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from flask import Flask, jsonify, request
import pickle
import pandas as pd
import numpy as np
app = Flask(__name__)
# Load pickled data
# Open the file using 'with' statement
with open('popular1.pkl', 'rb') as f:
popular_df = pd.read_pickle(f)
with open('pt1.pkl', 'rb') as fi:
pt = pd.read_pickle(fi)
with open('banquet.pkl', 'rb') as fil:
banquets = pd.read_pickle(fil)
with open('similarity_scores1.pkl', 'rb') as file:
similarity_scores = pd.read_pickle(file)
# Define a route to get recommendations
@app.route('/recommend/<string:n>',methods=['GET'])
def recommend(n):
# Get user input from request
# Perform recommendation logic
if n not in pt.index:
return "Banquet not found in the index"
index = np.where(pt.index == n)[0][0]
similar_items = sorted(enumerate(similarity_scores[index]), key=lambda x: x[1], reverse=True)[1:5]
data = []
for i in similar_items:
item = []
temp_df = banquets[banquets['Hall-Name'] == pt.index[i[0]]]
item.extend(temp_df.drop_duplicates('Hall-Name')['Hall-Name'].values)
item.extend(temp_df.drop_duplicates('Hall-Name')['Address'].values)
item.extend(temp_df.drop_duplicates('Hall-Name')['Contact'].values)
item.extend(temp_df.drop_duplicates('Hall-Name')['Rating'].values)
item.extend(temp_df.drop_duplicates('Hall-Name')['Jn12ke src'].values)
data.append(item)
return jsonify({'recommendations': data})
if __name__ == "__main__":
app.run(debug=True)
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