app.py

from flask import Flask, render_template, request

import os, sys

import numpy as np
import pandas as pd
from src.irisdataprediction.pipeline.prediction_pipeline import PredictionPipeline
from src.irisdataprediction.exception import IrisPredictionException
from src.irisdataprediction.utils.ml_utils.preprocessor.helper import data_standardization

app = Flask(__name__)

@app.route('/', methods=['GET']) 

def homepage():
    return render_template("index.html")


@app.route('/predict', methods=['POST','GET'])

def index():
    if request.method == 'POST':
        try:
            sepal_length = float(request.form['sepal_length'])
            sepal_width = float(request.form['sepal_width'])
            petal_length = float(request.form['petal_length'])
            petal_width = float(request.form['petal_width'])
            data = [sepal_length, sepal_width, petal_length, petal_width]
            independent_features = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width']
            
            data=pd.DataFrame(np.array(data).reshape(1,-1),columns=independent_features,index=None) 

            normalized_data = data_standardization(data,independent_features)

            prediction_pipeline = PredictionPipeline()
            
            spicies = prediction_pipeline.predict(normalized_data)

            flower_type = ['setosa', 'versicolor', 'virginica']

            return render_template("index.html", prediction=str(flower_type[int(spicies[0])]))
        

        except Exception as e:
            raise IrisPredictionException(e,sys)
    else:
        return render_template("index.html")
    
        

if __name__ == '__main__':
    app.run(host="0.0.0.0", port=8080)