app.py

import streamlit as st
from streamlit_drawable_canvas import st_canvas
from huggingface_hub import HfFileSystem
from flax.serialization import msgpack_restore, from_state_dict

fs = HfFileSystem()

with fs.open("PrakhAI/HelloWorld/checkpoint.msgpack", "rb") as f:
    state = msgpack_restore(f.read())

print(type(state))
print(state)
print([(k, type(v)) for (k, v) in state.items()])
# print(state['params'])
# print(state['opt_state'])

# logits = state.apply_fn({'params': state.params}, batch['image'])
# print(logits)

# x = st.slider('Select a value')
# st.write(x, 'squared is', x * x)

# Specify canvas parameters in application
drawing_mode = st.sidebar.selectbox(
    "Drawing tool:",
    ("freedraw", "line", "rect", "circle", "transform", "polygon", "point"),
)
stroke_width = st.sidebar.slider("Stroke width: ", 1, 25, 3)
if drawing_mode == "point":
    point_display_radius = st.sidebar.slider("Point display radius: ", 1, 25, 3)
stroke_color = st.sidebar.color_picker("Stroke color hex: ")
bg_color = st.sidebar.color_picker("Background color hex: ", "#eee")
bg_image = st.sidebar.file_uploader("Background image:", type=["png", "jpg"])
realtime_update = st.sidebar.checkbox("Update in realtime", True)

# Create a canvas component
canvas_result = st_canvas(
    fill_color="rgba(255, 165, 0, 0.3)",  # Fixed fill color with some opacity
    stroke_width=stroke_width,
    stroke_color=stroke_color,
    background_color=bg_color,
    background_image=Image.open(bg_image) if bg_image else None,
    update_streamlit=realtime_update,
    height=150,
    drawing_mode=drawing_mode,
    point_display_radius=point_display_radius if drawing_mode == "point" else 0,
    display_toolbar=st.sidebar.checkbox("Display toolbar", True),
    key="full_app",
)

# Do something interesting with the image data and paths
if canvas_result.image_data is not None:
    st.image(canvas_result.image_data)
if canvas_result.json_data is not None:
    objects = pd.json_normalize(canvas_result.json_data["objects"])
    for col in objects.select_dtypes(include=["object"]).columns:
        objects[col] = objects[col].astype("str")
    st.dataframe(objects)