add info

app.py

@@ -4,13 +4,10 @@ import gradio as gr
 from PIL import Image
 from cli import iterative_refinement
 from viz import grid_of_images_default
-# from subprocess
-# subprocess.call("download_models.sh", shell=True)
 models = {
     "convae": torch.load("convae.th", map_location="cpu"),
     "deep_convae": torch.load("deep_convae.th", map_location="cpu"),
 }
-
 def gen(model, seed, nb_iter, nb_samples, width, height):
     torch.manual_seed(int(seed))
     bs = 64
@@ -26,9 +23,17 @@ def gen(model, seed, nb_iter, nb_samples, width, height):
     grid = (grid*255).astype("uint8")
     return Image.fromarray(grid)
 
+text = """
+Interface with ConvAE model (from [here](https://arxiv.org/pdf/1606.04345.pdf)) and DeepConvAE model (from [here](https://tel.archives-ouvertes.fr/tel-01838272/file/75406_CHERTI_2018_diffusion.pdf), Section 10.1 with `L=3`)
+
+These models were trained on MNIST only (digits), but were found to generate new kinds of symbols, see the references for more details.
+"""
 iface = gr.Interface(
     fn=gen,
-    inputs=[gr.Dropdown(list(models.keys()), value="deep_convae"), gr.Number(value=0), gr.Number(value=20), gr.Number(value=1), gr.Number(value=28), gr.Number(value=28)],
+    inputs=[
+        gr.Markdown(text),
+        gr.Dropdown(list(models.keys()), value="deep_convae"), gr.Number(value=0), gr.Number(value=20), gr.Number(value=1), gr.Number(value=28), gr.Number(value=28)
+    ],
     outputs="image"
 )
 iface.launch()