intro2.py

import streamlit as st 
import numpy as np 
import seaborn as sns

import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.model_selection import train_test_split

from sklearn.decomposition import PCA
from sklearn.svm import SVC
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score

from PIL import Image


# #Set title

st.title('Machine Learning Using Streamlit')

image = Image.open("Techy.jfif")
#st.image(image,use_column_width=True)


# Get the new width and height
new_width = st.slider("Select new width", min_value=1, max_value=image.width, value=image.width)
new_height = st.slider("Select new height", min_value=1, max_value=image.height, value=image.height)

# Reshape the image to the new dimensions
resized_image = image.resize((new_width, new_height))

# Display the resized image
st.image(resized_image, caption=f"Resized Image ({new_width}x{new_height})", use_column_width=True)



#set subtitle

st.write("""
    # A simple Data App With Streamlit
    """)

st.write("""
 ### Let's Explore different classifiers and datasets
""")

dataset_name=st.sidebar.selectbox('Select dataset',('Breast Cancer','Iris','Wine'))

classifier_name=st.sidebar.selectbox('Select Classifier',('SVM','KNN'))


def get_dataset(name):
    data=None
    if name=='Iris':
        data=datasets.load_iris()
    elif name=='Wine':
        data=datasets.load_wine()
    else:
        data=datasets.load_breast_cancer()
    x=data.data
    y=data.target

    return x,y


x,y=get_dataset(dataset_name)
st.dataframe(x)
st.write('Shape of your dataset is:',x.shape)
st.write('Unique target variables:', len(np.unique(y)))


fig=plt.figure()
sns.boxplot(data=x, orient='h')
st.pyplot()


plt.hist(x)
st.pyplot()

st.set_option('deprecation.showPyplotGlobalUse', False)



#BUILDING OUR ALGORITHM

def add_parameter(name_of_clf):
    params=dict()
    if name_of_clf=='SVM':
        C=st.sidebar.slider('C',0.01,15.0)
        params['C']=C
    else:
        name_of_clf='KNN'
        k=st.sidebar.slider('k',1,15)
        params['k']=k
    return params

params=add_parameter(classifier_name)



#Accessing our classifier

def get_classifier(name_of_clf,params):
    clf=None
    if name_of_clf=='SVM':
        clf=SVC(C=params['C'])
    elif name_of_clf=='KNN':
        clf=KNeighborsClassifier(n_neighbors=params['k'])
    else:
        st.warning("you didn't select any option, please select at least one algo")
    return clf 


clf=get_classifier(classifier_name,params)


# Split data
x_train,x_test,y_train,y_test=train_test_split(x,y, test_size=0.2, random_state=10)

#Fit data
clf.fit(x_train,y_train) #80% for training

#Predict
y_pred=clf.predict(x_test) #20% for testing

st.write(y_pred)

#Test score
accuracy=accuracy_score(y_test,y_pred)

st.write('classifier_name:',classifier_name)
st.write('Acccuray for your model is:',accuracy)