;
background-color: rgba(0, 0, 0, 0.5);
border-radius: 5px;
margin-bottom: 10px;
}
.shaded-box {
background-color: rgba(0, 0, 0, 0.5);
padding: 10px;
border-radius: 10px;
margin: 10px 0;
}
.movie-poster {
width: 100%;
height: auto; /* Maintain aspect ratio */
object-fit: cover; /* Ensures the image covers the whole area */
border-radius: 10px;
}
.movie-container {
display: flex;
flex-direction: column;
align-items: center;
}
</style>
""",
unsafe_allow_html=True
)
st.markdown('<div class="title shaded-box">Movie Select - Discover Your Movie Mojo</div>', unsafe_allow_html=True)
# Function to recommend movies
def recommend(movie, similarity, exclude_movies=set()):
movie_index = movies_dict[movies_dict['title'] == movie].index[0]
distances = similarity[movie_index]
movies_list = sorted(list(enumerate(distances)), reverse=True, key=lambda x: x[1])[1:101]
recommended_movies = []
recommended_movies_poster = []
for i in movies_list:
movie_title = movies_dict.iloc[i[0]].title
if movie_title not in exclude_movies:
recommended_movies.append(movie_title)
recommended_movies_poster.append(poster[movie_title])
if len(recommended_movies) == 10: # Limit to 10 recommendations
break
# Combine the movies and their posters into a list of tuples
combined_list = list(zip(recommended_movies, recommended_movies_poster))
# Sort the combined list by IMDB rating and then by revenue
combined_list = sorted(
combined_list,
key=lambda x: (
movies_dict[movies_dict['title'] == x[0]]['IMDB_Rating'].values[0],
movies_dict[movies_dict['title'] == x[0]]['revenue'].values[0]
),
reverse=True
)
# Separate the sorted movies and posters back into two lists
recommended_movies, recommended_movies_poster = zip(*combined_list)
return list(recommended_movies), list(recommended_movies_poster)
selected_movie_name = st.selectbox('Select your Favorite Movie', movies_dict['title'].values)
if st.button('Show Recommendation'):
# Get the first set of recommendations
recommended_movie_names_1, recommended_movie_posters_1 = recommend(selected_movie_name, similarity_main_tags)
st.markdown('<div class="subheader shaded-box">Recommendations</div>', unsafe_allow_html=True)
# Display first 5 recommendations
col1, col2, col3, col4, col5 = st.columns(5)
for i, col in enumerate([col1, col2, col3, col4, col5]):
with col:
movie_name = recommended_movie_names_1[i]
movie_poster = recommended_movie_posters_1[i]
google_search_url = f"https://www.google.com/search?q={movie_name}+watch+now"
col.markdown(f'<div class="movie-container"><div class="movie-title">{movie_name}</div><a href="{google_search_url}" target="_blank"><img src="{movie_poster}" class="movie-poster"></a></div>', unsafe_allow_html=True)
# Display next 5 recommendations
col6, col7, col8, col9, col10 = st.columns(5)
for i, col in enumerate([col6, col7, col8, col9, col10], start=5):
with col:
movie_name = recommended_movie_names_1[i]
movie_poster = recommended_movie_posters_1[i]
google_search_url = f"https://www.google.com/search?q={movie_name}+watch+now"
col.markdown(f'<div class="movie-container"><div class="movie-title">{movie_name}</div><a href="{google_search_url}" target="_blank"><img src="{movie_poster}" class="movie-poster"></a></div>', unsafe_allow_html=True)
# Get the second set of recommendations, excluding the first set
recommended_movie_names_2, recommended_movie_posters_2 = recommend(selected_movie_name, similarity_tags, set(recommended_movie_names_1))
st.markdown('<div class="subheader shaded-box">Some other Suggestions </div>', unsafe_allow_html=True)
# Display first 5 suggestions
col1, col2, col3, col4, col5 = st.columns(5)
for i, col in enumerate([col1, col2, col3, col4, col5]):
with col:
movie_name = recommended_movie_names_2[i]
movie_poster = recommended_movie_posters_2[i]
google_search_url = f"https://www.google.com/search?q={movie_name}+watch+now"
col.markdown(f'<div class="movie-container"><div class="movie-title">{movie_name}</div><a href="{google_search_url}" target="_blank"><img src="{movie_poster}" class="movie-poster"></a></div>', unsafe_allow_html=True)
# Display next 5 suggestions
col6, col7, col8, col9, col10 = st.columns(5)
for i, col in enumerate([col6, col7, col8, col9, col10], start=5):
with col:
movie_name = recommended_movie_names_2[i]
movie_poster = recommended_movie_posters_2[i]
google_search_url = f"https://www.google.com/search?q={movie_name}+watch+now"
col.markdown(f'<div class="movie-container"><div class="movie-title">{movie_name}</div><a href="{google_search_url}" target="_blank"><img src="{movie_poster}" class="movie-poster"></a></div>', unsafe_allow_html=True)