app.py

import pickle
from flask import Flask, request, app, render_template
import numpy as np
import pandas as pd
import gspread

app = Flask(__name__)

# Load the model
model = pickle.load(open("model1.pkl", "rb"))


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


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


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


@app.route('/colleges')
def colleges():
    return render_template('Top Colleges.html')


@app.route('/learn')
def learn():
    return render_template('Coding.html')


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


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


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

    Category = {'0': 'General', '1': 'Other Backward Classes-Non Creamy Layer', '6': 'Scheduled Castes', '8': 'Scheduled Tribes',
                '3': 'General & Persons with Disabilities', '5': 'Other Backward Classes & Persons with Disabilities',
                '7': 'Scheduled Castes & Persons with Disabilities', '9': 'Scheduled Tribes & Persons with Disabilities',
                '1': 'General & Economically Weaker Section', '2': 'General & Economically Weaker Section & Persons with Disability'}

    Quota = {'0': 'All-India', '3': 'Home-State', '1': 'Andhra Pradesh',
             '2': 'Goa', '4': 'Jammu & Kashmir', '5': 'Ladakh'}

    Pool = {'0': 'Neutral', '1': 'Female Only'}

    Institute = {'0': 'IIT', '1': 'NIT'}

    # sa = gspread.service_account(filename="College.json")
    # sh = sa.open("College Data")
    # wks = sh.worksheet("Sheet1")

    data = [x for x in request.form.values()]

    list1 = data.copy()

    list1[2] = Category.get(list1[2])
    list1[3] = Quota.get(list1[3])
    list1[4] = Pool.get(list1[4])
    list1[5] = Institute.get(list1[5])

    data.pop(0)
    data.pop(0)
    data.pop(7)
    data1 = [float(x) for x in data]

    final_output = np.array(data1).reshape(1, -1)
    output = model.predict(final_output)[0]

    list1.append(output[0])
    list1.append(output[1])
    list1.append(output[2])
    # wks.append_row(list1, table_range="A2:M2")

    return render_template("home.html", prediction_text="College : {} ,  Degree : {} , Course : {}".format(output[0], output[1], output[2]), prediction="Thank you, Hope this will match your requirement !!!")


if __name__ == '__main__':
    app.run(debug=True)