main_app.py

import streamlit as st 
import numpy as np 
import pandas as pd
import seaborn as sns
import matplotlib
import matplotlib.pyplot as plt
#from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn import model_selection
#from sklearn.preprocessing import LabelEncoder
matplotlib.use('Agg')

from PIL import Image


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.sidebar.slider("Adjust Image Width", min_value=1, max_value=image.width, value=image.width)
new_height = st.sidebar.slider("Adjust Image 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)



def main():
	activities=['EDA','Visualisation','model','About us']
	option=st.sidebar.selectbox('Selection option:',activities)

	

#DEALING WITH THE EDA PART

	if option=='EDA':
		st.subheader("Exploratory Data Analysis")

		data=st.file_uploader("Upload dataset:",type=['csv','xlsx','txt','json'])
		st.success("Data successfully loaded")
		if data is not None:
			df=pd.read_csv(data)
			st.dataframe(df.head(50))

			if st.checkbox("Display shape"):
				st.write(df.shape)
			if st.checkbox("Display columns"):
				st.write(df.columns)
			if st.checkbox("Select multiple columns"):
				selected_columns=st.multiselect('Select preferred columns:',df.columns)
				df1=df[selected_columns]
				st.dataframe(df1)

			if st.checkbox("Display summary"):
				st.write(df1.describe().T)

			if st.checkbox('Display Null Values'):
				st.write(df1.isnull().sum())

			if st.checkbox("Display the data types"):
				st.write(df1.dtypes)
			if st.checkbox('Display Correlation of data variuos columns'):
				st.write(df1.corr())




#DEALING WITH THE VISUALISATION PART


	elif option=='Visualisation':
		st.subheader("Data Visualisation")

		data=st.file_uploader("Upload dataset:",type=['csv','xlsx','txt','json'])
		st.success("Data successfully loaded")
		if data is not None:
			df=pd.read_csv(data)
			st.dataframe(df.head(50))

			if st.checkbox('Select Multiple columns to plot'):
				selected_columns=st.multiselect('Select your preferred columns',df.columns)
				df1=df[selected_columns]
				st.dataframe(df1)

			if st.checkbox('Display Heatmap'):
				st.write(sns.heatmap(df1.corr(),vmax=1,square=True,annot=True,cmap='viridis'))
				st.pyplot()
				st.set_option('deprecation.showPyplotGlobalUse', False)
			if st.checkbox('Display Pairplot'):
				st.write(sns.pairplot(df1,diag_kind='kde'))
				st.pyplot()
				st.set_option('deprecation.showPyplotGlobalUse', False)
			if st.checkbox('Display Pie Chart'):
				all_columns=df.columns.to_list()
				pie_columns=st.selectbox("select column to display",all_columns)
				pieChart=df[pie_columns].value_counts().plot.pie(autopct="%1.1f%%")
				st.write(pieChart)
				st.pyplot()





	# DEALING WITH THE MODEL BUILDING PART

	elif option=='model':
		st.subheader("Model Building")

		data=st.file_uploader("Upload dataset:",type=['csv','xlsx','txt','json'])
		st.success("Data successfully loaded")
		if data is not None:
			df=pd.read_csv(data)
			st.dataframe(df.head(5))

			if st.checkbox('Select Multiple columns'):
				new_data=st.multiselect("Select your preferred columns. NB: Let your target variable be the last column to be selected",df.columns)
				df1=df[new_data]
				st.dataframe(df1)


				#Dividing my data into X and y variables

				X=df1.iloc[:,0:-1]
				y=df1.iloc[:,-1]


			seed=st.sidebar.slider('Seed',1,200)

			classifier_name=st.sidebar.selectbox('Select your preferred classifier:',('KNN','SVM','LR','naive_bayes','decision tree'))


			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


			#calling the function
			params=add_parameter(classifier_name)



			#defing a function for 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'])
				elif name_of_clf=='LR':
					clf=LogisticRegression()
				elif name_of_clf=='naive_bayes':
					clf=GaussianNB()
				elif name_of_clf=='decision tree':
					clf=DecisionTreeClassifier()
				else:
					st.warning('Select your choice of algorithm')

				return clf


			#calling the function
			clf=get_classifier(classifier_name,params)


			X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2, random_state=seed)

			clf.fit(X_train,y_train)

			y_pred=clf.predict(X_test)
			st.write('Predictions:',y_pred)

			accuracy=accuracy_score(y_test,y_pred)

			st.write('Name of classifier:',classifier_name)
			st.write('Accuracy',accuracy)




#DELING WITH THE ABOUT US PAGE



	elif option=='About us':

		st.markdown('This is an interactive web page ML project. Datasets are fetched from the UCI Machine learning repository. The analysis in here is to demonstrate how we can present work to stakeholders in an interractive way by building a web app for machine learning algorithms using different dataset.'
			)


		st.balloons()
	# 	..............


if __name__ == '__main__':
	main()