Update app.py

app.py

@@ -1,4 +1,5 @@
 import gradio as gr
+import transforms
 import requests
 import io
 from PIL import Image
@@ -6,24 +7,12 @@ from transformers import pipeline
 from torchvision import transforms
 
 title = "Fine Tuned SD Model - Authoral stylization"
-description = "Automatically remove the image background from a profile photo."
+description = "Generate images trained in an authoral illustration model."
 article = "<p style='text-align: center'><a href='https://news.machinelearning.sg/posts/beautiful_profile_pics_remove_background_image_with_deeplabv3/'>Blog</a> | <a href='https://github.com/eugenesiow/practical-ml'>Github Repo</a></p>"
 
 
 gr.Interface.load(
-  "spaces/eugenesiow/remove-bg", 
-  
-    inputs=[gr.Image(label="Input Image", source="webcam")]
-).launch()
-
-def greet(name):
-    return "Hello " + name + "!!"
-
-with gr.Blocks(theme=gr.themes.Glass()) as demo:
-    name = gr.Textbox(label="Name")
-    output = gr.Textbox(label="Output Box")
-    greet_btn = gr.Button("Greet")
-    greet_btn.click(fn=greet, inputs=name, outputs=output, api_name="greet")
+  "spaces/Cacau/heart-of-painting",
 
 demo = gr.Interface(
     fn=greet,
@@ -31,4 +20,23 @@ demo = gr.Interface(
     outputs="text",
 )
 
+
+with gr.Blocks(theme=gr.themes.Glass()) as demo:
+   inputs=[gr.Textbox(label="Prompt", source="input box")]
+output=[gr.Ima]
+).launch()
+    
+def query(payload):
+	response = requests.post(API_URL, headers=headers, json=payload)
+	return response.content
+image_bytes = query({
+	"inputs":gr.Textbox(lines=2, placeholder="Your prompt here..."),    
+})
+
+# You can access the image with PIL.Image for example
+import io
+from PIL import Image
+image = Image.open(io.BytesIO(image_bytes))
+
+return "This is your generated image:" + image "**Save it in your files!"
 demo.launch()