app.py

from flask import Flask, render_template, request
import pandas as pd
import pickle
from sklearn.preprocessing import StandardScaler
from werkzeug.utils import secure_filename
import os

app = Flask(__name__)

# Load the dataset
df = pd.read_csv("purchase_history.csv")

# Load the trained model and scaler
with open('knn_model.pickle', 'rb') as f:
    knn_model = pickle.load(f)

with open('scaler.pickle', 'rb') as f:
    scaler = pickle.load(f)

# Function to make predictions
def predict_purchase(gender, age, salary, price):
    # Encode gender
    gender_encoded = 1 if gender == 'Male' else 0
    
    # Scale the input features
    input_features = scaler.transform([[gender_encoded, age, salary, price]])
    
    # Make prediction
    prediction = knn_model.predict(input_features)[0]
    
    return prediction

# Home page route
@app.route('/')
def home():
    return render_template('index.html')

# Prediction route
@app.route('/predict', methods=['POST'])
def predict():
    if request.method == 'POST':
        gender = request.form['gender']
        age = float(request.form['age'])
        salary = float(request.form['salary'])
        price = float(request.form['price'])
        
        prediction = predict_purchase(gender, age, salary, price)
        prediction_text = "Likely to purchase" if prediction == 1 else "Not likely to purchase"
        
        return render_template('result.html', prediction_text=prediction_text)

def predict_from_csv(file_path):
    df = pd.read_csv(file_path)
    predictions = []
    for index, row in df.iterrows():
        gender = row['Gender']
        age = row['Age']
        salary = row['Salary']
        price = row['Price']
        prediction = predict_purchase(gender, age, salary, price)
        prediction_text = "Likely to purchase" if prediction == 1 else "Not likely to purchase"
        predictions.append(prediction_text)
    return predictions

# File upload route
@app.route('/upload', methods=['POST'])
def upload_file():
    if request.method == 'POST':
        if 'file' not in request.files:
            return 'No file part'
        file = request.files['file']
        if file.filename == '':
            return 'No selected file'
        if file:
            filename = secure_filename(file.filename)
            file.save(os.path.join('uploads', filename))
            predictions = predict_from_csv(os.path.join('uploads', filename))
            return render_template('result.html', predictions=predictions)

# Function to predict purchases for customers in CSV file


if __name__ == '__main__':
    app.run(debug=True)