import numpy as np
from flask import Flask, request, jsonify, render_template
import pickle
app = Flask(__name__)
model = pickle.load(open('model.pkl', 'rb'))


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


@app.route('/predict', methods=['POST'])
def predict():
    if request.method == 'POST':
        features = [int(i) for i in request.form.values()]
        final_features = [np.array(features)]
        prediction = model.predict(final_features)
        output = round(prediction[0], 2)
    return render_template('index.html', prediction_text='Car price is {}'.
        format(output))


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

0c9fcc81db518387004ae15a5823c0d3bc04bcaf
<mask token>


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


@app.route('/predict', methods=['POST'])
def predict():
    if request.method == 'POST':
        features = [int(i) for i in request.form.values()]
        final_features = [np.array(features)]
        prediction = model.predict(final_features)
        output = round(prediction[0], 2)
    return render_template('index.html', prediction_text='Car price is {}'.
        format(output))


<mask token>

<mask token>


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


@app.route('/predict', methods=['POST'])
def predict():
    if request.method == 'POST':
        features = [int(i) for i in request.form.values()]
        final_features = [np.array(features)]
        prediction = model.predict(final_features)
        output = round(prediction[0], 2)
    return render_template('index.html', prediction_text='Car price is {}'.
        format(output))


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

<mask token>
app = Flask(__name__)
model = pickle.load(open('model.pkl', 'rb'))


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


@app.route('/predict', methods=['POST'])
def predict():
    if request.method == 'POST':
        features = [int(i) for i in request.form.values()]
        final_features = [np.array(features)]
        prediction = model.predict(final_features)
        output = round(prediction[0], 2)
    return render_template('index.html', prediction_text='Car price is {}'.
        format(output))


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

import numpy as np
from flask import Flask, request, jsonify, render_template
import pickle
app = Flask(__name__)
model = pickle.load(open('model.pkl', 'rb'))


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


@app.route('/predict', methods=['POST'])
def predict():
    if request.method == 'POST':
        features = [int(i) for i in request.form.values()]
        final_features = [np.array(features)]
        prediction = model.predict(final_features)
        output = round(prediction[0], 2)
    return render_template('index.html', prediction_text='Car price is {}'.
        format(output))


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