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from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
import pandas as pd
import numpy as np
import joblib
# 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
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)
# 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"
if predicted_status == "RETURN TO CLIENT":
probability = 100 - probability
return {"Probability": round(probability,2)}
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