Update app.py

app.py

@@ -1,24 +1,35 @@
 import gradio as gr
+from transformers import FlaxAutoModel, AutoTokenizer
 
-# Inference function takes prompt, negative prompt and image
-def infer(prompt, negative_prompt, image):
-    # Implement your inference function here for ControlNet-based Fill Circle
-    output_image = generate_fill_circle_image(prompt, negative_prompt, image) # Replace with your function
+def load_model_and_tokenizer(model_name):
+    model = FlaxAutoModel.from_pretrained(model_name)
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    return model, tokenizer
+
+def generate_image(model, tokenizer, prompt):
+    # Process the input prompt and generate the output image using the model and tokenizer
+    # ...
+    # output_image = ... (your implementation here)
+    return output_image
+
+def infer(prompt):
+    model_name = "Ryukijano/controlnet-fill-circle"
+    model, tokenizer = load_model_and_tokenizer(model_name)
+    output_image = generate_image(model, tokenizer, prompt)
     return output_image
 
-title = "ControlNet on Fill Circle"
-description = "This is a demo on ControlNet based on fill circle."
+iface = gr.Interface(
+    fn=infer,
+    inputs=["text"],
+    outputs="image",
+    title="ControlNet Fill Circle",
+    description="This is a demo of ControlNet Fill Circle.",
+    examples=[
+        ["red circle with blue background"],
+        ["cyan circle with brown floral background"]
+    ],
+    theme="gradio/soft",
+)
 
-# You need to pass your examples according to your inputs
-# Each inner list is one example, each element in the list corresponding to a component in the `inputs`.
-examples = [["red circle with blue background", "cyan circle with brown floral background", None]]
+iface.launch()
 
-gr.Interface(
-    fn = infer,
-    inputs = ["text", "text", "image"],
-    outputs = "image",
-    title = title,
-    description = description,
-    examples = examples,
-    theme='gradio/soft'
-).launch()