Iris-Species-Predictor-ML-Flask-App-With-Materialize.css.zip

from flask import Flask,render_template,url_for,request from flask_material import Material # EDA PKg import pandas as pd import numpy as np # ML Pkg from sklearn.externals import joblib app = Flask(__name__) Material(app) @app.route('/') def index(): return render_template("index.html") @app.route('/preview') def preview(): df = pd.read_csv("data/iris.csv") return render_template("preview.html",df_view = df) @app.route('/',methods=["POST"]) def analyze(): if request.method == 'POST': petal_length = request.form['petal_length'] sepal_length = request.form['sepal_length'] petal_width = request.form['petal_width'] sepal_width = request.form['sepal_width'] model_choice = request.form['model_choice'] # Clean the data by convert from unicode to float sample_data = [sepal_length,sepal_width,petal_length,petal_width] clean_data = [float(i) for i in sample_data] # Reshape the Data as a Sample not Individual Features ex1 = np.array(clean_data).reshape(1,-1) # ex1 = np.array([6.2,3.4,5.4,2.3]).reshape(1,-1) # Reloading the Model if model_choice == 'logitmodel': logit_model = joblib.load('data/logit_model_iris.pkl') result_prediction = logit_model.predict(ex1) elif model_choice == 'knnmodel': knn_model = joblib.load('data/knn_model_iris.pkl') result_prediction = knn_model.predict(ex1) elif model_choice == 'svmmodel': knn_model = joblib.load('data/svm_model_iris.pkl') result_prediction = knn_model.predict(ex1) return render_template('index.html', petal_width=petal_width, sepal_width=sepal_width, sepal_length=sepal_length, petal_length=petal_length, clean_data=clean_data, result_prediction=result_prediction, model_selected=model_choice) if __name__ == '__main__': app.run(debug=True)