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from flask import Flask, request, render_template
import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler
from src.pipelines.predict_pipeline import CustomData, PredictPipeline
application = Flask(__name__)
app = application
# Route for home page
@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:
data = CustomData(
gender=request.form.get('gender'),
race_ethnicity=request.form.get('ethnicity'),
parental_level_of_education=request.form.get('parental_level_of_education'),
lunch=request.form.get('lunch'),
test_preparation_course=request.form.get('test_preparation_course'),
reading_score=float(request. form. get('writing_score')),
writing_score=float(request. form.get('reading_score'))
)
pred_df = data.get_data_as_dataframe()
print(pred_df)
predict_pipeline = PredictPipeline()
results = predict_pipeline.predict(pred_df)
return render_template('home.html',results = results[0])
if __name__=='__main__':
app.run('0.0.0.0')