Update app.py

app.py

@@ -20,9 +20,8 @@ iface = gr.Interface(fn=txt2img, inputs=[
     gr.Textbox(label = 'Input Text Prompt'),
     gr.Slider(2, 20, value = 9, label = 'Guidence Scale'),
     gr.Slider(10, 100, value = 50, step = 1, label = 'Number of Iterations'),
-    gr.Slider(.01, 100, value=1)], outputs = 'image',title='Stable Diffusion with Keras and TensorFlow CPU or GPU', description='Now Using Keras and TensorFlow with Stable Diffusion. This allows very complex image generation with less code footprint, and less text. https://keras.io/about/')
-gr.HTML(
-            """
+    gr.Slider(.01, 100, value=1)], outputs = 'image',title='Stable Diffusion with Keras and TensorFlow CPU or GPU', description='Now Using Keras and TensorFlow with Stable Diffusion. This allows very complex image generation with less code footprint, and less text. https://keras.io/about/',
+    gr.HTML("""
                 <div class="footer">
                     <p> Gradio Demo by 🤗 Majushri
                     </p>
@@ -33,7 +32,6 @@ The model is licensed with a <a href="https://huggingface.co/spaces/CompVis/stab
                     <p><h4>Biases and content acknowledgment</h4>
 Despite how impressive being able to turn text into image is, beware to the fact that this model may output content that reinforces or exacerbates societal biases, as well as realistic faces, pornography and violence. The model was trained on the <a href="https://laion.ai/blog/laion-5b/" style="text-decoration: underline;" target="_blank">LAION-5B dataset</a>, which scraped non-curated image-text-pairs from the internet (the exception being the removal of illegal content) and is meant for research purposes. You can read more in the <a href="https://huggingface.co/CompVis/stable-diffusion-v1-4" style="text-decoration: underline;" target="_blank">model card</a></p>
                </div>
-           """
-        )
+           """)
 
 iface.launch()