Update

app.py

@@ -2,7 +2,9 @@
 import gradio as gr
 import base64
 import io
+from logzero import logger
 from PIL import Image  # opencv-python
+from random import choice
 
 # from PIL import Image
 # from transformers import AutoTokenizer, AutoModelForSeq2SeqLM,pipeline
@@ -12,9 +14,11 @@ from PIL import Image  # opencv-python
 image_gen = gr.Interface.load("spaces/multimodalart/latentdiffusion")
 
 
-def generate_images(phrase: str):
+def generate_images(phrase: str, steps: int = 125):
+    if not phrase.strip():
+        phrase = choice(["an apple", "a cat", "blue moon", "metaverse"])
     generated_text = phrase
-    steps = 125
+    # steps = 125
     width = 256
     height = 256
     num_images = 4
@@ -27,29 +31,38 @@ def generate_images(phrase: str):
     # generated_images = []
 
     img = None
+    err_msg = phrase
     for image in image_bytes[1]:
         image_str = image[0]
         try:
             image_str = image_str.replace("data:image/png;base64,", "")
         except Exception as exc:
             logger.error(exc)
-            return None
+            err_msg = str(exc)
+            return None, f"Error: {err_msg}. Try again."
         decoded_bytes = base64.decodebytes(bytes(image_str, "utf-8"))
         img = Image.open(io.BytesIO(decoded_bytes))
 
         # generated_images.append(img)
 
     # return generated_images
-    return img
 
+    return img, err_msg
 
-examples = ["an apple", "Donald Trump"]
+
+examples = [["an apple", 125], ["Donald Trump", 125]]
+
+inputs = [
+    # "text",
+    gr.Text(value="a dog with a funny hat"),
+    gr.Slider(minimum=2, maximum=500, value=115, step=5),
+]
 
 iface = gr.Interface(
     generate_images,
-    "text",
-    "image",
+    inputs,
+    ["image", gr.Text(value="", label="phrase")],
     examples=examples,
 )
 
-iface.launch()
+iface.launch(enable_queue=True)