app.py

print('INFO: import modules')
import base64
from flask import Flask, request
import json
import pickle
import numpy as np
import os

from required_classes import BertEmbedder, PredictModel


CLS_WEIGHTS = {'mlp': 0.3, 'svc': 0.4, 'xgboost': 0.3}

print('INFO: loading models')
try:
    with open('embedder/embedder.pkl', 'rb') as f:
        embedder = pickle.load(f)
    print('INFO: embedder loaded')
except Exception as e:
    print(f"ERROR: loading embedder failed with: {str(e)}")


classifiers_codes = {}
try:
    for clf_name in os.listdir('classifiers/codes'):
        if '.' == clf_name[0]:
            continue
        with open('classifiers/codes/'+clf_name, 'rb') as f:
            model = pickle.load(f)
            classifiers_codes[clf_name.split('.')[0]] = model
        print(f'INFO: classifier {clf_name} loaded')
except Exception as e:
    print(f"ERROR: loading classifiers failed with: {str(e)}")

classifiers_groups = {}
try:
    for clf_name in os.listdir('classifiers/groups'):
        if '.' == clf_name[0]:
            continue
        with open('classifiers/groups/'+clf_name, 'rb') as f:
            model = pickle.load(f)
            classifiers_groups[clf_name.split('.')[0]] = model
        print(f'INFO: classifier {clf_name} loaded')
except Exception as e:
    print(f"ERROR: loading classifiers failed with: {str(e)}")


def classify_code(text, top_n):
    embed = [embedder(text)]
    preds = {}
    for clf_name in classifiers_codes.keys():
        model = classifiers_codes[clf_name]
        probs = model.predict_proba(embed)
        best_n = np.flip(np.argsort(probs, axis=1,)[0,-top_n:])
        clf_preds = {str(model.classes_[i]): float(probs[0][i]) for i in best_n}
        preds[clf_name] = clf_preds
    return preds


def classify_group(text, top_n):
    embed = [embedder(text)]
    preds = {}
    for clf_name in classifiers_groups.keys():
        model = classifiers_groups[clf_name]
        probs = model.predict_proba(embed)
        best_n = np.flip(np.argsort(probs, axis=1,)[0,-top_n:])
        clf_preds = {str(model.classes_[i]): float(probs[0][i]) for i in best_n}
        preds[clf_name] = clf_preds
    return preds

def get_top_result(preds):
    total_scores = {}
    for clf_name, scores in preds.items():
        clf_name = clf_name.replace('_codes', '').replace('_groups', '')
        for class_name, score in scores.items():
            if class_name in total_scores:
                total_scores[class_name] += CLS_WEIGHTS[clf_name] * score
            else:
                total_scores[class_name] = CLS_WEIGHTS[clf_name] * score

    max_idx = np.array(total_scores.values()).argmax()
    if list(total_scores.values())[max_idx] > 0.5:
        return list(total_scores.keys())[max_idx]
    else:
        return None


app = Flask(__name__)

@app.get("/")
def test_get():
    return {'hello': 'world'}

@app.route("/test", methods=['POST'])
def test():
    data = request.json
    return {'response': data}

@app.route("/predict", methods=['POST'])
def read_root():
    data = request.json
    base64_bytes = str(data['textB64']).encode("ascii")
    sample_string_bytes = base64.b64decode(base64_bytes)
    text = sample_string_bytes.decode("ascii")
    top_n = int(data['top_n'])

    if top_n < 1:
        return {'error': 'top_n should be geather than 0'}
    if text.strip() == '':
        return {'error': 'text is empty'}
    
    pred_codes = classify_code(text, top_n)
    pred_groups = classify_group(text, top_n)
    pred_codes_top = get_top_result(pred_codes)
    pred_groups_top = get_top_result(pred_groups)
    result = {
        "icd10": 
            {'result': pred_codes_top, 'details': pred_codes},
        "dx_group":
            {'result': pred_groups_top, 'details': pred_groups}
    }
    return result

if __name__ == "__main__":
    app.run(host='0.0.0.0', port=7860)