Update app.py

app.py

@@ -1,11 +1,14 @@
+import os
 import gradio as gr
 import pandas as pd
 import joblib
 
+# Load your model and feature list
 model = joblib.load("ar_overdue_model.joblib")
 feature_names = joblib.load("ar_model_features.joblib")
 
 def predict(company_code, document_type, amount, due_in_days):
+    # Build the input DataFrame
     input_dict = {
         "company_code": company_code,
         "document_type": document_type,
@@ -13,25 +16,35 @@ def predict(company_code, document_type, amount, due_in_days):
         "due_in_days": due_in_days
     }
     input_df = pd.DataFrame([input_dict])
+    # One-hot encode and align columns
     input_df = pd.get_dummies(input_df)
     for col in feature_names:
         if col not in input_df.columns:
             input_df[col] = 0
     input_df = input_df[feature_names]
-    proba = model.predict_proba(input_df)[0,1]
+    # Predict
+    proba = model.predict_proba(input_df)[0, 1]
     pred = model.predict(input_df)[0]
     return f"Overdue: {bool(pred)} (Probability: {proba:.2f})"
 
+# Define the Gradio interface
 iface = gr.Interface(
     fn=predict,
     inputs=[
         gr.Dropdown(['CompanyA', 'CompanyB', 'CompanyC'], label="Company Code"),
-        gr.Dropdown(['INV', 'CRN', 'DBN'], label="Document Type"),
+        gr.Dropdown(['INV', 'CRN', 'DBN'],      label="Document Type"),
         gr.Number(label="Amount"),
         gr.Number(label="Due In Days")
     ],
-    outputs="text"
+    outputs="text",
+    title="AR Overdue Prediction",
+    description="Enter invoice details to predict overdue probability."
 )
 
 if __name__ == "__main__":
-    iface.launch()
+    # 1) Turn on the async queue so the /api/* routes get mounted
+    iface = iface.queue()
+    # 2) Read the HF Spaces port (default to 7860 locally)
+    port = int(os.environ.get("PORT", 7860))
+    # 3) Launch on all interfaces
+    iface.launch(server_name="0.0.0.0", server_port=port)