chatgpt/webapp/flask-dump.py
2022-12-17 22:39:21 +02:00

71 lines
1.6 KiB
Python
Executable File

#!/usr/bin/python3
import json
import sqlite3
import requests
import time
from flask import Flask
# Initialize the Flask app
app = Flask(__name__)
# Dump the contents of a SQLite database to the response
@app.route("/dump/<ask_timestamp>")
def dump(ask_timestamp):
# Open the database connection
con = sqlite3.connect("../btc_timeseries.db")
# Create a cursor to navigate the database
cur = con.cursor()
# Fetch all rows from the table
rows = cur.execute("SELECT * FROM timeseries").fetchall()
data = {
"parameter": ask_timestamp,
"rows": rows
}
# Build the response as a string
response = json.dumps(data)
con.close()
for row in rows:
old_timestamp = time.strptime(row[0], "%Y-%m-%d %H:%M:%S")
unix_timestamp = time.mktime(old_timestamp)
if int(ask_timestamp) < int(unix_timestamp):
print('EQUALS: ', ask_timestamp, ' AND ', unix_timestamp)
else:
print('NOPE ', row[0], ' AS ', unix_timestamp)
return response
#@app.route("/dump/timestamp")
#def dump(timestamp):
# # Open the database connection
# con = sqlite3.connect("../btc_timeseries.db")
#
# print(timestamp)
#
# # Create a cursor to navigate the database
# cur = con.cursor()
#
# # Fetch all rows from the table
# rows = cur.execute("SELECT * FROM timeseries").fetchall()
#
# # Build the response as a string
# response = ""
# for row in rows:
# response += ", ".join(row) + "\n"
#
# # Close the database connection
# con.close()
#
# # Return the response
# return response
# Run the app
if __name__ == "__main__":
app.run()