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app.py

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+import nltk
+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()

requirements.txt

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+nltk
+streamlit