Update app.py

app.py

@@ -1,44 +1,19 @@
 import gradio as gr
-import PIL
 from diffusers import DDPMPipeline, DDIMPipeline, PNDMPipeline
 
 MODEL_ID = "verkaDerkaDerk/tiki-based-128"
-PIPELINE = DDPMPipeline.from_pretrained(MODEL_ID)
+PIPELINE = PNDMPipeline.from_pretrained(MODEL_ID)
 
-def nope():
-    # ValueError: pipeline must be a transformers.Pipeline
-    gr.Interface.from_pipeline(PIPELINE).launch()
-
-def imagine(name):
+#def imagine(name):
+def imagine():
     image = PIPELINE()["sample"][0]
-    print( f"type is {type(image)}" )
-    # type is <class 'PIL.Image.Image'>
-    gr_image = gr.Image( image )
-    print( f"type iz {type(gr_image)}" )
-    # type iz <class 'gradio.components.Image'>
-    if isinstance(image, PIL.Image.Image):
-        print( "of course it is a PIL.Image.Image")
-    else:
-        print( "oddly, it is not a PIL.Image.Image")
     return image
-    
-def greet(name):
-    return f"Sup, {name}?"
 
 def main():
-    #gr.Interface(fn=greet, inputs="text", outputs="text").launch()
-    outputs = "image" # also doesn't work
-    outputs = gr.Image(type="pil")
+    inputs = "text"
+    inputs = None
     outputs = "pil"
-    gr.Interface(fn=imagine, inputs="text", outputs=outputs).launch()
-    # ValueError: Cannot process this value as an Image
-        
-    #demo = gr.Interface(image_mod, gr.Image(type="pil"), "image",
-    #   flagging_options=["blurry", "incorrect", "other"], examples=[
-    #    os.path.join(os.path.dirname(__file__), "images/cheetah1.jpg"),
-    #    os.path.join(os.path.dirname(__file__), "images/lion.jpg"),
-    #   os.path.join(os.path.dirname(__file__), "images/logo.png")
-    #])
+    gr.Interface(fn=imagine, inputs=inputs, outputs=outputs).launch()
 
         
 main()