Create app.py

app.py

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+import gradio as gr
+from PIL import Image
+
+
+def infer_segmentation(prompt, negative_prompt, image):
+    # implement your inference function here
+    im = Image.open("cat_image.jpeg")
+    return im
+
+def infer_canny(prompt, negative_prompt, image):
+    # implement your inference function here
+    im = Image.open("cat_image.jpeg")
+    return im
+
+with gr.Blocks() as demo:
+    gr.Markdown("## Stable Diffusion with Different Controls")
+    gr.Markdown("In this app, you can find different ControlNets with different filters. ")
+
+    input_list = [prompt_input, negative_prompt_input, image_input]
+    with gr.Tab("ControlNet on Canny Filter "):
+        prompt_input = gr.Textbox(label="Prompt")
+        negative_prompt_input = gr.Textbox(label="Negative Prompt")
+        image_input = gr.Image(label="Image")
+        submit_btn = gr.Button("Submit")
+        submit_btn.click(fn=write_to_dataset, inputs=input_list, outputs="image")
+    with gr.Tab("ControlNet with Semantic Segmentation"):
+        prompt_input = gr.Textbox(label="Prompt")
+        negative_prompt_input = gr.Textbox(label="Negative Prompt")
+        image_input = gr.Image(label="Image")
+        submit_btn = gr.Button("Submit")
+        submit_btn.click(fn=write_to_dataset, inputs=input_list, outputs="image")
+
+demo.launch()