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main.py
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main.py
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from fastapi import FastAPI
from typing import Optional, List
from models import DestinationRecommendationAttribute
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.feature_extraction.text import TfidfVectorizer
import pandas as pd
from sklearn.preprocessing import StandardScaler
import tensorflow as tf
import json
from supabase import Client, create_client
from dotenv import load_dotenv
from os import environ
app = FastAPI()
model = tf.keras.models.load_model('destination_recommender_model.h5',compile=False)
model2 = tf.keras.models.load_model('restaurant_recommender_model.h5',compile=False)
tv = TfidfVectorizer(max_features=5000)
tv2 = TfidfVectorizer(max_features=5000)
content_based_data = pd.read_csv('data/content_based_data.csv')
resto_content_based_data = pd.read_csv('data/resto_content_based_data.csv')
dfs = pd.read_csv('data/dfs.csv')
scaler = StandardScaler()
scaler2 = StandardScaler()
def hotel_recommender(filtered_hotel_df, city, top_k=3):
hotel_by_city_df = filtered_hotel_df[filtered_hotel_df['city'] == city]
random_rows = hotel_by_city_df.sample(n=top_k)
return random_rows
def get_filtered_hotel_df():
hotel_df = dfs[dfs.keyword_category.apply(lambda x: isinstance(x,str) and 'penginapan' in x)]
hotel_df.drop(['Unnamed: 0'],axis=1,inplace=True)
filtered_hotel_df = hotel_df[hotel_df['rating'] >= 4.4]
filtered_hotel_df = filtered_hotel_df[filtered_hotel_df['total_review'] > 30]
return filtered_hotel_df
def get_recommendation_cosine(city: str, user_preferences: List[str]):
vectors=tv.fit_transform(content_based_data.preferences).toarray()
str_user_preferences = ' '.join(user_preferences)
user_vector = tv.transform([str_user_preferences]).toarray()
# Calculate similarity scores between user preferences and content data
similarity_scores =tf.keras.losses.cosine_similarity(user_vector, vectors)
sorted_indices = tf.argsort(similarity_scores)
k = 0
top_k = 10
top_similar_places = []
for index in sorted_indices:
if k >= top_k:
break
place_id = content_based_data.iloc[index.numpy()].id
place = dfs[dfs['id']==place_id]
if (place.city == city).all():
top_similar_places.append(place)
k += 1
return top_similar_places
def get_recommendation_rest_cosine(city: str, user_preferences: List[str]):
vectors=tv.fit_transform(resto_content_based_data.preferences).toarray()
str_user_preferences = ' '.join(user_preferences)
user_vector = tv.transform([str_user_preferences]).toarray()
# Calculate similarity scores between user preferences and content data
similarity_scores =tf.keras.losses.cosine_similarity(user_vector, vectors)
sorted_indices = tf.argsort(similarity_scores)
k = 0
top_k = 10
top_similar_places = []
resto_content_lst =[]
for index in sorted_indices:
if k >= top_k:
break
name = resto_content_based_data.iloc[index.numpy()]['name']
place = dfs[dfs['name']==name]
if (place.city == city).all():
top_similar_places.append(place)
k += 1
return top_similar_places
def combine_dataframes(df_list):
combined_df = pd.DataFrame()
for df in df_list:
combined_df = pd.concat([combined_df, df], ignore_index=True)
return combined_df
def df_to_json(lst_row_df):
json_res = []
for df in lst_row_df:
json_res.append(df.to_dict())
@app.post('/get-destination-recommendation')
async def get_destination_recommendation(recommendationAttribute: DestinationRecommendationAttribute):
city = recommendationAttribute.city
user_dest_preferences = recommendationAttribute.user_destination_preferences
user_restaurant_preferences = recommendationAttribute.user_restaurant_preferences
# recommend destination
vectors=tv.fit_transform(content_based_data.preferences).toarray()
scaler.fit_transform(vectors)
str_user_dst_preferences = ' '.join(user_dest_preferences)
user_dest_vector = tv.transform([str_user_dst_preferences]).toarray()
scaled_item_dest_vecs = scaler.transform(user_dest_vector)
vms = model.predict(scaled_item_dest_vecs)
similarities = cosine_similarity(vectors, vms)
sorted_similarities = np.sort(similarities,axis=0)[::-1]
sorted_indices = np.argsort(similarities,axis=0)[::-1]
top_k = 20
# Retrieve top-k similar places
top_similar_places_model = []
k = 0
for index in sorted_indices:
if k >= top_k:
break
place_id = content_based_data.iloc[index].id.values
place = dfs[dfs['id']==place_id[0]]
if (place.city == city).all():
top_similar_places_model.append(place)
k += 1
top_similar_places_cosine = get_recommendation_cosine(city, user_dest_preferences)
recom_cosine = pd.DataFrame()
recom_model = pd.DataFrame()
recom_cosine = combine_dataframes(top_similar_places_cosine)
recom_cosine.drop(['Unnamed: 0'],axis=1,inplace=True)
url_cosine_lst = recom_cosine.map_url.tolist()
recom_model = combine_dataframes(top_similar_places_model)
recom_model.drop(['Unnamed: 0'],axis=1,inplace=True)
url_model_lst = recom_model.map_url.tolist()
duplicates = [url for url in url_model_lst if url in url_cosine_lst]
recom_model = recom_model[~recom_model['map_url'].isin(duplicates)]
recom_dest = pd.concat([recom_cosine.sample(5), recom_model.sample(5)], ignore_index=True)
recom_dest_lst = [i for _,i in recom_dest.iterrows()]
json_recom_dest = []
for df in recom_dest_lst:
json_recom_dest.append(df.to_dict())
# recommend hotel
filtered_hotel_df = get_filtered_hotel_df()
hotel_recommendation_df = hotel_recommender(filtered_hotel_df, city)
recom_hotel_lst = [i for _,i in hotel_recommendation_df.iterrows()]
json_hotel_dest = []
for df in recom_hotel_lst:
json_hotel_dest.append(df.to_dict())
# recommend tempat makan
vectors2=tv2.fit_transform(resto_content_based_data.preferences).toarray()
scaler2.fit_transform(vectors2)
str_user_restaurant_preferences = ' '.join(user_restaurant_preferences)
user_rest_vector = tv2.transform([str_user_restaurant_preferences]).toarray()
scaled_item_rest_vecs = scaler2.transform(user_rest_vector)
vms2 = model2.predict(scaled_item_rest_vecs)
similarities2 = cosine_similarity(vectors2, vms2)
sorted_similarities2 = np.sort(similarities2,axis=0)[::-1]
sorted_indices2 = np.argsort(similarities2,axis=0)[::-1]
top_k = 15
# Retrieve top-k similar places
top_similar_restaurant_model = []
k = 0
for index in sorted_indices2:
if k >= top_k:
break
place_id = resto_content_based_data.iloc[index].name.values
place = dfs[dfs['name']==place_id[0]]
if (place.city == city).all():
top_similar_restaurant_model.append(place)
k += 1
top_similar_restaurant_cosine = get_recommendation_rest_cosine(city, user_restaurant_preferences)
recom_rest_cosine = pd.DataFrame()
recom_rest_model = pd.DataFrame()
recom_rest_cosine = combine_dataframes(top_similar_restaurant_cosine)
recom_rest_cosine.drop(['Unnamed: 0'],axis=1,inplace=True)
url_cosine_rest_lst = recom_rest_cosine.map_url.tolist()
recom_rest_model = combine_dataframes(top_similar_restaurant_model)
recom_rest_model.drop(['Unnamed: 0'],axis=1,inplace=True)
url_model_rest_lst = recom_rest_model.map_url.tolist()
duplicates = [url for url in url_model_rest_lst if url in url_cosine_rest_lst]
recom_rest_model = recom_rest_model[~recom_rest_model['map_url'].isin(duplicates)]
recom_rest = pd.concat([recom_rest_cosine.sample(3), recom_rest_model.sample(2)], ignore_index=True)
recom_rest_lst = [i for _,i in recom_rest.iterrows()]
json_recom_rest = []
for df in recom_rest_lst:
json_recom_rest.append(df.to_dict())
print(json_recom_rest)
return {"result":
{
"destination_recommendation": json_recom_dest,
"hotel_recommendation": json_hotel_dest,
"restaurant_recommendation": json_recom_rest
}
}