application.py

# from flask import Flask

# app = Flask(__name__)

# @app.route("/")
# def hello_world():
#     return "<h1>Hello World!</h1>"

# if __name__ == '__main__':
#     app.run(host="0.0.0.0")

import pickle
from flask import Flask, request, jsonify, render_template
import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler

application = Flask(__name__)
app=application

# immort ridge regressor and standardscaler pickler
ridge_model=pickle.load(open('models/ridge.pkl', 'rb'))
standard_scaler=pickle.load(open('models/scaler.pkl', 'rb'))

@app.route("/")
def index():
    return render_template('index.html')

@app.route('/predictdata', methods=['GET','POST']) # type: ignore
def predict_datapoint():
    if request.method=='POST':
        Temperature = float(request.form.get('Temperature')) # type: ignore
        RH = float(request.form.get('RH')) # type: ignore
        WS = float(request.form.get('WS')) # type: ignore
        Rain = float(request.form.get('Rain')) # type: ignore
        FFMC = float(request.form.get('FFMC')) # type: ignore
        DMC = float(request.form.get('DMC')) # type: ignore
        ISI = float(request.form.get('ISI')) # type: ignore
        Classes = float(request.form.get('Classes')) # type: ignore
        Region = float(request.form.get('Region')) # type: ignore

        new_data_scaled=standard_scaler.transform([[Temperature,RH,WS,Rain,FFMC,DMC,ISI,Classes,Region]])
        result=ridge_model.predict(new_data_scaled)

        return render_template('home.html', results=result[0])

    else:
        return render_template(('home.html'))

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