Create app.py

app.py

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+# -*- coding: utf-8 -*-
+"""Diabetes.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/15IbzL0ARqBYPhh4fx4KN2rJ62USEmIO2
+
+Importing the Dependencies
+"""
+
+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('/content/diabetes.csv')
+
+pd.read_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)
+
+!pip install gradio
+
+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(1, 20, value=4, label="Pregnancies", info="Choose between 1 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"),
+    ],
+    outputs = "text",
+)
+
+if __name__ == "__main__":
+    demo.launch(share=True)
+