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router.py
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router.py
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# Import the Libraries
import numpy as np
import pandas as pd
from flask import Flask, redirect, render_template, request, jsonify
import joblib
import os
# the function I craeted to process the data in utils.py
import src.util as util
# from src.data_preprocessing import preprocess_new
from src.helper import preprocess_new
# Intialize the Flask APP
app = Flask(__name__)
params = util.load_config(config_dir="config/config.yaml")
# Loading the Model
model = util.pickle_load(params["production_model_path"])
# Route for Home page
@app.route('/')
def home():
return render_template('index.html')
# Route for API Call predict
@app.route('/predict_api', methods=['POST'])
def predict_api():
data_get = request.get_json(force=True)
data = data_get["data"]
# print(data["long"])
# return jsonify(data["long"])
long = data['long']
latit = data['lat']
med_age = data['med_age']
total_rooms = data['total_rooms']
total_bedrooms = data['total_bedrooms']
pop = data['pop']
hold = data['hold']
income = data['income']
ocean = data['ocean']
# return jsonify(ocean)
# # Remmber the Feature Engineering we did
rooms_per_hold = total_rooms / hold
bedroms_per_rooms = total_bedrooms / total_rooms
pop_per_hold = pop / hold
# # Concatenate all Inputs
X_new = pd.DataFrame({'longitude': [long], 'latitude': [latit], 'housing_median_age': [med_age], 'total_rooms': [total_rooms],
'total_bedrooms': [total_bedrooms], 'population': [pop], 'households': [hold], 'median_income': [income],
'ocean_proximity': [ocean], 'rooms_per_household': [rooms_per_hold], 'bedroms_per_rooms': bedroms_per_rooms,
'population_per_household': [pop_per_hold]
})
# # Call the Function and Preprocess the New Instances
X_processed = preprocess_new(X_new)
# # call the Model and predict
y_pred_new = model.predict(X_processed)
y_pred_new = '{:.4f}'.format(y_pred_new[0])
return jsonify(y_pred_new)
# Route for Predict page
@app.route('/predict', methods=['GET', 'POST'])
def predict():
if request.method == 'POST': # while prediction
long = float(request.form['long'])
latit = float(request.form['latit'])
med_age = float(request.form['med_age'])
total_rooms = float(request.form['total_rooms'])
total_bedrooms = float(request.form['total_bedrooms'])
pop = float(request.form['pop'])
hold = float(request.form['hold'])
income = float(request.form['income'])
ocean = request.form['ocean']
# Remmber the Feature Engineering we did
rooms_per_hold = total_rooms / hold
bedroms_per_rooms = total_bedrooms / total_rooms
pop_per_hold = pop / hold
# Concatenate all Inputs
X_new = pd.DataFrame({'longitude': [long], 'latitude': [latit], 'housing_median_age': [med_age], 'total_rooms': [total_rooms],
'total_bedrooms': [total_bedrooms], 'population': [pop], 'households': [hold], 'median_income': [income],
'ocean_proximity': [ocean], 'rooms_per_household': [rooms_per_hold], 'bedroms_per_rooms': bedroms_per_rooms,
'population_per_household': [pop_per_hold]
})
# Call the Function and Preprocess the New Instances
X_processed = preprocess_new(X_new)
# call the Model and predict
y_pred_new = model.predict(X_processed)
y_pred_new = '{:.4f}'.format(y_pred_new[0])
return render_template('predict.html', pred_val=y_pred_new)
else:
return render_template('predict.html')
# Route for About page
@ app.route('/about')
def about():
return render_template('about.html')
# Run the App from the Terminal
if __name__ == '__main__':
app.run(debug=True)