added script to test sending the post request to hugging_face deployed fast_api application

test_post_request.py

@@ -0,0 +1,59 @@
+import json
+import requests
+import argparse
+
+import numpy as np
+from sklearn.model_selection import train_test_split
+
+from modeling.ml_model_dev import read_csv_file
+
+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):
+    df_csv = read_csv_file(ARGS.file_csv)
+
+    df_train, df_test = train_test_split(df_csv, test_size=0.1, random_state=4)
+    list_cols = df_train.columns[:-1]
+    X_test, Y_test = df_test.to_numpy()[:, :-1], df_test.to_numpy()[:, -1:]
+    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()