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| import json | |
| import requests | |
| import argparse | |
| import numpy as np | |
| from sklearn.model_selection import train_test_split | |
| from modeling.data_utils import WaterPotabilityDataLoader | |
| class NumpyEncoder(json.JSONEncoder): | |
| def default(self, obj): | |
| if isinstance(obj, np.ndarray): | |
| return obj.tolist() | |
| return json.JSONEncoder.default(self, obj) | |
| def send_post_reqest(ARGS): | |
| water_pot_dataset = WaterPotabilityDataLoader(ARGS.file_csv) | |
| water_pot_dataset.read_csv_file() | |
| water_pot_dataset.split_data() | |
| list_cols = water_pot_dataset.df_csv.columns[:-1] | |
| X_test, Y_test = water_pot_dataset.get_data_from_data_frame(which_set="test") | |
| print(X_test.shape) | |
| url = "https://abhishekrs4-ml-water-potability.hf.space/predict" | |
| # the endpoint of the post request | |
| headers = {"Content-type": "application/json"} | |
| # additional headers to indicate the content type of the post request | |
| # perform 20 post requests | |
| for i in range(0, ARGS.num_requests): | |
| list_values = list(X_test[i, :]) | |
| encoded_data = dict(zip(list_cols, list_values)) | |
| print(encoded_data) | |
| result = requests.post(url, data=json.dumps(encoded_data), headers=headers) | |
| print(f"{json.loads(result.text)} \n") | |
| # print(f"{type(json.loads(result.text))} \n") | |
| return | |
| def main(): | |
| file_csv = "dataset/water_potability.csv" | |
| num_requests = 20 | |
| parser = argparse.ArgumentParser( | |
| formatter_class=argparse.ArgumentDefaultsHelpFormatter | |
| ) | |
| parser.add_argument( | |
| "--file_csv", default=file_csv, type=str, help="full path to dataset csv file" | |
| ) | |
| parser.add_argument( | |
| "--num_requests", | |
| default=num_requests, | |
| type=int, | |
| help="number of post requests to send", | |
| ) | |
| ARGS, unparsed = parser.parse_known_args() | |
| send_post_reqest(ARGS) | |
| return | |
| if __name__ == "__main__": | |
| main() | |