Create app.py

app.py

@@ -0,0 +1,38 @@
+import gradio as gr
+import joblib
+import pandas as pd
+
+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):
+    input_dict = {
+        "company_code": company_code,
+        "document_type": document_type,
+        "amount": amount,
+        "due_in_days": due_in_days
+    }
+    input_df = pd.DataFrame([input_dict])
+    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]
+    pred = model.predict(input_df)[0]
+    return f"Overdue: {bool(pred)} (Probability: {proba:.2f})"
+
+iface = gr.Interface(
+    fn=predict,
+    inputs=[
+        gr.Dropdown(['CompanyA', 'CompanyB', 'CompanyC'], label="Company Code"),
+        gr.Dropdown(['INV', 'CRN', 'DBN'], label="Document Type"),
+        gr.Number(label="Amount"),
+        gr.Number(label="Due In Days")
+    ],
+    outputs="text",
+    title="SAP AR Overdue Predictor"
+)
+
+if __name__ == "__main__":
+    iface.launch()