app.py

import streamlit as st
import pandas as pd
import pickle
import numpy as np
import os
from openai import OpenAI
import utils as ut

if "GROQ_API_KEY" in os.environ:
    api_key = os.environ.get("GROQ_API_KEY")
else:
    api_key = st.secrets["GROQ_API_KEY"]
    
client = OpenAI(
    base_url="https://api.groq.com/openai/v1",
    api_key=api_key
)

def load_model(file_name):
    with open(file_name, 'rb') as file:
        return pickle.load(file)
    
xgb_model = load_model('xgb_model.pkl')
naive_bayes_model = load_model('nb_model.pkl')
random_forest_model = load_model('rf_model.pkl')
decision_tree_model = load_model('dt_model.pkl')
knn_model = load_model('knn_model.pkl')

def prepare_input_data(credit_score, location, gender, age, tenure, balance, num_products, has_credit_card, is_active_member, estimated_salary):
    input_dict = {
        'CreditScore': credit_score,
        'Age': age,
        'Tenure': tenure,
        'Balance': balance,
        'NumOfProducts': num_products,
        'HasCrCard': has_credit_card,
        'IsActiveMember': is_active_member,
        'EstimatedSalary': estimated_salary,
        'Geography_France': 1 if location == 'France' else 0,
        'Geography_Germany': 1 if location == 'Germany' else 0,
        'Geography_Spain': 1 if location == 'Spain' else 0,
        'Gender_Male': 1 if gender == 'Male' else 0,
        'Gender_Female': 1 if gender == 'Female' else 0
    }

    input_df = pd.DataFrame([input_dict])
    return input_df, input_dict

def make_prediction(input_df, input_dict):
    probabilities = {
        'XGBoost': xgb_model.predict_proba(input_df)[0, 1],
        'Naive Bayes': naive_bayes_model.predict_proba(input_df)[0, 1],
        'Random Forest': random_forest_model.predict_proba(input_df)[0, 1],
        'Decision Tree': decision_tree_model.predict_proba(input_df)[0, 1],
        'K-Nearest Neighbors': knn_model.predict_proba(input_df)[0, 1],
    }
    avg_probability = np.mean(list(probabilities.values()))

    col1, col2 = st.columns(2)

    with col1:
        fig = ut.create_guage_chart(avg_probability)
        st.plotly_chart(fig, use_container_width=True)
        st.write(f"The customer has a {avg_probability:.2f}% probability of churning.")

    with col2:
        fig = ut.create_model_probability_chart(probabilities)
        st.plotly_chart(fig, use_container_width=True)

    st.markdown("### Model Probabilities")
    for model, prob in probabilities.items():
        st.markdown(f"{model}: {prob:.2f}")

    st.markdown(f"### Average Probability: {avg_probability:.2f}")

    return avg_probability

def explain_prediction(probability, input_dict, surname):
    prompt = f"""You are an expert data scientist at a bank, where you specialize in interpreting and explaining predictions of machine learning models.

A customer with the name {surname} has been assessed as having a {round(probability * 100, 1)}% likelihood of churning based on their profile and engagement. Here is the customer's information:

{input_dict}

Here are the machine learning model's top 10 most influential features affecting churn:

Feature | Importance:
-------------------------------
NumOfProducts | 0.323888
IsActiveMember | 0.164146
Age | 0.109550
Geography_Germany | 0.091373
Balance | 0.052786
Geography_France | 0.046463
Gender_Female | 0.045283
Geography_Spain | 0.036855
CreditScore | 0.035005
EstimatedSalary | 0.032655
HasCrCard | 0.031940
Tenure | 0.030054
Gender_Male | 0.000000

{pd.set_option('display.max_columns', None)}

Here are the summary statistics for churned customers:
{df[df['Exited'] == 1].describe()}

Here are the summary statistics for non-churned customers:
{df[df['Exited'] == 0].describe()}

Based on the customer’s probability of churning:
- If the probability is above 40%, generate a brief 3-sentence explanation outlining why the customer is at risk of churning.
- If the probability is below 40%, generate a 3-sentence explanation of why the customer may not be at risk of churning.

The output should only be the explanation itself, based on the customer's information, the summary statistics of churned and non-churned customers, and the most influential features, without mentioning probability, model, or feature names. No extra text or summaries are needed.
"""

    raw_response = client.chat.completions.create(
        model="llama-3.2-3b-preview",
        messages=[{"role": "user", "content": prompt}],
        temperature=0.5
    )
    return raw_response.choices[0].message.content

st.title("Customer Churn Predictor")

df = pd.read_csv('churn.csv')

customers = [f"{row['CustomerId']} - {row['Surname']}" for _, row in df.iterrows()]

selected_customer_option = st.selectbox("Select a customer", customers)

if selected_customer_option:
    selected_customer_id = selected_customer_option.split(' - ')[0]
    selected_customer_surname = selected_customer_option.split(' - ')[1]
    selected_customer = df.loc[df["CustomerId"] == int(selected_customer_id)].iloc[0]

    col1, col2 = st.columns(2)

    with col1:
        credit_score = st.number_input(
            "Credit Score", 
            min_value=300,
            max_value=850,
            value=selected_customer["CreditScore"]
        )
        
        location = st.selectbox(
            "Location",
            ["France", "Spain", "Germany"],
            index=["France", "Spain", "Germany"].index(selected_customer["Geography"])
        )

        gender = st.radio(
            "Gender",
            ["Male", "Female"],
            index=0 if selected_customer["Gender"] == "Male" else 1
        )

        age = st.number_input(
            "Age",
            min_value=18,
            max_value=100,
            value=int(selected_customer["Age"])
        )

        tenure = st.number_input(
            "Tenure (years)",
            min_value=0,
            max_value=50,
            value=int(selected_customer["Tenure"])
        )

    with col2:
        balance = st.number_input(
            "Balance",
            min_value=0.0,
            value=float(selected_customer["Balance"])
        )

        num_products = st.number_input(
            "Number of Products",
            min_value=1,
            max_value=10,
            value=int(selected_customer["NumOfProducts"])
        )   

        has_credit_card = st.checkbox(
            "Has Credit Card",
            value=bool(selected_customer["HasCrCard"])
        )

        is_active_member = st.checkbox(
            "Active Member",
            value=bool(selected_customer["IsActiveMember"])
        )

        estimated_salary = st.number_input(
            "Estimated Salary",
            min_value=0.0,
            value=float(selected_customer["EstimatedSalary"])
        )

    input_df, input_dict = prepare_input_data(credit_score, location, gender, age, tenure, balance, num_products, has_credit_card, is_active_member, estimated_salary)
    avg_probability = make_prediction(input_df, input_dict)
    explanation = explain_prediction(avg_probability, input_dict, selected_customer_surname)

    st.markdown("---")
    st.subheader("Explanation of the Prediction")
    st.markdown(explanation)