add test

app.py

@@ -29,7 +29,28 @@ sorted_dict = dict(sorted(downloads.items(), key=lambda item: item[1], reverse=T
 # Convert the sorted dictionary to a DataFrame
 df = pd.DataFrame(list(sorted_dict.items()), columns=['Pipeline class', 'Downloads'])
 
+NOTE = """
+This table shows the **total** number of downloads **per pipeline class** of `diffusers`.
+The pipeline classes are retrieved from the `_class_name` attribute of `model_index.json` or 
+`config.json` depending on whether the diffusers repo is a pipeline repo or a model repo.
+
+**Note**: It's important to excatly understand how downloads are measured here. One should use this graph
+to figure out if a *"type"* of pipeline is used, not a specific pipeline is used. More specifically, we 
+know from this graph that `stable-diffusion` checkpoints are highly used, **but** we don't know exactly which stable diffusion 
+class is highly used.
+
+=> So what conclusions can we draw from this graph?
+- 1.) `stable-diffusion` checkpoints are highly used and account for most downloads. 
+- 2.) All `stable-diffusion` checkpoints are compatible with `StableDiffusionPipeline`, `StableDiffusionImg2ImgPipeline`, `StableDiffusionInpaintPipeline`, `StableDiffusionControlNetPipeline`, `StableDiffusionImg2ImgControlNetPipeline` or `StableDiffusionInpaintControlNetPipeline`, but all downloads contribute only to `StableDiffusionPipeline` here. This means we don't really know which pipeline class is used when the checkpoints are downloaded.
+- 3.) ControlNet is used a lot - it accounts for > 10% of all downloads
+- 4.) If a pipeline class **and** no compatible pipeline class shows up in the graph, we know that the pipeline class is not used a lot. For example:
+    - `VersatileDiffusionPipeline` can only be used with "VersatileDiffusion" checkpoints and so here we no that all VersatileDiffusion classes combined have less than <2k monthly downloads.
+    - `ConsistencyPipeline` can only be used with exactly this pipeline and here we're at < 100 monthly downloads
+- 5.) All LoRA and Textual Inversion downloads are grouped together in "other" for now.
+"""
+
 with gr.Blocks() as demo:
+    gr.Markdown(NOTE)
     gr.DataFrame(df)
 
 demo.launch()