Update app.py

app.py

@@ -63,25 +63,6 @@ css = """
             --tw-ring-color: rgb(191 219 254 / var(--tw-ring-opacity));
             --tw-ring-opacity: .5;
         }
-        .footer {
-            margin-bottom: 45px;
-            margin-top: 35px;
-            text-align: center;
-            border-bottom: 1px solid #e5e5e5;
-        }
-        .footer>p {
-            font-size: .8rem;
-            display: inline-block;
-            padding: 0 10px;
-            transform: translateY(10px);
-            background: white;
-        }
-        .dark .footer {
-            border-color: #303030;
-        }
-        .dark .footer>p {
-            background: #0b0f19;
-        }
         .prompt h4{
             margin: 1.25em 0 .25em 0;
             font-weight: bold;
@@ -159,13 +140,8 @@ with block:
                   <rect x="23" y="69" width="23" height="23" fill="black"></rect>
                 </svg>
                 <h1 style="font-weight: 900; margin-bottom: 7px;">
-                  Whisper
                 </h1>
               </div>
-              <p style="margin-bottom: 10px; font-size: 94%">
-                Whisper is a general-purpose speech recognition model. It is trained on a large dataset of diverse audio and is also a multi-task model that can perform multilingual speech recognition as well as speech translation and language identification. This demo cuts audio after around 30 secs.
-              </p>
-               <p>You can skip the queue by using google colab for the space: <a href="https://colab.research.google.com/drive/1WJ98KHgZxFGrHiMm4TyWZllSew_Af_ff?usp=sharing"><img data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" src="https://camo.githubusercontent.com/84f0493939e0c4de4e6dbe113251b4bfb5353e57134ffd9fcab6b8714514d4d1/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667"></a></p>
             </div>
         """
     )
@@ -182,21 +158,13 @@ with block:
                 btn = gr.Button("Transcribe")
         text = gr.Textbox(show_label=False, elem_id="result-textarea")
         with gr.Group(elem_id="share-btn-container"):
-            community_icon = gr.HTML(community_icon_html, visible=False)
             loading_icon = gr.HTML(loading_icon_html, visible=False)
-            share_button = gr.Button("Share to community", elem_id="share-btn", visible=False)
+
         
 
 
         
-        btn.click(inference, inputs=[audio], outputs=[text, community_icon, loading_icon, share_button])
-        share_button.click(None, [], [], _js=share_js)
- 
-        gr.HTML('''
-        <div class="footer">
-                    <p>Model by <a href="https://github.com/openai/whisper" style="text-decoration: underline;" target="_blank">OpenAI</a> - Gradio Demo by 🤗 Hugging Face
-                    </p>
-        </div>
-        ''')
+        btn.click(inference, inputs=[audio], outputs=[text, loading_icon])
+
 
 block.launch()