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#%%
import pandas as pd
import numpy as np
import torch
from sentence_transformers.util import cos_sim
from sentence_transformers import SentenceTransformer
import gradio as gr
#%%
etalon = pd.read_csv("etalon_prod.csv")
df = pd.read_csv("preprocessed_complaints.csv")
model = SentenceTransformer('sentence-transformers/multi-qa-distilbert-cos-v1')
unique_complaints = df['Жалобы'].values.tolist()
with open("embeddings.npy", 'rb') as f:
embeddings = np.load(f)
def get_recommend(user_input,
top_k_spec = 3,
top_k_services = 5,
treshold = 0.8):
cols_for_top_k = ["Специальность врача",
"Рекомендуемые специалисты"]
usr_embeddings = model.encode(user_input)
cos_similarity = cos_sim(usr_embeddings, embeddings).detach().numpy()
sorted_idx = cos_similarity[0].argsort()[::-1]
cos_similarity.sort()
cos_similarity = cos_similarity[0][::-1]
sorted_df = df.loc[sorted_idx].copy()
sorted_df['cos_sim'] = cos_similarity
sorted_df = sorted_df[sorted_df['cos_sim'] > treshold]
result = {}
for col in cols_for_top_k:
result[col] = sorted_df[col].value_counts()[:top_k_spec].index.tolist()
result['Жалобы'] = sorted_df['Жалобы'].value_counts()[:top_k_services].index.tolist()
top_k_mkb = sorted_df['Диагноз МКБ'].value_counts()[:top_k_services].index.tolist()
result['Диагноз МКБ'] = top_k_mkb
categories = ['Инструментальная диагностика', 'Лабораторная диагностика']
top_k_services_lst_by_mkb = []
for mkb in top_k_mkb:
temp_lst = []
slice_df = sorted_df[sorted_df['Диагноз МКБ'] == mkb]
for category in categories:
top_k_services_in_cat_mkb = slice_df[slice_df['service_name_category'] == category]['Рекомендации по обследованию'].value_counts()[:top_k_services].index.tolist()
temp_lst.append({category:top_k_services_in_cat_mkb})
top_k_services_lst_by_mkb.append({mkb:temp_lst})
top_k_services_lst = []
for category in categories:
slice_df = sorted_df[sorted_df['service_name_category'] == category]
list_top_k_services = slice_df['Рекомендации по обследованию'].value_counts()[:top_k_services].index.tolist()
top_k_services_lst.append({category:list_top_k_services})
result['Рекомендации по обследованию'] = top_k_services_lst
result['Рекомендации по обследованию по МКБ'] = top_k_services_lst_by_mkb
return result
#%%
gradio_app = gr.Interface(
get_recommend,
inputs='text',
outputs=gr.JSON(label='Рекомендации: '),
# title="Предсказание топ-10 наиболее схожих услуг",
description="Введите услугу:"
)
if __name__ == "__main__":
gradio_app.launch()
# %%
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