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

dataset=pd.read_csv('diabetes.csv')

# print(dataset.head())

print(dataset.shape)

# print(dataset.describe())

# print(dataset['Outcome'].value_counts())

# print(dataset.groupby('Outcome').mean())

#seperating data and labels

X=dataset.drop(columns='Outcome',axis=1)

Y=dataset["Outcome"]

print(X)

# print(Y)

# standardisation

scaler=StandardScaler()

scaler.fit(X)

std_data=scaler.transform(X)

print(std_data)

X=std_data

# print(X)

# print(Y)

#splitting training and training set

x_train,x_test,y_train,y_test=train_test_split(X,Y,test_size=0.1,stratify=Y,random_state=3)

print(x_train.shape,x_test.shape,y_train.shape,y_test.shape)

#training the model

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

#training the support vector machine classifier

print(classifier.fit(x_train,y_train))

#model evaluation

x_train_accuracy=classifier.predict(x_train)

training_data_accuracy=accuracy_score(x_train_accuracy,y_train)

print("accuracy score of training data:",training_data_accuracy)

x_test_accuracy=classifier.predict(x_test)

test_data_accuracy=accuracy_score(x_test_accuracy,y_test)

print("accuracy score of test data:",test_data_accuracy)

#making a prediction

input_data=(10,100,88,60,110,46.8,0.942,31)

#changing the input data to numpy array

numpy_array=np.asarray(input_data)

#reshape the array

reshaped=numpy_array.reshape(1,-1)

std_data=scaler.transform(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")

# 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

# class DiabetesPredictor:

#     def __init__(self, dataset_path):

#         self.dataset_path = dataset_path

#         self.model = svm.SVC(kernel='linear')

#         self.scaler = StandardScaler()

#     def load_data(self):

#         dataset = pd.read_csv(self.dataset_path)

#         X = dataset.drop(columns='Outcome', axis=1)

#         Y = dataset['Outcome']

#         return X, Y

#     def preprocess_data(self, X):

#         self.scaler.fit(X)

#         return self.scaler.transform(X)

#     def train_model(self, X, Y):

#         X_train, X_test, Y_train, Y_test = train_test_split(

#             X, Y, test_size=0.2, stratify=Y, random_state=2

#         )

#         self.model.fit(X_train, Y_train)

#         # Evaluate the model

#         train_accuracy = accuracy_score(Y_train, self.model.predict(X_train))

#         test_accuracy = accuracy_score(Y_test, self.model.predict(X_test))

#         return train_accuracy, test_accuracy

#     def predict(self, input_data):

#         input_array = np.asarray(input_data).reshape(1, -1)

#         standardized_data = self.scaler.transform(input_array)

#         prediction = self.model.predict(standardized_data)

#         return 'Diabetic' if prediction[0] == 1 else 'Not Diabetic'

# if __name__ == "__main__":

#     # Initialize predictor

#     predictor = DiabetesPredictor('diabetes.csv')

#     # Load and preprocess data

#     X, Y = predictor.load_data()

#     X = predictor.preprocess_data(X)

#     # Train the model

#     train_accuracy, test_accuracy = predictor.train_model(X, Y)

#     print(f"Training Accuracy: {train_accuracy:.2f}")

#     print(f"Testing Accuracy: {test_accuracy:.2f}")

#     # Make a prediction

#     input_data = (5, 166, 72, 19, 175, 25.8, 0.587, 51)

#     result = predictor.predict(input_data)

#     print(f"Prediction for input data: {result}")