Spaces:
Running
on
Zero
Running
on
Zero
Update app.py
Browse files
app.py
CHANGED
@@ -3,28 +3,28 @@ import gradio as gr
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from demo.infer import LiveCCDemoInfer
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waiting_video_response = 'Waiting for video input...'
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not_found_video_response = 'Video does not exist...'
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mode2api = {
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'Real-Time Commentary': 'live_cc',
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'Conversation': 'video_qa'
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}
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with gr.Blocks() as demo:
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gr.Markdown("## LiveCC Real-Time Commentary and Conversation - Gradio Demo")
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gr.Markdown("### [LiveCC: Learning Video LLM with Streaming Speech Transcription at Scale (CVPR 2025)](https://showlab.github.io/livecc/)")
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gr.Markdown("1️⃣ Select Mode, Real-Time Commentary (LiveCC) or Conversation (Common QA)")
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from demo.infer import LiveCCDemoInfer
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with gr.Blocks() as demo:
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class GradioBackend:
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waiting_video_response = 'Waiting for video input...'
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not_found_video_response = 'Video does not exist...'
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mode2api = {
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'Real-Time Commentary': 'live_cc',
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'Conversation': 'video_qa'
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}
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@spaces.GPU
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def __init__(self, model_path: str = 'chenjoya/LiveCC-7B-Instruct'):
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self.infer = LiveCCDemoInfer(model_path)
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from kokoro import KPipeline
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self.audio_pipeline = KPipeline(lang_code='a')
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@spaces.GPU
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def __call__(self, query: str = None, state: dict = {}, mode: str = 'Real-Time Commentary', **kwargs):
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return getattr(self.infer, self.mode2api[mode])(query=query, state=state, **kwargs)
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gradio_backend = GradioBackend()
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gr.Markdown("## LiveCC Real-Time Commentary and Conversation - Gradio Demo")
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gr.Markdown("### [LiveCC: Learning Video LLM with Streaming Speech Transcription at Scale (CVPR 2025)](https://showlab.github.io/livecc/)")
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gr.Markdown("1️⃣ Select Mode, Real-Time Commentary (LiveCC) or Conversation (Common QA)")
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