migrate to blocks from interface

app.py

@@ -1,23 +1,12 @@
 import os
-os.system("pip install gradio==3.3")
 import gradio as gr
 import numpy as np
-import streamlit as st
 
-title = "Hokkien Translation"
 
-description = "Gradio Demo for fairseq speech-to-speech translation models. We have S2UT and UnitY models for bidirectional Hokkien and English translation. Please select the model and record your input to try it."
-
-article = "<p style='text-align: center'><a href='https://ai.facebook.com/blog/ai-translation-hokkien/' target='_blank'>Speech-to-speech translation for a real-world unwritten language</a> | <a href='https://github.com/facebookresearch/fairseq/tree/ust/examples/hokkien' target='_blank'>Github Repo</a></p>"
-
-examples = [
-  ["enhanced_direct_s2st_units_audios_es-en_set2_source_12478_cv.flac","xm_transformer_s2ut_800m-es-en-st-asr-bt_h1_2022"],
-]
-
-io1 = gr.Interface.load("huggingface/facebook/xm_transformer_s2ut_en-hk", api_key=st.secrets["api_key"])
-io2 = gr.Interface.load("huggingface/facebook/xm_transformer_s2ut_hk-en", api_key=st.secrets["api_key"])
-io3 = gr.Interface.load("huggingface/facebook/xm_transformer_unity_en-hk", api_key=st.secrets["api_key"])
-io4 = gr.Interface.load("huggingface/facebook/xm_transformer_unity_hk-en", api_key=st.secrets["api_key"])
+io1 = gr.Interface.load("huggingface/facebook/xm_transformer_s2ut_en-hk")
+io2 = gr.Interface.load("huggingface/facebook/xm_transformer_s2ut_hk-en")
+io3 = gr.Interface.load("huggingface/facebook/xm_transformer_unity_en-hk")
+io4 = gr.Interface.load("huggingface/facebook/xm_transformer_unity_hk-en")
    
 def inference(audio, model):
     if model == "xm_transformer_s2ut_en-hk":
@@ -30,12 +19,124 @@ def inference(audio, model):
         out_audio = io4(audio)
     return out_audio 
 
+    
+css = """
+        .gradio-container {
+            font-family: 'IBM Plex Sans', sans-serif;
+        }
+        .gr-button {
+            color: white;
+            border-color: black;
+            background: black;
+        }
+        input[type='range'] {
+            accent-color: black;
+        }
+        .dark input[type='range'] {
+            accent-color: #dfdfdf;
+        }
+        .container {
+            max-width: 730px;
+            margin: auto;
+            padding-top: 1.5rem;
+        }
+     
+        .details:hover {
+            text-decoration: underline;
+        }
+        .gr-button {
+            white-space: nowrap;
+        }
+        .gr-button:focus {
+            border-color: rgb(147 197 253 / var(--tw-border-opacity));
+            outline: none;
+            box-shadow: var(--tw-ring-offset-shadow), var(--tw-ring-shadow), var(--tw-shadow, 0 0 #0000);
+            --tw-border-opacity: 1;
+            --tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color);
+            --tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(3px var(--tw-ring-offset-width)) var(--tw-ring-color);
+            --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;
+            font-size: 115%;
+        }
+        .animate-spin {
+            animation: spin 1s linear infinite;
+        }
+        @keyframes spin {
+            from {
+                transform: rotate(0deg);
+            }
+            to {
+                transform: rotate(360deg);
+            }
+        }
+"""
+
+block = gr.Blocks(css=css)
+
+
+
+with block:
+    gr.HTML(
+        """
+            <div style="text-align: center; max-width: 650px; margin: 0 auto;">
+              <div
+                style="
+                  display: inline-flex;
+                  align-items: center;
+                  gap: 0.8rem;
+                  font-size: 1.75rem;
+                "
+              >
+                <h1 style="font-weight: 900; margin-bottom: 7px;">
+                  Hokkien Translation
+                </h1>
+              </div>
+              <p style="margin-bottom: 10px; font-size: 94%">
+                Gradio Demo for fairseq speech-to-speech translation models. We have S2UT and UnitY models for bidirectional Hokkien and English translation. Please select the model and record your input to try it.
+              </p>
+            </div>
+        """
+    )
+    with gr.Group():
+        with gr.Box():
+            with gr.Row().style(mobile_collapse=False, equal_height=True):
+                audio = gr.Audio(
+                   source="microphone", type="filepath", label="Input"
+                )
+                
+                btn = gr.Button("Submit")
+        model = gr.Dropdown(choices=["xm_transformer_unity_en-hk", "xm_transformer_unity_hk-en", "xm_transformer_s2ut_en-hk", "xm_transformer_s2ut_hk-en"], value="xm_transformer_unity_en-hk",type="value", label="Model")
+        out = gr.Audio(label="Output")
+        
+        btn.click(inference, inputs=[audio, model], outputs=out) 
+        gr.HTML('''
+        <div class="footer">
+                    <p>Model by <a href="https://ai.facebook.com/" style="text-decoration: underline;" target="_blank">Meta AI</a>
+                    </p>
+        </div>
+        ''')
 
-gr.Interface(
-    inference,
-    [gr.inputs.Audio(source="microphone", type="filepath", label="Input"),gr.inputs.Dropdown(choices=["xm_transformer_unity_en-hk", "xm_transformer_unity_hk-en", "xm_transformer_s2ut_en-hk", "xm_transformer_s2ut_hk-en"], default="xm_transformer_unity_en-hk",type="value", label="Model")
-],
-    gr.outputs.Audio(label="Output"),
-    article=article,
-    title=title,
-    description=description).queue().launch()
+block.launch()