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from bokeh.models import ColumnDataSource, FactorRange, HoverTool
from bokeh.plotting import figure
from bokeh.transform import dodge
from bokeh.io import output_notebook, show
from bokeh.palettes import Category20
from huggingface_hub import HfApi
from huggingface_hub import ModelSearchArguments, DatasetSearchArguments
from huggingface_hub import ModelFilter
import pandas as pd
import gradio as gr
api = HfApi()
filt = ModelFilter(library = "diffusers",)
diffusers_models = api.list_models(filter=filt, sort='downloads', direction=-1)
#len(diffusers_models)
diffusers_dict = {}
downloads, authors, modelids, likes = [], [], [], []
print(len(diffusers_models))
for data in diffusers_models:
#print(data.downloads, data.author, data.modelId, data.likes)
downloads.append(data.downloads)
authors.append(data.author)
modelids.append(data.modelId)
likes.append(data.likes)
diffusers_dict['modelid'] = modelids
diffusers_dict['author'] = authors
diffusers_dict['download'] = downloads
diffusers_dict['likes'] = likes
diffusers_df = pd.DataFrame.from_dict(diffusers_dict)
diffusers_df = diffusers_df[(diffusers_df['download'] != 0) & (diffusers_df['likes'] != 0) ]
grouped = diffusers_df.groupby('author').sum().sort_values(by='download', ascending=False)
#getting data ready for bokeh plots
data_bokeh = grouped.sort_values('download', ascending=False).head(15)
data_bokeh.reset_index(inplace=True)
data_bokeh
#y - axis 1
authors = data_bokeh['author']
#x-axis
downloads = data_bokeh['download']
#y - axis 2
likes = data_bokeh['likes']
# create sample data
data = {'authors': authors,
'downloads': downloads,
'likes': likes}
source = ColumnDataSource(data=data)
def display_df():
df = grouped
return df
def bokehplots():
# set up figure
p = figure(x_range=FactorRange(*authors), plot_height=350, plot_width=600, title='Downloads and Likes by Author')
p.vbar(x=dodge('authors',-0.2, range=p.x_range), top='downloads', width=0.4, source=source,
color=Category20[3][0], legend_label='Downloads')
p.vbar(x=dodge('authors',0.2, range=p.x_range), top='likes', width=0.4, source=source,
color=Category20[3][1], legend_label='Likes')
p.xaxis.major_label_orientation = 45
# set up y-axis for downloads
p.yaxis.axis_label = 'Downloads'
p.yaxis.axis_label_text_color = Category20[3][0]
p.yaxis.major_label_text_color = Category20[3][0]
# set up y-axis for likes
p.extra_y_ranges = {'likes': Range1d(start=0, end=max(likes)+500)}
p.add_layout(LinearAxis(y_range_name='likes', axis_label='Likes',
axis_label_text_color=Category20[3][1], major_label_text_color=Category20[3][1]), 'right')
p.vbar(x=dodge('authors', 0.4, range=p.x_range), top='likes', width=0.5, source=source,
color=Category20[3][1], legend_label='Likes', y_range_name='likes')
# Create a HoverTool object and specify the information to display in the tooltip
hover = HoverTool(tooltips=[('authors', '@authors'), ('downloads', '@downloads'), ('likes', '@likes')])
# Add the HoverTool to the plot
p.add_tools(hover)
# remove grid lines
p.xgrid.grid_line_color = None
p.ygrid.grid_line_color = None
# set legend location
p.legend.location = 'top_right'
return p
with gr.Blocks() as demo:
plot = gr.Plot()
out_dataframe = gr.Dataframe(wrap=True, max_rows=10, overflow_row_behaviour= "paginate", datatype = ["str", "number", "number"], interactive=False)
demo.load(bokehplots, outputs=[plot])
demo.load(fn=display_df, outputs=[out_dataframe])
demo.launch(debug=True) |