Created
June 11, 2020 04:45
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Data visualization tool using Streamlit
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import pathlib | |
from typing import List | |
from PIL import Image | |
import streamlit as st | |
from tensorflow import keras | |
@st.cache | |
def download() -> str: | |
data_dir = keras.utils.get_file( | |
origin="https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz", | |
fname="flower_photos", | |
untar=True, | |
) | |
return data_dir | |
class FlowerDataset: | |
def __init__(self): | |
self.data_dir = pathlib.Path(download()) | |
self.labels = ["sunflowers", "daisy", "roses", "tulips", "dandelion"] | |
def select(self, label: str) -> List[pathlib.Path]: | |
return list(self.data_dir.glob(f"{label}/*")) | |
def main(): | |
st.markdown("# Data visualization tool using Streamlit") | |
dataset = FlowerDataset() | |
selector = st.sidebar.selectbox("Select your favorite flower", dataset.labels) | |
selected_data = dataset.select(selector) | |
index = st.sidebar.number_input( | |
f"Select index from 0 to {len(selected_data)}", | |
min_value=0, | |
max_value=len(selected_data), | |
value=0, | |
step=1, | |
) | |
sample_path = selected_data[index] | |
image = Image.open(sample_path) | |
expand = st.sidebar.checkbox("Expand") | |
degree = st.sidebar.slider("Degree", min_value=0, max_value=180, step=1) | |
st.image(image.rotate(degree, expand=expand)) | |
if __name__ == "__main__": | |
main() |
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How to use
app.py
References
License
MIT