File size: 1,585 Bytes
758d5fe 32b93c1 758d5fe 32b93c1 758d5fe 32b93c1 758d5fe 32b93c1 758d5fe 32b93c1 758d5fe 910f34c 758d5fe |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 |
import streamlit as st
from bokeh.plotting import figure
from bokeh.models import ColumnDataSource, HoverTool
from datasets import load_dataset
from bokeh.models import ColumnDataSource, LinearColorMapper, ColorBar
from bokeh.transform import linear_cmap
from bokeh.palettes import Viridis256 # You can choose any palette you like and reverse it using [::-1]
from bokeh.models import BasicTicker
# Load the dataset
dataset = load_dataset("tonyassi/lucy6-embeddings-xy")['train']
# Extract data
data = {
'x': [item['x'] for item in dataset],
'y': [item['y'] for item in dataset],
'label': [f"ID: {item['id']}" for item in dataset],
'image': [item['image_url'] for item in dataset],
'id': [item['id'] for item in dataset] # Include 'id' for color mapping
}
source = ColumnDataSource(data=data)
# Create a color mapper with reversed palette
color_mapper = linear_cmap(field_name='id', palette=Viridis256[::-1], low=0, high=100)
# Create the figure
p = figure(title="Scatter Plot with Image Hover", tools="hover", tooltips="""
<div>
<div><strong>@label</strong></div>
<div><img src="@image" ></div>
</div>
""")
p.circle('x', 'y', size=10, source=source, color=color_mapper) # Apply the color mapper
# Add color bar
color_bar = ColorBar(color_mapper=color_mapper['transform'], width=8, location=(0, 0),
ticker=BasicTicker(desired_num_ticks=10))
p.add_layout(color_bar, 'right') # Position the color bar to the right
st.markdown("""
hello
hi
tony
""")
# Display the Bokeh figure in Streamlit
st.bokeh_chart(p)
|