Update main.py

main.py

@@ -1,70 +1,91 @@
-from fastapi import FastAPI, HTTPException
-from fastapi.middleware.cors import CORSMiddleware
-from pydantic import BaseModel
+import asyncio
+from fastapi import FastAPI
+import requests
 import pandas as pd
-import numpy as np
-import joblib
+import json
 
-
-# Load your trained model and encoders
-xgb_model = joblib.load("xgb_model.joblib")
-encoders = joblib.load("encoders.joblib")
-
-# Function to handle unseen labels during encoding
-def safe_transform(encoder, column):
-    classes = encoder.classes_
-    return [encoder.transform([x])[0] if x in classes else -1 for x in column]
-
-# Define FastAPI app
+global data
 app = FastAPI()
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-
-# Endpoint for making predictions
-@app.post("/predict")
-def predict(customer_name: str,
-    customer_address: str,
-    customer_phone: str,
-    customer_email: str,
-    cod:str,
-    weight: str,
-    pickup_address: str,
-    origin_city_name: str,
-    destination_city_name: str,
-    origin_country: str):
-    # Convert input data to DataFrame
-    input_data = {
-        'customer_name': customer_name,
-        'customer_address': customer_address,
-        'customer_phone': customer_phone,
-        'customer_email': customer_email,
-        'cod': float(cod),
-        'weight': float(weight),
-        'pickup_address':pickup_address,
-        'origin_city.name':origin_city_name,
-        'destination_city.name':destination_city_name
-    }
-    input_df = pd.DataFrame([input_data])
-
-    # Encode categorical variables using the same encoders used during training
-    for col in input_df.columns:
-        if col in encoders:
-            input_df[col] = safe_transform(encoders[col], input_df[col])
 
-    # Predict and obtain probabilities
-    pred = xgb_model.predict(input_df)
-    pred_proba = xgb_model.predict_proba(input_df)
+# Declare the continuous function as an async function.
+async def your_continuous_function():
+    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
 
-    # Output
-    predicted_status = "Unknown" if pred[0] == -1 else encoders['status.name'].inverse_transform([pred])[0]
-    probability = pred_proba[0][pred[0]] * 100 if pred[0] != -1 else "Unknown"
+# 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())
 
-    if predicted_status == "RETURN TO CLIENT":
-       probability = 100 - probability
-
-    return {"Probability": round(probability,2)}
+@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": status_counts_more_than_80[0],
+                "incorrect_values": status_counts_more_than_80[1]
+            },
+            "status_counts_50_to_80": {
+                "correct_values": status_counts_50_to_80[0],
+                "incorrect_values": status_counts_50_to_80[1]
+            },
+            "status_counts_30_to_49": {
+                "correct_values": status_counts_30_to_49[0],
+                "incorrect_values": status_counts_30_to_49[1]
+            },
+            "status_counts_below_30": {
+                "correct_values": status_counts_below_30[0],
+                "incorrect_values": status_counts_below_30[1]
+            }
+        }
+    }
+    return kpi_result