Delete app.py

app.py

@@ -1,57 +0,0 @@
-# -*- coding: utf-8 -*-
-"""app.ipynb
-
-Automatically generated by Colaboratory.
-
-Original file is located at
-    https://colab.research.google.com/drive/1HGxLfdQXQt3h4I7aoSTmLSGxPaqhdJbe
-"""
-
-!pip install gradio
-
-import gradio as gr
-import numpy as np
-import pandas as pd
-from sklearn.preprocessing import StandardScaler
-from sklearn.model_selection import train_test_split
-from sklearn import svm
-from sklearn.metrics import accuracy_score
-salim_diabetes_dataset = pd.read_csv('diabetes.csv') 
-X = salim_diabetes_dataset.drop(columns = 'Outcome', axis=1)
-Y = salim_diabetes_dataset['Outcome']
-scaler = StandardScaler()
-scaler.fit(X)
-standardized_data = scaler.transform(X)
-print(standardized_data)
-X = standardized_data
-Y = salim_diabetes_dataset['Outcome']
-from sklearn.neural_network import MLPClassifier
-classifier =  MLPClassifier(max_iter=1000,  alpha=1)
-X_train, X_test, Y_train, Y_test = train_test_split(X,Y, test_size = 0.2, stratify=Y, random_state=2)
-classifier.fit(X_train, Y_train)
-
-def diabetes(Pregnancies, Glucose_levels_in_millimoles_per_litres, Blood_Pressure_in_millimetres_of_mercury, Skin_Thickness_in_millimetres, Insulin_levels, BMI_in_kilogram_per_square_metres, Diabetes_Pedigree, Age_in_years):
-    x = np.array([Pregnancies, Glucose_levels_in_millimoles_per_litres, Blood_Pressure_in_millimetres_of_mercury, Skin_Thickness_in_millimetres, Insulin_levels, BMI_in_kilogram_per_square_metres, Diabetes_Pedigree, Age_in_years])
-    prediction = classifier.predict(x.reshape(1, -1))
-    if prediction == 0:
-        return "Patient is NOT DIABETIC"
-    elif prediction == 1:
-        return "Patient is DIABETIC"
-
-outputs = gr.outputs.Textbox()
-app = gr.Interface(fn=diabetes, 
-                   inputs=[gr.inputs.Slider(0,9,step=1,label= 'How many months pregnant is the patient, 0 if unapplicable'),
-                           'number',
-                           'number',
-                           'number',
-                           'number',
-                           'number',
-                           gr.inputs.Slider(0,1,label= 'Diabetes Pedigree function'),
-                           'number'], 
-                   outputs='text',
-                   theme="grass",
-                   title="Salim's Machine Learning App",
-                   description="This is a diabetes model")
-
-app.launch(share = True)
-