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import os
os.system('pip install openpyxl')
os.system('pip install sentence-transformers')
import pandas as pd
import gradio as gr
import statistics
from sklearn.neighbors import NearestNeighbors
from sentence_transformers import SentenceTransformer
df = pd.read_parquet('df_encoded.parquet')
df['neighbourhood group'][0:2500] = df['neighbourhood group'][0:2500].apply(lambda x : 'Manhattan')
df['neighbourhood group'][2500:5000] = df['neighbourhood group'][0:2500].apply(lambda x : 'Brooklyn')
df['neighbourhood group'][5000:7500] = df['neighbourhood group'][0:2500].apply(lambda x : 'Queens')
df['neighbourhood group'][7500:] = df['neighbourhood group'][0:2500].apply(lambda x : 'Bronx')
df['location'] = df['neighbourhood group']
df = df[['price', 'sq. meters', 'description', 'location', 'host name', 'cancellation_policy', 'house_rules', 'text_vector_']]
df = df.reset_index(drop=True)
df
model = SentenceTransformer('all-mpnet-base-v2') #all-MiniLM-L6-v2 #all-mpnet-base-v2
#prepare model #we run it anew in the search function every time, after the initial filtering
# nbrs = NearestNeighbors(n_neighbors=3, algorithm='ball_tree').fit(df['text_vector_'].values.tolist())
def closest_number(x):
closest_numbers = [25, 40, 45, 55, 60, 70]
closest_number = closest_numbers[0]
min_distance = abs(x - closest_number)
for number in closest_numbers[1:]:
distance = abs(x - number)
if distance < min_distance:
closest_number = number
min_distance = distance
return closest_number
def search(df, query):
product = model.encode(query).tolist()
# product = df.iloc[0]['text_vector_'] #use one of the products as sample
nbrs = NearestNeighbors(n_neighbors=3, algorithm='ball_tree').fit(df['text_vector_'].values.tolist())
distances, indices = nbrs.kneighbors([product]) #input the vector of the reference object
#print out the description of every recommended product
df_search = df.iloc[list(indices)[0]].drop(['text_vector_'], axis=1) #.sort_values('avgFeedbackScore', ascending=False)
return df_search.sort_values('price', ascending=False)
def filter_df(df, column_name, filter_type, filter_value):
if filter_type == '==':
df_filtered = df[df[column_name]==filter_value]
elif filter_type == '>=':
df_filtered = df[df[column_name]>=filter_value]
elif filter_type == '<=':
df_filtered = df[df[column_name]<=filter_value]
return df_filtered
def predict(history, input1, input2, input3, input4):
history.append([input1, input2, input3, input4])
print(history)
df_location = filter_df(df, 'location', '==', input3)
df_size = filter_df(df_location, 'sq. meters', '==', input2)
df_price = filter_df(df_size, 'price', '<=', input1)
df_result = search(df_price, input4)
prediction = [
round(statistics.mean([x[0] for x in history])), #price
closest_number(statistics.mean([x[1] for x in history])), #square meters
statistics.mode([x[2] for x in history]) #state
]
return df_result, prediction
with gr.Blocks(theme=gr.themes.Soft(primary_hue='amber', secondary_hue='gray', neutral_hue='amber')) as demo:
history = gr.Variable(value=[]) #beginning
gr.Markdown(
"""
# Airbnb Search Engine
"""
)
input1 = gr.Slider(100, 1200, value=700, step_size=100, label="Max Price")
input2 = gr.Radio([25, 40, 45, 55, 60, 70], multiselect=False, label='square meters', value=45)
input3 = gr.Radio(['Manhattan', 'Brooklyn', 'Queens', 'Bronx'], multiselect=False, label='State', value='Brooklyn')
input4 = gr.Textbox(label='Query', value='I want to take a break from work 😴!!!')
btn = gr.Button(value="Search for a Room")
output1 = gr.Dataframe()
output2 = gr.Textbox(label='prediction for the next search')
# btn.click(greet, inputs='text', outputs=['dataframe'])
btn.click(predict, [history, input1, input2, input3, input4], [output1, output2])
demo.launch(share=False) |