.

app.py

@@ -0,0 +1,30 @@
+import gradio as gr
+import numpy as np
+import requests
+import base64
+import os
+
+API_ENDPOINT = os.getenv('API_ENDPOINT')
+API_KEY = os.getenv('API_KEY')
+
+# setup
+gallery = gr.Gallery(label="Rendered Image", show_label=False, elem_id="gallery").style(grid=[1], height="auto")
+
+# infer
+def infer(latex): 
+    formula = latex
+    data = {'formula': formula, 'api_key': API_KEY}
+    with requests.post(url=API_ENDPOINT, data=data, timeout=600, stream=True) as r:
+        i = 0
+        for line in r.iter_lines():
+            response = line.decode('ascii').strip()
+            r = base64.decodebytes(response.encode('ascii'))
+            q = np.frombuffer(r, dtype=np.float32).reshape((64, 320))
+            i += 1
+            yield i, [q,]
+    
+title = "Markup-to-Image Diffusion Models with Scheduled Sampling"
+description="Yuntian Deng, Noriyuki Kojima, Alexander M. Rush"
+
+# launch
+gr.Interface(fn=infer, inputs=["text"], outputs=[gr.Slider(0, 1000, value=0, label='step (out of 1000)'), gallery],title=title,description=description).queue(max_size=100).launch(enable_queue=True)