Create app.py

app.py

@@ -0,0 +1,50 @@
+import os
+import gradio as gr
+
+
+demo = gr.Blocks()
+
+EXAMPLES = ["cantina.wav"]
+
+def speech_to_text(x):
+    return [("yada yada", "speaker 0"), ("blah blah blah", "speaker 1")]
+
+def summarize(y, c):
+    return "> " + len(c)*"stuff"
+
+def sentiment(x, y):
+    if y == 0:
+        return [("yada yada", "happy")]
+    if y == 1:
+        return [("blah blah blah", "sad")]
+
+with demo:
+    with gr.Row():
+        with gr.Column():
+            audio = gr.Audio(label="Audio file", type='filepath')
+            with gr.Row():
+                btn = gr.Button("Transcribe")
+            with gr.Row():
+                examples = gr.components.Dataset(components=[audio], samples=[EXAMPLES], type="index")
+        with gr.Column():
+                gr.Markdown("**Diarized Output:**")
+                diarized = gr.HighlightedText(label="Diarized Output")
+                gr.Markdown("Choose speaker(s) for summarization:")
+                check = gr.CheckboxGroup(["Speaker 0", "Speaker 1"], show_label=False)
+                gr.Textbox("**Summary:**")
+                summary = gr.Markdown()
+                gr.Markdown("Choose speaker for sentiment analysis:")                
+                radio = gr.Radio(["Speaker 0", "Speaker 1"], show_label=False, type="index")
+                analyzed = gr.HighlightedText(label="Customer Sentiment")
+
+    btn.click(speech_to_text, audio, diarized)
+    check.change(summarize, [diarized, check], summary)
+    radio.change(sentiment, [diarized, radio], analyzed)
+
+    def load_example(example_id):
+        processed_examples = audio.preprocess_example(EXAMPLES[example_id])
+        return processed_examples
+
+    examples.click(load_example, inputs=[examples], outputs=[audio], _preprocess=False)
+        
+    demo.launch()