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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 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') | |
# Load Dataset | |
#iris = load_iris(as_frame=True) | |
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) | |
# Create a DataFrame from the iris data | |
#df = pd.DataFrame(iris.data, columns=iris.feature_names) | |
# Add a target column to the DataFrame | |
#df['Target'] = iris['target'] | |
# Translate the target | |
#df['Target'] = df['Target'].apply(lambda x: iris['target_names'][x]) | |
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]) | |
st.write("---") | |
if st.checkbox("Sample preview the Iris Dataset"): | |
#st.write(df.sample(10)) # Same as st.write(df) | |
pr = ProfileReport(df,title="Dataset Report") | |
st_profile_report(pr) | |
st.write("---") | |