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87af1bc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 | import streamlit as st
import pickle
def load_model(filename):
with open(filename, 'rb') as f:
return pickle.load(f)
# Load the model
loaded_model = load_model('recommendation_model.pkl')
def get_recommendations(input_categories):
tfidf_vectorizer = loaded_model['tfidf_vectorizer']
knn_classifier = loaded_model['knn_classifier']
df = loaded_model['df']
all_predictions = []
for category in input_categories:
category_tfidf = tfidf_vectorizer.transform([category])
top_20_indices = knn_classifier.kneighbors(category_tfidf, n_neighbors=20, return_distance=False)[0]
top_20_places = df.iloc[top_20_indices]
best_2_places = top_20_places.sort_values('normalized_score', ascending=False).head(2)
all_predictions.append({
'category': category,
'predictions': best_2_places[['name', 'rating', 'user_ratings_total', 'normalized_score']].to_dict('records')
})
return all_predictions
# Streamlit app
st.title("Place Recommendation System")
st.write("Enter the categories you are interested in (comma-separated):")
input_categories = st.text_input("Categories", "historical monuments, history tours, wildlife")
if st.button("Get Recommendations"):
categories_list = [category.strip() for category in input_categories.split(',')]
recommendations = get_recommendations(categories_list)
for prediction in recommendations:
st.subheader(f"Top 2 recommended places for '{prediction['category']}':")
for place in prediction['predictions']:
st.write(f"**Name:** {place['name']}")
st.write(f"**Rating:** {place['rating']:.2f}")
st.write(f"**Review Count:** {place['user_ratings_total']}")
st.write(f"**Score:** {place['normalized_score']:.4f}")
st.write("") # Add a blank line for better spacing
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