Defined get_price function & modified timestamp

test_fp2_14.py

@@ -0,0 +1,63 @@
+from fp2_14 import get_product_prices
+import h5py
+import pandas as pd
+import datetime
+import sys
+from datetime import datetime
+import ntplib
+import arrow
+
+project_id = "fp-20230425"
+subscription_name = "fp-subscription-NFL-zipcodes"
+url = "https://www.sayweee.com/en/product/House-Foods-Firm-Tofu-/85175?trace_id=c93f3d1a-80a4-4e1a-8d41-a402ae43186f"
+
+df_new = get_product_prices(project_id, subscription_name, url)
+
+'''
+# When using sys to call the 'get_product_prices' function
+
+if __name__ == '__main__':
+    # Check if the required command-line arguments are provided
+    if len(sys.argv) < 4:
+        print("Usage: python test_fp2_14.py project_id subscription_name url")
+        sys.exit(1)
+
+    project_id = sys.argv[1]
+    subscription_name = sys.argv[2]
+    url = sys.argv[3:]
+
+    get_product_prices(project_id, subscription_name, url)
+'''
+
+
+# Now we have the new dataframe "df" recording the new price listed in weee's page
+# Load such data in the existing HDF5 file
+file_path = 'product1.h5'
+with h5py.File(file_path, 'a') as file:
+    # Load the existing dataframe 'df' from the HDF5 file
+    # REPLACE KEY TO GET THE EXISTING DATAFRAME FOR EACH DIFFERENT PRODUCT
+    df = pd.read_hdf(file, key='df')
+
+    # Add timestamp to the new dataframe "df_new"
+    # timestamp = datetime.now()
+    '''
+    To deal with the issue of inconsistent time when running a script on different systems, we can use 'ntplib' that
+    provides time synchronization with a reliable time server.
+    Also, to ensure that the local time is interpreted in the system's local time zone, we use 'arrow' which offers a
+    sensible and human-friendly approach to format and convert the timestamps.
+    '''
+    # Fetch the time from an NTP server
+    ntp_client = ntplib.NTPClient()
+    response = ntp_client.request('pool.ntp.org')
+    ntp_time = datetime.fromtimestamp(response.tx_time)
+    timestamp = arrow.get(ntp_time).to('local')
+    df_new['Timestamp'] = timestamp
+
+    # Append 'df_new' to the existing df stored in the key
+    df_update = df.append(df_new, ignore_index=True)
+
+    # Save 'df2' to the HDF5 file with the key 'df'
+    # REPLACE KEY FOR DIFFERENT PRODUCT
+    df_update.to_hdf(file, key='df', mode='a')
+
+print("Data appended and saved successfully.")