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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) |