myapp.py

import numpy as np
import pandas as pd
import streamlit as st
from PIL import Image
from sklearn import svm
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

# load the diabetes dataset
diabetes_df = pd.read_csv('diabetes.csv')

# group the data by outcome to get a sense of the distribution
diabetes_mean_df = diabetes_df.groupby('Outcome').mean()

# split the data into input and target variables
X = diabetes_df.drop('Outcome', axis=1)
y = diabetes_df['Outcome']

# scale the input variables using StandardScaler
scaler = StandardScaler()
scaler.fit(X)
X = scaler.transform(X)

# split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=20, random_state=1)

# create an SVM model with a linear kernel
model = svm.SVC(kernel='linear')

# train the model on the training set
model.fit(X_train, y_train)

# make predictions on the training and testing sets
train_y_pred = model.predict(X_train)
test_y_pred = model.predict(X_test)

# calculate the accuracy of the model on the training and testing sets
train_acc = accuracy_score(train_y_pred, y_train)
test_acc = accuracy_score(test_y_pred, y_test)

# create the Streamlit app
def app():

    img = Image.open(r"img.jpeg")
    img = img.resize((200,200))
    st.image(img,caption="Diabetes Image",width=200)


    st.title('Diabetes Prediction')

    # create the input form for the user to input new data
    st.sidebar.title('Input Features')
    preg = st.sidebar.slider('Pregnancies', 0, 17, 3)
    glucose = st.sidebar.slider('Glucose', 0, 199, 117)
    bp = st.sidebar.slider('Blood Pressure', 0, 122, 72)
    skinthickness = st.sidebar.slider('Skin Thickness', 0, 99, 23)
    insulin = st.sidebar.slider('Insulin', 0, 846, 30)
    bmi = st.sidebar.slider('BMI', 0.0, 67.1, 32.0)
    dpf = st.sidebar.slider('Diabetes Pedigree Function', 0.078, 2.42, 0.3725, 0.001)
    age = st.sidebar.slider('Age', 21, 81, 29)

    # make a prediction based on the user input
    input_data = [preg, glucose, bp, skinthickness, insulin, bmi, dpf, age]
    input_data_array = np.asarray(input_data)
    input_data_scaled = scaler.transform(input_data_array.reshape(1, -1))
    prediction = model.predict(input_data_scaled)

    # display the prediction to the user
    st.write('Based on the input features, the model predicts:')
    if prediction[0] == 1:
        st.warning('This person has diabetes.')
    else:
        st.success('This person does not have diabetes.')

    # display some summary statistics about the dataset
    st.header('Dataset Summary')
    st.write(diabetes_df.describe())

    st.header('Distribution by Outcome')
    st.write(diabetes_mean_df)

    # display the model accuracy
    st.header('Model Accuracy')
    st.write(f'Train set accuracy: {train_acc:.2f}')
    st.write(f'Test set accuracy: {test_acc:.2f}')

if __name__ == '__main__':
    app()