Update main.py

main.py

@@ -7,8 +7,8 @@ import joblib
 
 
 # Load your trained model and encoders
-xgb_model = joblib.load("model/xgb_model.joblib")
-encoders = joblib.load("model/encoders.joblib")
+xgb_model = joblib.load("model/transexpress_xgb_model.joblib")
+encoders = joblib.load("model/transexpress_encoders.joblib")
 
 # Function to handle unseen labels during encoding
 def safe_transform(encoder, column):
@@ -33,18 +33,22 @@ def predict(
     customer_phone: str,
     customer_email: str,
     weight: str,
+    cod:str,
     pickup_address: str,
     destination_city_name: str):
     # Convert input data to DataFrame
+
+    if destination_city_name=="":
+        destination_city_name = 'Missing'
+        
     input_data = {
         'customer_name': customer_name,
         'customer_address': customer_address,
         'customer_phone': customer_phone,
         'customer_email': customer_email,
-        'cod': 1.0,
+        '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])
@@ -62,7 +66,7 @@ def predict(
     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":
+    if predicted_status == "Returned to Client":
        probability = 100 - probability
 
     return {"Probability": round(probability,2)}