Iris_demo_app.py

import streamlit as st
import pandas as pd
from sklearn import datasets
from sklearn.ensemble import RandomForestClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn import metrics
from sklearn.naive_bayes import GaussianNB
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis, QuadraticDiscriminantAnalysis
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.linear_model import LogisticRegression

st.write("""
# Dehao's Iris Flower Prediction App
This app predicts the **Iris flower** type based on user input sepal/petal measurements!
""")

st.sidebar.header('User Input Parameters')

def user_input_features():
    sepal_length = st.sidebar.slider('Sepal length', 4.3, 7.9, 5.4)
    sepal_width = st.sidebar.slider('Sepal width', 2.0, 4.4, 3.4)
    petal_length = st.sidebar.slider('Petal length', 1.0, 6.9, 1.3)
    petal_width = st.sidebar.slider('Petal width', 0.1, 2.5, 0.7)
    data = {'sepal_length': sepal_length,
            'sepal_width': sepal_width,
            'petal_length': petal_length,
            'petal_width': petal_width}
    features = pd.DataFrame(data, index=[0])
    return features

df = user_input_features()

st.subheader('User Input parameters')
st.write(df)

iris = datasets.load_iris()
X = iris.data
Y = iris.target

st.subheader('Class labels and their corresponding index number')
st.write(iris.target_names.reshape(1,3))

models = []
pred = []

models.append(('Decision Tree', DecisionTreeClassifier(max_depth = 3, random_state = 1)))
models.append(('Gaussian Naive Bayes', GaussianNB()))
models.append(('LDA', LinearDiscriminantAnalysis()))
models.append(('QDA', QuadraticDiscriminantAnalysis()))
models.append(('KNN', KNeighborsClassifier(n_neighbors = 3)))
models.append(('Logistic Regression', LogisticRegression(solver = 'newton-cg')))
models.append(('Linear SVC', SVC(kernel='linear')))

for name, model in models:
    pred.append(iris.target_names[model.fit(X,Y).predict(df)])

idx = [models[i][0] for i in range(len(models))]
pred = pd.DataFrame(pred, columns = ['Prediction'], index = idx)

st.subheader('Prediction for each classifier')
st.write(pred)

st.subheader('Prediction from the majority of classifiers')
st.write(pred['Prediction'].value_counts().index[0])