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import os
os.system('pip install openpyxl')
os.system('pip install sentence-transformers')
import pandas as pd
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('all-mpnet-base-v2') #all-MiniLM-L6-v2 #all-mpnet-base-v2
df = pd.read_parquet('df_encoded.parquet')
df
from sklearn.neighbors import NearestNeighbors
import numpy as np
import pandas as pd
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('all-mpnet-base-v2') #all-MiniLM-L6-v2 #all-mpnet-base-v2
#prepare model
# nbrs = NearestNeighbors(n_neighbors=8, algorithm='ball_tree').fit(df['text_vector_'].values.tolist())
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]
return df_filtered
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=8, 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(['skills', 'text_vector_'], axis=1).sort_values('avgFeedbackScore', ascending=False)
return df_search[['shortName', 'location', 'title', 'hourlyRate', 'avgFeedbackScore', 'description']]
# search('I want to hire a person who does both backend and')
df_location = filter_df(df, 'location', '==', 'New York')
df_price = filter_df(df_location, 'hourlyRate', '<=', 80)
search(df_price, 'I want to hire a person who does both backend and')
import gradio as gr
import os
#the first module becomes text1, the second module file1
def greet(price, location, query):
# df1 =
df_location = filter_df(df, 'location', '==', location)
df_price = filter_df(df_location, 'hourlyRate', '<=', price)
df_search = search(df_price, query)
return df_search
with gr.Blocks(theme=gr.themes.Soft(primary_hue='amber', secondary_hue='gray', neutral_hue='amber')) as demo:
gr.Markdown(
"""
# Freelancer Upwork Search
"""
)
input1 = gr.Slider(20, 120, value=90, step_size=5, label="Max Hourly Rate")
input2 = gr.Radio(['New York', 'Chicago', 'Washington'], multiselect=False, label='State', value='New York')
input3 = gr.Textbox(label='Query', value='I want to develop a mobile app')
btn = gr.Button(value="Search for Product")
output = gr.Dataframe()
# btn.click(greet, inputs='text', outputs=['dataframe'])
btn.click(greet, [input1, input2, input3], [output])
demo.launch(share=True) |