"""i
mport streamlit as st
import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from joblib import dump, load



# Load and process data
data = pd.read_csv("C:/Users/lenovo/Downloads/Create_score_model/clean_train.csv")
X = data.drop('Credit_Score', axis=1)
y = data['Credit_Score']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

X_train.head()

# Train the model
rf = RandomForestClassifier(random_state=42)
rf.fit(X_train, y_train)

# Save the model
#dump(rf, 'credit_classifier.joblib', compress=('gzip', 9))

# Streamlit App
# %%writefile scoring_app.py
loaded_model = load(rf)

# Streamlit interface
st.title("Credit Score Prediction App")

# Input fields
feature1 = st.number_input("Outstanding Debt")
feature2 = st.number_input("Credit Mix")
feature3 = st.number_input("Credit History Age (in months)")
feature4 = st.number_input("Monthly Balance")
feature5 = st.number_input("Payment Behaviour")
feature6 = st.number_input("Annual Income")
feature7 = st.number_input("Number of Delayed Payments")

# Define target names
target_names = {0: "Good", 1: "Poor", 2: "Standard"}

if st.button("Predict"):
    prediction = loaded_model.predict([[feature1, feature2, feature3, feature4, feature5, feature6, feature7]])
    st.write(f"Predicted Class: {target_names[prediction[0]]}")

"""

import streamlit as st
import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from joblib import dump, load
@st.cache_resource
def load_and_train_model():
    try:
        # Load and process data
        data = pd.read_csv("clean_train.csv")
    except FileNotFoundError:
        st.error("Error: The file 'clean_train.csv' was not found. Please upload the file.")
        st.stop()

    # Splitting data into features and target
    X = data.drop('Credit_Score', axis=1)
    y = data['Credit_Score']
    
    # Train-test split
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

    # Train the Random Forest model
    rf = RandomForestClassifier(random_state=42)
    rf.fit(X_train, y_train)

    # Save the trained model to a file
    dump(rf, 'credit_classifier.joblib')
    return rf

@st.cache_resource
def load_model():
    import os
    if os.path.exists('credit_classifier.joblib'):
        try:
            return load('credit_classifier.joblib')
        except Exception as e:
            st.warning(f"Error loading model: {e}. Retraining the model...")
            return load_and_train_model()
    else:
        st.warning("Model file not found. Training a new model...")
        return load_and_train_model()


# Load the trained model
loaded_model = load_model()

# Streamlit App
st.title("Credit Score Prediction App")
st.markdown("Predict the credit score category based on user inputs.")

# Input fields
st.sidebar.header("Enter Feature Values:")
feature1 = st.sidebar.number_input("Outstanding Debt", min_value=0.0, step=100.0)
feature2 = st.sidebar.selectbox("Credit Mix (0=Bad, 1=Good, 2=Excellent)", [0, 1, 2])
feature3 = st.sidebar.number_input("Credit History Age (in months)", min_value=0.0, step=1.0)
feature4 = st.sidebar.number_input("Monthly Balance", min_value=0.0, step=100.0)
feature5 = st.sidebar.number_input("Payment Behaviour", min_value=0.0, step=1.0)
feature6 = st.sidebar.number_input("Annual Income", min_value=0.0, step=1000.0)
feature7 = st.sidebar.number_input("Number of Delayed Payments", min_value=0, step=1)

# Define target names
target_names = {0: "Good", 1: "Poor", 2: "Standard"}

# Predict Button
if st.button("Predict"):
    try:
        # Prepare the input as a 2D array
        inputs = np.array([[feature1, feature2, feature3, feature4, feature5, feature6, feature7]])
        
        # Make prediction
        prediction = loaded_model.predict(inputs)
        
        # Display result
        st.success(f"Predicted Credit Score: {target_names[prediction[0]]}")
    except Exception as e:
        st.error(f"An error occurred during prediction: {e}")