main.py

import asyncio
from fastapi import FastAPI
import requests
import pandas as pd
import json

global data
app = FastAPI()

# Declare the continuous function as an async function.
async def your_continuous_function():
    import pandas as pd
    global data
    while True:
        print("data fetcher running.....")
        
        # Initialize an empty DataFrame to store the combined data
        combined_df = pd.DataFrame()
        
        # Loop through pages 1 to 4
        for page in range(1,6):
            try:
              # Update the payload for each page
              url = "https://dev3.api.curfox.parallaxtec.com/api/admin/test-dataset?sort=id&paginate=2000&page="+str(page)
        
              payload = {}
              headers = {
                'Accept': 'application/json',
                'X-Tenant': 'mng',
                'Authorization': 'Bearer eyJ0eXAiOiJKV1QiLCJhbGciOiJSUzI1NiJ9.eyJhdWQiOiIxIiwianRpIjoiNDE3YTIyNDE4MGUzNDJmMjRiYzlkMjAyYTk3NWE0MjRiYzhlY2I0NDM3NmI0ZDdiN2M5MTQ5NjI4YTM0ZWM1Y2ZlYmZhMGMzOWFkMmNkZWIiLCJpYXQiOjE2OTA3OTk4MjQuNzgwNzY4LCJuYmYiOjE2OTA3OTk4MjQuNzgwNzcyLCJleHAiOjE3MjI0MjIyMjQuNzcwOTk0LCJzdWIiOiIxIiwic2NvcGVzIjpbXX0.OCTRI6oDChKUHsZAbxdrsarqXJEuQpnNuQzo1cp4OWj2KddKfBCMCad9cFA5-aJ2S6INN1WsGZAEa1JtOLSzxnsfLIwRr73ODHiRZM9ZYOLdJukTisbO6ONOkAFbpE48o6CHTFYen_D2QYSxqKmQ1lCO2-FQn18RPK6QOiuHuW1KYTWtxo_wXncRl5Rt2PWyrm88iMClD969-oXcjk-wU7Wr1TFTY01JDLLHYe9sqfUTkRewpzFk2YIg9pt1yTGJRboyF9mqeVcq7fADdOzP7uqUFQnro-dHrzivYYYYXUmc0UBBu7t0FY2SerwHnWObNF-tMxyPX_9xGQjzy9H1NtbvxULIrnqA9WLJBqERhpRGlKH2S_C-eacAUwI548JkwHQGelRJHpAh36xaJqNsOGh0l2HX2LIF52TqdHevj-4FE4AFLPRPqGYYv-v-tL50LRHdbTmLw_n8bpeROba8dNoz7RC-YKdyYRpBsIiaIA78_ltk_-1Qme-u9ArX_Zycf2m9BkefEVpN1DUHOuPRm_pGAyIagkCmMfIFsV9rMiEhCtqT_IV0HmMrBw1OJ2m-c-DxXLdCLpjgiKc4HPb44aCxZgm8s0vJOGGP-2zajtQ1H2IpJ7Zkjyggl4frZ2EkwX4Ay0dsiQxTG5DgKOcQZfvCMgzJCtQPL7So7c8QcUk'
              }
        
              response = requests.request("GET", url, headers=headers, data=payload)
        
              import pandas as pd
              import json
        
              # Sample JSON response
              json_response = response.json()
              # Extracting 'data' for conversion
              data = json_response['data']
        
              df = pd.json_normalize(data)
        
              # Concatenate the current page's DataFrame with the combined DataFrame
              combined_df = pd.concat([combined_df, df], ignore_index=True)
            except:
              print("data over")
        print("data collected....")
        data = combined_df[combined_df['status.name'].isin(['RETURN TO CLIENT', 'DELIVERED'])]
        data = data[['delivery_possibility','status.name']]
        data = data[data['delivery_possibility'].between(0, 100)]
        
        await asyncio.sleep(43200)  # Adjust the sleep interval as needed

# Create a startup event.
@app.on_event("startup")
async def startup_event():
    # Start the continuous function as a background task.
    asyncio.create_task(your_continuous_function())

@app.get("/kpi_results")
def read_root():
    global data
    status_counts_more_than_80 = data[data['delivery_possibility'] > 80]['status.name'].value_counts()
    
    status_counts_50_to_80 = data[(data['delivery_possibility'] >= 50) & (data['delivery_possibility'] <= 80)]['status.name'].value_counts()
    
    status_counts_30_to_49 = data[(data['delivery_possibility'] >= 30) & (data['delivery_possibility'] <= 49)]['status.name'].value_counts()
    
    status_counts_below_30 = data[data['delivery_possibility'] < 30]['status.name'].value_counts()


    kpi_result = {
            "kpi_result": {
                "status_counts_more_than_80": {   
                    "correct_values": str(status_counts_more_than_80[0]),
                    "incorrect_values": str(status_counts_more_than_80[1])
                },
                "status_counts_50_to_80": {
                    "correct_values": str(status_counts_50_to_80[0]),
                    "incorrect_values": str(status_counts_50_to_80[1])
                },
                "status_counts_30_to_49": {
                    "correct_values": str(status_counts_30_to_49[0]),
                    "incorrect_values": str(status_counts_30_to_49[1])
                },
                "status_counts_below_30": {
                    "correct_values": str(status_counts_below_30[0]),
                    "incorrect_values": str(status_counts_below_30[1])
                }
            }
        }
    return kpi_result