Create app.py

app.py

@@ -0,0 +1,76 @@
+import gradio as gr
+from vector_search import HybridVectorSearch
+from whisper_asr import WhisperAutomaticSpeechRecognizer
+
+with gr.Blocks() as demo:
+    with gr.Tab("Live Mode"):
+        full_stream = gr.State()
+        transcript = gr.State(value="")
+        chats = gr.State(value=[])
+
+        with gr.Row(variant="panel"):
+            audio_input = gr.Audio(sources=["microphone"], streaming=True)
+        with gr.Row(variant="panel", equal_height=True):
+            with gr.Column(scale=1):
+                chatbot = gr.Chatbot(
+                    bubble_full_width=True, height="65vh", show_copy_button=True
+                )
+                chat_input = gr.Textbox(
+                    interactive=True, placeholder="Type Search Query...."
+                )
+            with gr.Column(scale=1):
+                transcript_textbox = gr.Textbox(
+                    lines=40,
+                    placeholder="Transcript",
+                    max_lines=40,
+                    label="Transcript",
+                    show_label=True,
+                    autoscroll=True,
+                )
+
+        chat_input.submit(
+            HybridVectorSearch.chat_search, [chat_input, chatbot], [chat_input, chatbot]
+        )
+        audio_input.stream(
+            WhisperAutomaticSpeechRecognizer.transcribe_with_diarization,
+            [audio_input, full_stream, transcript],
+            [transcript_textbox, full_stream, transcript],
+        )
+
+    with gr.Tab("Offline Mode"):
+        full_stream = gr.State()
+        transcript = gr.State(value="")
+        chats = gr.State(value=[])
+
+        with gr.Row(variant="panel"):
+            audio_input = gr.Audio(sources=["upload"], type="filepath")
+        with gr.Row(variant="panel", equal_height=True):
+            with gr.Column(scale=1):
+                chatbot = gr.Chatbot(
+                    bubble_full_width=True, height="55vh", show_copy_button=True
+                )
+                chat_input = gr.Textbox(
+                    interactive=True, placeholder="Type Search Query...."
+                )
+            with gr.Column(scale=1):
+                transcript_textbox = gr.Textbox(
+                    lines=35,
+                    placeholder="Transcripts",
+                    max_lines=35,
+                    label="Transcript",
+                    show_label=True,
+                    autoscroll=True,
+                )
+
+        chat_input.submit(
+            HybridVectorSearch.chat_search, [chat_input, chatbot], [chat_input, chatbot]
+        )
+        audio_input.upload(
+            WhisperAutomaticSpeechRecognizer.transcribe_with_diarization_file,
+            [audio_input],
+            [transcript_textbox, full_stream, transcript],
+        )
+
+if __name__ == "__main__":
+    demo.launch()
+    # demo.launch(server_name="0.0.0.0", server_port=7860, share=True)