Spaces:
Runtime error
Runtime error
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) |