import streamlit as st
import pandas as pd
import numpy as np
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.cluster import KMeans
import pickle

# Load dataset (For demonstration purposes, we'll generate dummy data)
def load_data():
    np.random.seed(42)
    customers = pd.DataFrame({
        'CustomerID': range(1, 101),
        'Spending_Score': np.random.randint(1, 101, 100),
        'Annual_Income': np.random.randint(15000, 120000, 100),
        'Purchases': np.random.randint(1, 50, 100)
    })
    return customers

def train_knn_model(customers):
    X = customers[['Spending_Score', 'Annual_Income', 'Purchases']]
    scaler = StandardScaler()
    X_scaled = scaler.fit_transform(X)
    
    # Train KNN model for segmentation
    kmeans = KMeans(n_clusters=3, random_state=42, n_init=10)
    customers['Segment'] = kmeans.fit_predict(X_scaled)
    
    # Train KNN classifier for recommendations
    knn = KNeighborsClassifier(n_neighbors=5)
    knn.fit(X_scaled, customers['Segment'])
    
    # Save models
    with open("scaler.pkl", "wb") as f:
        pickle.dump(scaler, f)
    with open("knn_model.pkl", "wb") as f:
        pickle.dump(knn, f)
    with open("kmeans_model.pkl", "wb") as f:
        pickle.dump(kmeans, f)
    
    return customers

# Load trained models
def load_models():
    with open("scaler.pkl", "rb") as f:
        scaler = pickle.load(f)
    with open("knn_model.pkl", "rb") as f:
        knn = pickle.load(f)
    with open("kmeans_model.pkl", "rb") as f:
        kmeans = pickle.load(f)
    return scaler, knn, kmeans

st.title("Customer Behavior Analysis and Product Recommendations using KNN")

# Instructions at the top of the app

st.markdown("""
**How to Use This App:**

This app helps you understand customer segments and provides product recommendations. It has two tabs: "Customer Segmentation" and "Product Recommendation."

**Important:** It is recommended to use the **Customer Segmentation tab first** to understand how customers are grouped. This will give context to the product recommendations.

**Customer Segmentation Tab:**

1. **Enter Customer ID:** Type the ID of the customer you want to analyze (1-100).
2. **View Segment:** The app shows the customer's segment (0, 1, or 2).
3. **View Customer Data:**  See the customer's spending score, income, and purchases.

**Product Recommendation Tab:**

1. **Spending Score:** Enter the customer's spending habits (1-100).
2. **Annual Income:** Enter the customer's income (15000-120000).
3. **Number of Purchases:** Enter how often the customer make purchases (1-50).
4. **View Segment:** The app shows the customer's segment.
5. **View Recommended Products:**  See products recommended for the customer's segment.

**Important Notes:**

* Data is for demonstration. Real data is more complex.
* Recommendations are based on simplified segments.
* Models are pre-trained. You don't need to train them.
""")

customers = load_data()
customers = train_knn_model(customers)
scaler, knn, kmeans = load_models()

tab1, tab2 = st.tabs(["Customer Segmentation", "Product Recommendation"])

with tab1:
    st.subheader("Customer Segmentation")
    customer_id = st.number_input("Enter Customer ID (1-100)", min_value=1, max_value=100, step=1)
    if customer_id:
        customer = customers[customers['CustomerID'] == customer_id]
        if not customer.empty:
            st.write(f"Segment: {customer['Segment'].values[0]}")
            st.write("Customer Data:", customer)
        else:
            st.write("Invalid Customer ID. Please enter a number between 1 and 100.")

with tab2:
    st.subheader("Product Recommendation")

    spending = st.number_input("Spending Score (1-100)", min_value=1, max_value=100, value=50, step=1)
    income = st.number_input("Annual Income (15000-120000)", min_value=15000, max_value=120000, value=60000, step=1000)
    purchases = st.number_input("Number of Purchases (1-50)", min_value=1, max_value=50, value=10, step=1)

    if spending and income and purchases:
        user_input = np.array([[spending, income, purchases]])
        user_scaled = scaler.transform(user_input)
        segment = knn.predict(user_scaled)[0]

        st.write(f"Based on the profile of the customer, the customer belongs to Segment {segment}.")

        recommendations = {
            0: ["Laptop", "Smartphone", "Headphones"],
            1: ["Shoes", "Clothing", "Watches"],
            2: ["Furniture", "Kitchenware", "Home Decor"]
        }

        st.write("For this customer, the recommended products are:")
        for product in recommendations[segment]:
            st.write(f"- {product}")

    else:
        st.write("Please provide all input values (Spending Score, Annual Income, and Number of Purchases) to get recommendations.")