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app.py
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app.py
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import pandas as pd
import numpy as np
from flask import Flask, request, render_template
from sklearn.preprocessing import StandardScaler
from src.pipeline.predict_pipeline import CustomData, PredictPipeline
import joblib
application = Flask(__name__)
model = joblib.load('artifacts/model.pkl')
app = application
@app.route('/')
def index():
return render_template('index.html')
@app.route('/predictdata', methods=['GET', 'POST'])
def predict_datapoint():
if request.method == 'GET':
return render_template('home.html')
else:
gdp = float(request.form.get('GDP'))
social_support = float(request.form.get('SocialSupport'))
healthy_life = float(request.form.get('HealthyLife'))
freedom = float(request.form.get('Freedom'))
generosity = float(request.form.get('Generosity'))
corruption = float(request.form.get('Corruption'))
'''data = CustomData(
GDP=gdp,
SocailSupport=social_support,
HealthyLife=healthy_life,
Freedom=freedom,
Generosity=generosity,
Corruption=corruption
)
pred_df = data.get_data_as_data_frame()
predict_pipeline = PredictPipeline()'''
list = np.array([gdp,social_support,healthy_life,freedom,generosity,corruption])
list= list.reshape(1,-1)
results = model.predict(list)
return render_template('home.html', results=results)
if __name__ == "__main__":
app.run(host="0.0.0.0", debug=True)