Update app.py

app.py

@@ -182,88 +182,85 @@ def inference(audio, sentiment_option):
     return lang.upper(), result.text, sentiment_output, prediction[0], prediction[1]
 
 
-def main():
-    title = """<h1 align="center">🎤 Multilingual ASR 💬</h1>"""
-    description = """
-    💻 This demo showcases a general-purpose speech recognition model called Whisper. It is trained on a large dataset of diverse audio and supports multilingual speech recognition, speech translation, and language identification tasks.<br><br>
-    <br>
-    ⚙️ Components of the tool:<br>
-    <br>
-    &nbsp;&nbsp;&nbsp;&nbsp; - Real-time multilingual speech recognition<br>
-    &nbsp;&nbsp;&nbsp;&nbsp; - Language identification<br>
-    &nbsp;&nbsp;&nbsp;&nbsp; - Sentiment analysis of the transcriptions<br>
-    <br>
-    🎯 The sentiment analysis results are provided as a dictionary with different emotions and their corresponding scores.<br>
-    <br>
-    😃 The sentiment analysis results are displayed with emojis representing the corresponding sentiment.<br>
-    <br>
-    ✅ The higher the score for a specific emotion, the stronger the presence of that emotion in the transcribed text.<br>
-    <br>
-    ❓ Use the microphone for real-time speech recognition.<br>
-    <br>
-    ⚡️ The model will transcribe the audio and perform sentiment analysis on the transcribed text.<br>
-    """
-
-    custom_css = """
-    #banner-image {
-        display: block;
-        margin-left: auto;
-        margin-right: auto;
-    }
-    #chat-message {
-        font-size: 14px;
-        min-height: 300px;
-    }
-    """
-
-    block = gr.Blocks(css=custom_css)
-
-    with block:
-        gr.HTML(title)
-
-        with gr.Row():
-            with gr.Column():
-                gr.HTML(description)
-
-        with gr.Group():
-            with gr.Box():
-
-                audio = gr.Audio(
-                    label="Input Audio",
-                    show_label=False,
-                    source="microphone",
-                    type="filepath"
-                )
-    
-                sentiment_option = gr.Radio(
-                    choices=["Sentiment Only", "Sentiment + Score"],
-                    label="Select an option",
-                    default="Sentiment Only"
-                )
-
-            btn = gr.Button("Transcribe")
-
-            lang_str = gr.Textbox(label="Language")
-
-            text = gr.Textbox(label="Transcription")
-
-            sentiment_output = gr.Textbox(label="Sentiment Analysis Results", output=True)
-
-            prediction = gr.Textbox(label="Prediction")
-            
-            language_translation = gr.Textbox(label="Language Translation")
-            
-
-            btn.click(inference, inputs=[audio, sentiment_option], outputs=[lang_str, text, sentiment_output, prediction,language_translation])
-
-            # gr.HTML('''
-            # <div class="footer">
-            #     <p>Model by <a href="https://github.com/openai/whisper" style="text-decoration: underline;" target="_blank">OpenAI</a>
-            #     </p>
-            # </div>
-            # ''') 
-
-    block.launch()
-
 
-main()
+title = """<h1 align="center">🎤 Multilingual ASR 💬</h1>"""
+description = """
+💻 This demo showcases a general-purpose speech recognition model called Whisper. It is trained on a large dataset of diverse audio and supports multilingual speech recognition, speech translation, and language identification tasks.<br><br>
+<br>
+⚙️ Components of the tool:<br>
+<br>
+&nbsp;&nbsp;&nbsp;&nbsp; - Real-time multilingual speech recognition<br>
+&nbsp;&nbsp;&nbsp;&nbsp; - Language identification<br>
+&nbsp;&nbsp;&nbsp;&nbsp; - Sentiment analysis of the transcriptions<br>
+<br>
+🎯 The sentiment analysis results are provided as a dictionary with different emotions and their corresponding scores.<br>
+<br>
+😃 The sentiment analysis results are displayed with emojis representing the corresponding sentiment.<br>
+<br>
+✅ The higher the score for a specific emotion, the stronger the presence of that emotion in the transcribed text.<br>
+<br>
+❓ Use the microphone for real-time speech recognition.<br>
+<br>
+⚡️ The model will transcribe the audio and perform sentiment analysis on the transcribed text.<br>
+"""
+
+custom_css = """
+#banner-image {
+    display: block;
+    margin-left: auto;
+    margin-right: auto;
+}
+#chat-message {
+    font-size: 14px;
+    min-height: 300px;
+}
+"""
+
+block = gr.Blocks(css=custom_css)
+
+with block:
+    gr.HTML(title)
+
+    with gr.Row():
+        with gr.Column():
+            gr.HTML(description)
+
+    with gr.Group():
+        with gr.Box():
+
+            audio = gr.Audio(
+                label="Input Audio",
+                show_label=False,
+                source="microphone",
+                type="filepath"
+            )
+
+            sentiment_option = gr.Radio(
+                choices=["Sentiment Only", "Sentiment + Score"],
+                label="Select an option",
+                default="Sentiment Only"
+            )
+
+        btn = gr.Button("Transcribe")
+
+        lang_str = gr.Textbox(label="Language")
+
+        text = gr.Textbox(label="Transcription")
+
+        sentiment_output = gr.Textbox(label="Sentiment Analysis Results", output=True)
+
+        prediction = gr.Textbox(label="Prediction")
+        
+        language_translation = gr.Textbox(label="Language Translation")
+        
+
+        btn.click(inference, inputs=[audio, sentiment_option], outputs=[lang_str, text, sentiment_output, prediction,language_translation])
+
+        # gr.HTML('''
+        # <div class="footer">
+        #     <p>Model by <a href="https://github.com/openai/whisper" style="text-decoration: underline;" target="_blank">OpenAI</a>
+        #     </p>
+        # </div>
+        # ''') 
+
+block.launch()