main.py

import asyncio
from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
import requests
import pandas as pd
import json
import os,datetime
import pandas as pd
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.preprocessing import LabelEncoder
from sklearn.utils import resample
from xgboost import XGBClassifier
from sklearn.metrics import accuracy_score, classification_report
from joblib import dump, load
import numpy as np


app = FastAPI()

app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)



def train_the_model():     

        data = pd.read_csv("trainer_data.csv")
        print(data["customer_name"].count())
    
        data = pd.read_csv("trainer_data_balanced.csv")
        print(data["customer_name"].count())
        

        # Select columns
        selected_columns = ['customer_name', 'customer_address', 'customer_phone_no',
                            'weight','cod','pickup_address','client_number','destination_city',
                            'status_name']
        
        # Handling missing values
        #data_filled = data[selected_columns].fillna('Missing')
        data_filled = data[selected_columns].dropna()
        
        # Encoding categorical variables
        encoders = {col: LabelEncoder() for col in selected_columns if data_filled[col].dtype == 'object'}
        for col, encoder in encoders.items():
            data_filled[col] = encoder.fit_transform(data_filled[col])
        
        # Splitting the dataset
        X = data_filled.drop('status_name', axis=1)
        y = data_filled['status_name']
        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
        
        # Parameters to use for the model
        # Parameters to use for the model
        """params = {
            'colsample_bytree': 0.3,
            'learning_rate': 0.6,
            'max_depth': 6,
            'n_estimators': 100,
            'subsample': 0.9,
            'use_label_encoder': False,
            'eval_metric': 'logloss'
        }"""
        params = {
            'colsample_bytree': 0.9,
            'learning_rate': 0.1,
            'max_depth': 30,
            'n_estimators': 500,
            'subsample': 0.9,
            'use_label_encoder': False,
            'eval_metric': 'logloss'
        }
        
        # Initialize the classifier with the specified parameters
        xgb = XGBClassifier(**params)
        
        # Train the model
        xgb.fit(X_train, y_train)        

        
        # Predict on the test set
        y_pred = xgb.predict(X_test)
        y_pred_proba = xgb.predict_proba(X_test)
        
        # Evaluate the model
        accuracy = accuracy_score(y_test, y_pred)
        classification_rep = classification_report(y_test, y_pred)
        
        # Save the model
        model_filename = 'transexpress_xgb_model.joblib'
        dump(xgb, model_filename)
        
        # Save the encoders
        encoders_filename = 'transexpress_encoders.joblib'
        dump(encoders, encoders_filename)
        
        return accuracy,classification_rep,"Model trained with new data"
    
@app.get("/trigger_the_data_fecher")
async def your_continuous_function(page: str,paginate: str):

            
    print("data fetcher running.....")
            
    # Initialize an empty DataFrame to store the combined data
    combined_df = pd.DataFrame()
            
    # Update the payload for each page
    url = "https://report.transexpress.lk/api/orders/delivery-success-rate/return-to-client-orders?page="+page+"&per_page="+paginate
    
    payload = {}
    headers = {
      'Cookie': 'development_trans_express_session=NaFDGzh5WQCFwiortxA6WEFuBjsAG9GHIQrbKZ8B'
    }
            
    response = requests.request("GET", url, headers=headers, data=payload)
            
    # Sample JSON response
    json_response = response.json()
    # Extracting 'data' for conversion
    data = json_response["return_to_client_orders"]['data']

    data_count = len(data)  
    
    df = pd.json_normalize(data)
    
            
    df['status_name'] = df['status_name'].replace('Partially Delivered', 'Delivered')
    df['status_name'] = df['status_name'].replace('Received by Client', 'Returned to Client')
    
    print("data collected from page : "+page)
    #return "done"
    try:
        file_path = 'trainer_data.csv'  # Replace with your file path
        source_csv = pd.read_csv(file_path)
        new_data = df
        combined_df_final = pd.concat([source_csv,new_data], ignore_index=True)
    
        combined_df_final.to_csv("trainer_data.csv")
        print("data added")
    except:
        
        df.to_csv("trainer_data.csv")
        print("data created")

    # Load the dataset
    file_path = 'trainer_data.csv'  # Update to the correct file path
    data = pd.read_csv(file_path)
    # Analyze class distribution
    class_distribution = data['status_name'].value_counts()
    print("Class Distribution before balancing:\n", class_distribution)
    
    # Get the size of the largest class to match other classes' sizes
    max_class_size = class_distribution.max()
    
    # Oversampling
    oversampled_data = pd.DataFrame()
    for class_name, group in data.groupby('status_name'):
        oversampled_group = resample(group,
                                     replace=True,  # Sample with replacement
                                     n_samples=max_class_size,  # to match majority class
                                     random_state=123)  # for reproducibility
        oversampled_data = pd.concat([oversampled_data, oversampled_group], axis=0)
    
    # Verify new class distribution
    print("Class Distribution after oversampling:\n", oversampled_data['status_name'].value_counts())
    
    # Save the balanced dataset if needed
    oversampled_data.to_csv('trainer_data_balanced.csv', index=False)
        

    
    accuracy,classification_rep,message = train_the_model()

    return {"message":message,"page_number":page,"data_count":data_count,"accuracy":accuracy,"classification_rep":classification_rep}


    

@app.get("/get_latest_model_updated_time")
async def model_updated_time():
    try:
        m_time_encoder = os.path.getmtime('transexpress_encoders.joblib')
        m_time_model = os.path.getmtime('transexpress_xgb_model.joblib')
        return {"base model created time ":datetime.datetime.fromtimestamp(m_time_encoder),
                "last model updated time":datetime.datetime.fromtimestamp(m_time_model)}
    except:
        return {"no model found so first trained the model using data fecther"}





# Endpoint for making predictions
@app.post("/predict")
def predict(
    date : str,
    customer_name: str,
    customer_address: str,
    customer_phone: str,
    weight: float,
    cod: int,
    pickup_address: str,
    client_number:str,
    destination_city:str
    ):


    try:
        # Load your trained model and encoders
        xgb_model = load('transexpress_xgb_model.joblib')
        encoders = load('transexpress_encoders.joblib')
    except:
        return {"no model found so first trained the model using data fecther"}

    
    # 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] 
        
    # Convert input data to DataFrame
    input_data = {
        'customer_name': customer_name,
        'customer_address': customer_address,
        'customer_phone_no': customer_phone,
        'weight': float(weight),
        'cod': int(cod),
        'pickup_address':pickup_address,
        'client_number':client_number,
        'destination_city':destination_city
    }
    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)

    # 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"
    print(predicted_status)

    if predicted_status == "Returned to Client":
       probability = 100 - probability

    return {"Probability": round(probability,2)}