EndtoEndMechineLearningProject/app.py at main · valiantProgrammer/EndtoEndMechineLearningProject · GitHub

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from flask import Flask, request, render_template
import numpy as np
import pandas as pd
import os
from sklearn.preprocessing import StandardScaler
from src.pipeline.predict_pipeline import CustomData, PredictPipeline

application = Flask(__name__,
    static_folder=os.path.join(os.path.dirname(__file__), 'static'),
    static_url_path='/static',
    template_folder=os.path.join(os.path.dirname(__file__), 'templates')
)

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", form_data={}, results=None)

    else:
        # Capture form data
        form_data = {
            'gender': request.form.get('gender'),
            'race': request.form.get('race'),
            'parental_education': request.form.get('parental_education'),
            'lunch': request.form.get('lunch'),
            'test_prep': request.form.get('test_prep'),
            'reading_score': request.form.get('reading_score'),
            'writing_score': request.form.get('writing_score')
        }

        data = CustomData(
            gender=form_data['gender'],
            race_ethnicity=form_data['race'],
            parental_level_of_education=form_data['parental_education'],
            lunch=form_data['lunch'],
            test_preparation_course=form_data['test_prep'],
            reading_score=float(form_data['reading_score']),
            writing_score=float(form_data['writing_score'])
        )
        pred_df=data.get_data_as_data_frame()
        print(pred_df)

        predict_pipeline = PredictPipeline()
        results = predict_pipeline.predict(pred_df)
        return render_template('home.html', results=results[0], form_data=form_data)


if __name__ =="__main__":
    app.run(host="0.0.0.0", port=5000, debug=False)