Create app.py

app.py

@@ -0,0 +1,217 @@
+import streamlit as st
+import pandas as pd
+import joblib
+from enum import Enum
+from pydantic import BaseModel, Field, confloat, constr, conlist, ValidationError
+from typing import Optional
+
+# Load the model
+model = joblib.load('lgb_model_main.joblib')
+
+categorical_features = [
+    'NAME_CONTRACT_TYPE',
+    'CODE_GENDER',
+    'NAME_INCOME_TYPE',
+    'NAME_EDUCATION_TYPE',
+    'NAME_FAMILY_STATUS',
+    'OCCUPATION_TYPE',
+    'ORGANIZATION_TYPE',
+]
+
+class ContractType(str, Enum):
+    Cash_loans = "Cash loans"
+    Revolving_loans = "Revolving loans"
+
+class Gender(str, Enum):
+    Male = "M"
+    Female = "F"
+    XNA = "XNA"
+
+class IncomeType(str, Enum):
+    Working = "Working"
+    Other = "Other"
+    Commercial_associate = "Commercial associate"
+    Pensioner = "Pensioner"
+
+class EducationType(str, Enum):
+    Other = "Other"
+    Higher_education = "Higher education"
+    Secondary = "Secondary / secondary special"
+
+class FamilyStatus(str, Enum):
+    Civil_marriage = "Civil marriage"
+    Married = "Married"
+    Single = "Single / not married"
+    Other = "Other"
+
+class OccupationType(str, Enum):
+    Laborers = "Laborers"
+    Sales_staff = "Sales staff"
+    Core_staff = "Core staff"
+    Managers = "Managers"
+    Drivers = "Drivers"
+    Other = "Other"
+
+class OrganizationType(str, Enum):
+    Business_Entity = "Business Entity Type 3"
+    Other = "Other"
+    XNA = "XNA"
+    Self_employed = "Self-employed"
+
+class PredictionInput(BaseModel):
+    AMT_INCOME_TOTAL: confloat(ge=0)
+    AMT_CREDIT: confloat(ge=0)
+    REGION_POPULATION_RELATIVE: confloat(ge=0)
+    DAYS_REGISTRATION: int
+    DAYS_BIRTH: int
+    DAYS_ID_PUBLISH: int
+    FLAG_WORK_PHONE: int
+    FLAG_PHONE: int
+    REGION_RATING_CLIENT_W_CITY: int
+    REG_CITY_NOT_WORK_CITY: int
+    FLAG_DOCUMENT_3: int
+    NAME_CONTRACT_TYPE: ContractType
+    CODE_GENDER: Gender
+    FLAG_OWN_CAR: int
+    NAME_INCOME_TYPE: IncomeType
+    NAME_EDUCATION_TYPE: EducationType
+    NAME_FAMILY_STATUS: FamilyStatus
+    OCCUPATION_TYPE: OccupationType
+    ORGANIZATION_TYPE: OrganizationType
+    CREDIT_ACTIVE_Active_count_Bureau: Optional[int] = None
+    CREDIT_ACTIVE_Closed_count_Bureau: Optional[int] = None
+    DAYS_CREDIT_Bureau: Optional[int] = None
+    AMT_INSTALMENT_mean_HCredit_installments: Optional[int] = None
+    DAYS_INSTALMENT_mean_HCredit_installments: Optional[int] = None
+    NUM_INSTALMENT_NUMBER_mean_HCredit_installments: Optional[int] = None
+    NUM_INSTALMENT_VERSION_mean_HCredit_installments: Optional[int] = None
+    NAME_CONTRACT_STATUS_Active_count_pos_cash: Optional[int] = None
+    NAME_CONTRACT_STATUS_Completed_count_pos_cash: Optional[int] = None
+    SK_DPD_DEF_pos_cash: Optional[int] = None
+    NAME_CONTRACT_STATUS_Refused_count_HCredit_PApp: Optional[int] = None
+    NAME_GOODS_CATEGORY_Other_count_HCredit_PApp: Optional[int] = None
+    NAME_PORTFOLIO_Cash_count_HCredit_PApp: Optional[int] = None
+    NAME_PRODUCT_TYPE_walk_in_count_HCredit_PApp: Optional[int] = None
+    NAME_SELLER_INDUSTRY_Other_count_HCredit_PApp: Optional[int] = None
+    NAME_YIELD_GROUP_high_count_HCredit_PApp: Optional[int] = None
+    NAME_YIELD_GROUP_low_action_count_HCredit_PApp: Optional[int] = None
+    AMT_CREDIT_HCredit_PApp: Optional[int] = None
+    SELLERPLACE_AREA_HCredit_PApp: Optional[int] = None
+
+def make_prediction(input_data: dict):
+    try:
+        # Convert dictionary to a pandas DataFrame
+        input_df = pd.DataFrame([input_data])
+
+        # Convert categorical features to 'category' type
+        for feature in categorical_features:
+            input_df[feature] = input_df[feature].astype('category')
+
+        # Make predictions using the loaded model
+        predictions = model.predict_proba(input_df, categorical_feature=categorical_features)[:, 1]
+
+        # Placeholder response for demonstration
+        response = {"Probability for this credit to be defaulted is: ": predictions[0]}  # Extract the probability for class 1
+
+        return response
+    except Exception as e:
+        return {"error": str(e)}
+
+def main():
+    st.title("Credit Default Prediction")
+
+    st.header("Input Data")
+    with st.form(key='input_form'):
+        AMT_INCOME_TOTAL = st.number_input("AMT_INCOME_TOTAL", min_value=0.0, format="%f")
+        AMT_CREDIT = st.number_input("AMT_CREDIT", min_value=0.0, format="%f")
+        REGION_POPULATION_RELATIVE = st.number_input("REGION_POPULATION_RELATIVE", min_value=0.0, format="%f")
+        DAYS_REGISTRATION = st.number_input("DAYS_REGISTRATION", min_value=-100000, max_value=100000, format="%d")
+        DAYS_BIRTH = st.number_input("DAYS_BIRTH", min_value=-100000, max_value=100000, format="%d")
+        DAYS_ID_PUBLISH = st.number_input("DAYS_ID_PUBLISH", min_value=-100000, max_value=100000, format="%d")
+        FLAG_WORK_PHONE = st.number_input("FLAG_WORK_PHONE", min_value=0, max_value=1, format="%d")
+        FLAG_PHONE = st.number_input("FLAG_PHONE", min_value=0, max_value=1, format="%d")
+        REGION_RATING_CLIENT_W_CITY = st.number_input("REGION_RATING_CLIENT_W_CITY", min_value=0, max_value=10, format="%d")
+        REG_CITY_NOT_WORK_CITY = st.number_input("REG_CITY_NOT_WORK_CITY", min_value=0, max_value=1, format="%d")
+        FLAG_DOCUMENT_3 = st.number_input("FLAG_DOCUMENT_3", min_value=0, max_value=1, format="%d")
+        NAME_CONTRACT_TYPE = st.selectbox("NAME_CONTRACT_TYPE", list(ContractType))
+        CODE_GENDER = st.selectbox("CODE_GENDER", list(Gender))
+        FLAG_OWN_CAR = st.number_input("FLAG_OWN_CAR", min_value=0, max_value=1, format="%d")
+        NAME_INCOME_TYPE = st.selectbox("NAME_INCOME_TYPE", list(IncomeType))
+        NAME_EDUCATION_TYPE = st.selectbox("NAME_EDUCATION_TYPE", list(EducationType))
+        NAME_FAMILY_STATUS = st.selectbox("NAME_FAMILY_STATUS", list(FamilyStatus))
+        OCCUPATION_TYPE = st.selectbox("OCCUPATION_TYPE", list(OccupationType))
+        ORGANIZATION_TYPE = st.selectbox("ORGANIZATION_TYPE", list(OrganizationType))
+
+        CREDIT_ACTIVE_Active_count_Bureau = st.number_input("CREDIT_ACTIVE_Active_count_Bureau", min_value=0, format="%d", value=0)
+        CREDIT_ACTIVE_Closed_count_Bureau = st.number_input("CREDIT_ACTIVE_Closed_count_Bureau", min_value=0, format="%d", value=0)
+        DAYS_CREDIT_Bureau = st.number_input("DAYS_CREDIT_Bureau", min_value=-100000, max_value=100000, format="%d", value=0)
+        AMT_INSTALMENT_mean_HCredit_installments = st.number_input("AMT_INSTALMENT_mean_HCredit_installments", min_value=0, format="%f", value=0.0)
+        DAYS_INSTALMENT_mean_HCredit_installments = st.number_input("DAYS_INSTALMENT_mean_HCredit_installments", min_value=-100000, max_value=100000, format="%d", value=0)
+        NUM_INSTALMENT_NUMBER_mean_HCredit_installments = st.number_input("NUM_INSTALMENT_NUMBER_mean_HCredit_installments", min_value=0, format="%d", value=0)
+        NUM_INSTALMENT_VERSION_mean_HCredit_installments = st.number_input("NUM_INSTALMENT_VERSION_mean_HCredit_installments", min_value=0, format="%d", value=0)
+        NAME_CONTRACT_STATUS_Active_count_pos_cash = st.number_input("NAME_CONTRACT_STATUS_Active_count_pos_cash", min_value=0, format="%d", value=0)
+        NAME_CONTRACT_STATUS_Completed_count_pos_cash = st.number_input("NAME_CONTRACT_STATUS_Completed_count_pos_cash", min_value=0, format="%d", value=0)
+        SK_DPD_DEF_pos_cash = st.number_input("SK_DPD_DEF_pos_cash", min_value=0, format="%d", value=0)
+        NAME_CONTRACT_STATUS_Refused_count_HCredit_PApp = st.number_input("NAME_CONTRACT_STATUS_Refused_count_HCredit_PApp", min_value=0, format="%d", value=0)
+        NAME_GOODS_CATEGORY_Other_count_HCredit_PApp = st.number_input("NAME_GOODS_CATEGORY_Other_count_HCredit_PApp", min_value=0, format="%d", value=0)
+        NAME_PORTFOLIO_Cash_count_HCredit_PApp = st.number_input("NAME_PORTFOLIO_Cash_count_HCredit_PApp", min_value=0, format="%d", value=0)
+        NAME_PRODUCT_TYPE_walk_in_count_HCredit_PApp = st.number_input("NAME_PRODUCT_TYPE_walk_in_count_HCredit_PApp", min_value=0, format="%d", value=0)
+        NAME_SELLER_INDUSTRY_Other_count_HCredit_PApp = st.number_input("NAME_SELLER_INDUSTRY_Other_count_HCredit_PApp", min_value=0, format="%d", value=0)
+        NAME_YIELD_GROUP_high_count_HCredit_PApp = st.number_input("NAME_YIELD_GROUP_high_count_HCredit_PApp", min_value=0, format="%d", value=0)
+        NAME_YIELD_GROUP_low_action_count_HCredit_PApp = st.number_input("NAME_YIELD_GROUP_low_action_count_HCredit_PApp", min_value=0, format="%d", value=0)
+        AMT_CREDIT_HCredit_PApp = st.number_input("AMT_CREDIT_HCredit_PApp", min_value=0, format="%f", value=0.0)
+        SELLERPLACE_AREA_HCredit_PApp = st.number_input("SELLERPLACE_AREA_HCredit_PApp", min_value=0, format="%d", value=0)
+
+        submit_button = st.form_submit_button(label='Predict')
+
+    if submit_button:
+        input_data = {
+            "AMT_INCOME_TOTAL": AMT_INCOME_TOTAL,
+            "AMT_CREDIT": AMT_CREDIT,
+            "REGION_POPULATION_RELATIVE": REGION_POPULATION_RELATIVE,
+            "DAYS_REGISTRATION": DAYS_REGISTRATION,
+            "DAYS_BIRTH": DAYS_BIRTH,
+            "DAYS_ID_PUBLISH": DAYS_ID_PUBLISH,
+            "FLAG_WORK_PHONE": FLAG_WORK_PHONE,
+            "FLAG_PHONE": FLAG_PHONE,
+            "REGION_RATING_CLIENT_W_CITY": REGION_RATING_CLIENT_W_CITY,
+            "REG_CITY_NOT_WORK_CITY": REG_CITY_NOT_WORK_CITY,
+            "FLAG_DOCUMENT_3": FLAG_DOCUMENT_3,
+            "NAME_CONTRACT_TYPE": NAME_CONTRACT_TYPE,
+            "CODE_GENDER": CODE_GENDER,
+            "FLAG_OWN_CAR": FLAG_OWN_CAR,
+            "NAME_INCOME_TYPE": NAME_INCOME_TYPE,
+            "NAME_EDUCATION_TYPE": NAME_EDUCATION_TYPE,
+            "NAME_FAMILY_STATUS": NAME_FAMILY_STATUS,
+            "OCCUPATION_TYPE": OCCUPATION_TYPE,
+            "ORGANIZATION_TYPE": ORGANIZATION_TYPE,
+            "CREDIT_ACTIVE_Active_count_Bureau": CREDIT_ACTIVE_Active_count_Bureau,
+            "CREDIT_ACTIVE_Closed_count_Bureau": CREDIT_ACTIVE_Closed_count_Bureau,
+            "DAYS_CREDIT_Bureau": DAYS_CREDIT_Bureau,
+            "AMT_INSTALMENT_mean_HCredit_installments": AMT_INSTALMENT_mean_HCredit_installments,
+            "DAYS_INSTALMENT_mean_HCredit_installments": DAYS_INSTALMENT_mean_HCredit_installments,
+            "NUM_INSTALMENT_NUMBER_mean_HCredit_installments": NUM_INSTALMENT_NUMBER_mean_HCredit_installments,
+            "NUM_INSTALMENT_VERSION_mean_HCredit_installments": NUM_INSTALMENT_VERSION_mean_HCredit_installments,
+            "NAME_CONTRACT_STATUS_Active_count_pos_cash": NAME_CONTRACT_STATUS_Active_count_pos_cash,
+            "NAME_CONTRACT_STATUS_Completed_count_pos_cash": NAME_CONTRACT_STATUS_Completed_count_pos_cash,
+            "SK_DPD_DEF_pos_cash": SK_DPD_DEF_pos_cash,
+            "NAME_CONTRACT_STATUS_Refused_count_HCredit_PApp": NAME_CONTRACT_STATUS_Refused_count_HCredit_PApp,
+            "NAME_GOODS_CATEGORY_Other_count_HCredit_PApp": NAME_GOODS_CATEGORY_Other_count_HCredit_PApp,
+            "NAME_PORTFOLIO_Cash_count_HCredit_PApp": NAME_PORTFOLIO_Cash_count_HCredit_PApp,
+            "NAME_PRODUCT_TYPE_walk_in_count_HCredit_PApp": NAME_PRODUCT_TYPE_walk_in_count_HCredit_PApp,
+            "NAME_SELLER_INDUSTRY_Other_count_HCredit_PApp": NAME_SELLER_INDUSTRY_Other_count_HCredit_PApp,
+            "NAME_YIELD_GROUP_high_count_HCredit_PApp": NAME_YIELD_GROUP_high_count_HCredit_PApp,
+            "NAME_YIELD_GROUP_low_action_count_HCredit_PApp": NAME_YIELD_GROUP_low_action_count_HCredit_PApp,
+            "AMT_CREDIT_HCredit_PApp": AMT_CREDIT_HCredit_PApp,
+            "SELLERPLACE_AREA_HCredit_PApp": SELLERPLACE_AREA_HCredit_PApp
+        }
+
+        try:
+            input_data_validated = PredictionInput(**input_data)
+            prediction = make_prediction(input_data_validated.dict())
+            st.write(prediction)
+        except ValidationError as e:
+            st.error(f"Validation error: {e}")
+
+if __name__ == "__main__":
+    main()