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import nltk
nltk.download('punkt_tab')
nltk.download('wordnet')
nltk.download('punkt')
nltk.download('stopwords')
import numpy as np
import pandas as pd
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.stem import WordNetLemmatizer
from ast import literal_eval
from nltk.stem import SnowballStemmer
import warnings
import streamlit as st
warnings.filterwarnings('ignore')
# Load your dataset
df = pd.read_csv('edited_hotel_list.csv')
# Function for hotel recommendation
def recommend_hotel(location, description):
description = description.lower()
location = location.lower()
word_tokenize(description)
stop_words = stopwords.words('english')
lemma = WordNetLemmatizer()
# Clean up description text
filtered_description = {word for word in description.split() if word not in stop_words}
filtered_description_set = {lemma.lemmatize(word) for word in filtered_description}
# Filter the data by location
country = df[df['country'] == location]
country = country.set_index(np.arange(country.shape[0]))
# Calculate similarity scores
cos = []
for i in range(country.shape[0]):
temp_tokens = set(word_tokenize(country['Tags'][i]))
vector = temp_tokens.intersection(filtered_description_set)
cos.append(len(vector))
country['similarity'] = cos
country.sort_values(by=['similarity', 'Average_Score'], ascending=False, inplace=True)
country.drop_duplicates(subset='Hotel_Name', keep='first', inplace=True)
country.reset_index(inplace=True)
return country[['Hotel_Name', 'Average_Score', 'Hotel_Address']].head(20)
# Streamlit UI: Make the interface fancier and more visually appealing
def main():
# Title and description with icons
st.title('Hotel Recommendation System 🏨✨')
st.markdown("""
<style>
.title {
font-size: 36px;
color: #1E90FF;
font-weight: bold;
}
.description {
font-size: 18px;
color: #333;
margin-bottom: 30px;
}
.sidebar .sidebar-content {
background-color: #f7f7f7;
}
.footer {
text-align: center;
font-size: 14px;
color: #aaa;
}
.recommend-button {
background-color: #1E90FF;
color: white;
padding: 10px;
border-radius: 5px;
font-weight: bold;
}
</style>
""", unsafe_allow_html=True)
st.markdown('<p class="title">Find Your Perfect Hotel</p>', unsafe_allow_html=True)
st.markdown('<p class="description">Enter your desired hotel qualifications, and let us recommend the best hotels for you!</p>', unsafe_allow_html=True)
# Sidebar for selecting country and entering description
st.sidebar.header('Your Preferences 🏡')
location = st.sidebar.selectbox('Select Country 🌍', df['country'].unique())
description = st.sidebar.text_input('Describe your desired hotel features 🏨')
# Button to trigger recommendation
if st.sidebar.button('Recommend Hotels 🔍', key="recommend_button"):
if description:
hotels = recommend_hotel(location, description)
st.markdown(f"### Top 20 Recommended Hotels in {location.capitalize()} 🌟")
# Fancy dataframe with color-coding and custom styling
st.dataframe(
hotels.style.applymap(lambda v: 'background-color: lightblue', subset=['Hotel_Name'])
.set_properties(**{'text-align': 'center'})
.set_table_styles([
{'selector': 'thead th', 'props': [('background-color', '#1E90FF'), ('color', 'white'), ('font-size', '14px')]},
{'selector': 'tbody td', 'props': [('font-size', '14px')]},
])
)
else:
st.warning('Please enter a description of your desired hotel features!')
# Footer section with custom styling
st.markdown("""
<div class="footer">
Made with ❤️ by Senasu
</div>
""", unsafe_allow_html=True)
if __name__ == '__main__':
main()
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