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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 | |
def index(): | |
return render_template('index.html') | |
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') |