create app.py

app.py

@@ -0,0 +1,104 @@
+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
+
+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, inputs=[], outputs=[plot])
+    demo.load(fn=display_df, outputs=out_dataframe)  
+
+demo.launch(debug=True)