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from flask import Flask, render_template, request
import numpy as np
import joblib
app = Flask(__name__)
# Load saved model and scaler
model = joblib.load('model/population_model.pkl') # Trained Logistic Regression model
scaler = joblib.load('model/scaler.pkl') # MinMaxScaler or StandardScaler
label_encoder = joblib.load('model/label_encoder.pkl') # LabelEncoder for decoding prediction
@app.route('/')
def home():
return render_template('index.html')
@app.route('/predict', methods=['POST'])
def predict():
try:
# Get form values
area = float(request.form['area'])
density = float(request.form['density'])
population = float(request.form['population'])
# Prepare input for prediction
input_data = np.array([[population, area, density]])
input_scaled = scaler.transform(input_data)
# Predict
prediction = model.predict(input_scaled)
predicted_label = label_encoder.inverse_transform(prediction)[0]
return render_template('index.html', prediction_text=f'Predicted Population Category: {predicted_label}')
except Exception as e:
return render_template('index.html', prediction_text=f'Error: {str(e)}')
if __name__ == '__main__':
app.run(debug=True)
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