another example

app.py

@@ -1,18 +1,28 @@
 import gradio as gr
+import numpy as np
 
-from diffusers import AutoPipelineForText2Image
-import torch
+def flip_text(text):
+    return text[::-1]
 
-pipeline = AutoPipelineForText2Image.from_pretrained(
-    "runwayml/stable-diffusion-v1-5"
-)
-prompt = "peasant and dragon combat, wood cutting style, viking era, bevel with rune"
+def flip_img(img):
+    return np.flipud(img)
 
-image = pipeline(prompt, num_inference_steps=25).images[0]
+with gr.Blocks() as demo:
+    gr.Markdown("flip the text or image using this demo")
+    with gr.Tab("Flip Text"):
+        text_input = gr.Textbox();
+        text_output = gr.Textbox();
+        text_btn = gr.Button("Flip");
+    with gr.Tab("Flip Img"):
+        with gr.Row():
+            image_input = gr.Image(source="webcam");
+            image_outpt = gr.Image();
+        image_btn = gr.Button("Flip");
+    with gr.Accordion("Open for more"):
+        gr.Markdown("Look at me");
+    
+    text_btn.click(flip_text, inputs=text_input, outputs=text_output)
+    image_btn.click(flip_img, inputs=image_input, outputs=image_outpt)
 
-def greet(name):
-    return "Hello " + name + "!!"
-
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
 
+demo.launch()