| from streamlit_pandas_profiling import st_profile_report | |
| from ydata_profiling import ProfileReport | |
| import streamlit as st | |
| import pandas as pd | |
| import numpy as np | |
| from prediction import predict | |
| from function import filter_dataframe | |
| from sklearn.datasets import load_iris | |
| from ydata_profiling.utils.cache import cache_file | |
| st.set_page_config(layout="wide") | |
| st.title('Iris Flowers - Classification') | |
| st.caption('Created by Bayhaqy') | |
| st.markdown('Classify iris flowers into \ | |
| setosa, versicolor, virginica') | |
| st.image('https://machinelearninghd.com/wp-content/uploads/2021/03/iris-dataset.png') | |
| st.image('https://www.integratedots.com/wp-content/uploads/2019/06/iris_petal-sepal-e1560211020463.png') | |
| st.write("---") | |
| # Load Dataset | |
| def load_data(url): | |
| df = pd.read_csv(url) | |
| return df | |
| iris = cache_file( | |
| 'Iris.csv', | |
| 'https://raw.githubusercontent.com/bayhaqy/Classification-Iris-Prediction/main/Iris.csv', | |
| ) | |
| df = load_data(iris) | |
| if st.checkbox('Open Iris Dataset'): | |
| fd = filter_dataframe(df) | |
| st.dataframe(fd, use_container_width=True) | |
| st.write("---") | |
| if st.checkbox('Open EDA Report'): | |
| pr = ProfileReport(df) | |
| st_profile_report(pr) | |
| st.write("---") | |
| st.header('Plant Features') | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| st.text('Sepal Size') | |
| sepal_l = st.slider('Sepal lenght (cm)', 1.0, 8.0, 0.5) | |
| sepal_w = st.slider('Sepal width (cm)', 2.0, 4.4, 0.5) | |
| with col2: | |
| st.text('Pepal Size') | |
| petal_l = st.slider('Petal lenght (cm)', 1.0, 7.0, 0.5) | |
| petal_w = st.slider('Petal width (cm)', 0.1, 2.5, 0.5) | |
| if st.button('Predict type of Iris'): | |
| result = predict(np.array([[sepal_l, sepal_w, petal_l, petal_w]])) | |
| st.text(result[0]) |