# -*- coding: utf-8 -*-
"""Diabetes.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/15IbzL0ARqBYPhh4fx4KN2rJ62USEmIO2

Importing the Dependencies
"""
#pip install -U scikit-learn

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

"""Data Collection and Analysis

PIMA Diabetes Dataset
"""

# loading the diabetes dataset to a pandas DataFrame
diabetes_dataset = pd.read_csv('diabetes.csv')


# printing the first 5 rows of the dataset
diabetes_dataset.head()

# number of rows and Columns in this dataset
diabetes_dataset.shape

# getting the statistical measures of the data
diabetes_dataset.describe()

diabetes_dataset['Outcome'].value_counts()

"""0 --> Non-Diabetic

1 --> Diabetic
"""

diabetes_dataset.groupby('Outcome').mean()

# separating the data and labels
X = diabetes_dataset.drop(columns = 'Outcome', axis=1)
Y = diabetes_dataset['Outcome']

print(X)

print(Y)

"""Data Standardization"""

scaler = StandardScaler()

scaler.fit(X)

standardized_data = scaler.transform(X)

print(standardized_data)

X = standardized_data
Y = diabetes_dataset['Outcome']

print(X)
print(Y)

"""Train Test Split"""

X_train, X_test, Y_train, Y_test = train_test_split(X,Y, test_size = 0.2, stratify=Y, random_state=2)

print(X.shape, X_train.shape, X_test.shape)

"""Training the Model"""

classifier = svm.SVC(kernel='linear')

#training the support vector Machine Classifier
classifier.fit(X_train, Y_train)

"""Model Evaluation

Accuracy Score
"""

# accuracy score on the training data
X_train_prediction = classifier.predict(X_train)
training_data_accuracy = accuracy_score(X_train_prediction, Y_train)

print('Accuracy score of the training data : ', training_data_accuracy)

# accuracy score on the test data
X_test_prediction = classifier.predict(X_test)
test_data_accuracy = accuracy_score(X_test_prediction, Y_test)

print('Accuracy score of the test data : ', test_data_accuracy)

"""Making a Predictive System"""

def predict(Pregnancies,Glucose,BloodPressure,SkinThickness,Insulin,BMI,DiabetesPedigreeFunction,Age):
  #input_data = (5,166,72,19,175,25.8,0.587,51)
  input_data = (Pregnancies,Glucose,BloodPressure,SkinThickness,Insulin,BMI,DiabetesPedigreeFunction,Age)


  # changing the input_data to numpy array
  input_data_as_numpy_array = np.asarray(input_data)

  # reshape the array as we are predicting for one instance
  input_data_reshaped = input_data_as_numpy_array.reshape(1,-1)

  # standardize the input data
  std_data = scaler.transform(input_data_reshaped)
  print(std_data)

  prediction = classifier.predict(std_data)
  #print(prediction)

  if (prediction[0] == 0):
    print('The person is not diabetic')
  else:
    print('The person is diabetic')
  return prediction

predict(4,136,64,20,175,25.6,0.597,50)


import gradio as gr


def dibetis_predict(Pregnancies,Glucose,BloodPressure,SkinThickness,Insulin,BMI,DiabetesPedigreeFunction,Age):
  #input_data = (5,166,72,19,175,25.8,0.587,51)
  input_data = (Pregnancies,Glucose,BloodPressure,SkinThickness,Insulin,BMI,DiabetesPedigreeFunction,Age)


  # changing the input_data to numpy array
  input_data_as_numpy_array = np.asarray(input_data)

  # reshape the array as we are predicting for one instance
  input_data_reshaped = input_data_as_numpy_array.reshape(1,-1)

  # standardize the input data
  std_data = scaler.transform(input_data_reshaped)
  print(std_data)

  prediction = classifier.predict(std_data)

  if (prediction[0] == 0):
    print('The person is not diabetic')
    return 'The person is not diabetic'
  else:
    print('The person is diabetic')
    return 'The person is diabetic'




demo = gr.Interface(
    fn=dibetis_predict,
    
    inputs = [
        gr.Slider(0, 20, value=4, label="Pregnancies", info="Choose between 0 and 20"),
        gr.Slider(1, 200, value=136, label="Glucose", info="Choose between 1 and 200"),
        gr.Slider(1, 100, value=64, label="BloodPressure", info="Choose between 1 and 100"),
        gr.Slider(1, 50, value=20, label="SkinThickness", info="Choose between 1 and 50"),
        gr.Slider(1, 200, value=175, label="Insulin", info="Choose between 1 and 200"),
        gr.Slider(1, 100, value=25.5, label="BMI", info="Choose between 1 and 100"),
        gr.Slider(0, 1.0, value=0.549, label="DiabetesPedigreeFunction", info="Choose between 0.0 and 1.0"),
        gr.Slider(1, 100, value=50, label="Age", info="Choose between 1 and 100"),
    ],
    #description="Diabetes Prediction Model By Yash Rawal"
    #Markdown("""Dibetese prediction system by Yash Rawal""")
    outputs = "text",
)

if __name__ == "__main__":
    demo.launch()