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Running on Zero
Running on Zero
| """Gradio entry point. PITCHIN_PREVIEW_ONLY=1 starts an explicitly marked UI preview.""" | |
| import os | |
| from pathlib import Path | |
| os.environ.setdefault("GRADIO_ANALYTICS_ENABLED", "False") | |
| import gradio as gr | |
| from audio_input import DEFAULT_QUESTION, MAX_SECONDS, prepare_input | |
| APP_DIR = Path(__file__).resolve().parent | |
| CSS = (APP_DIR / "gradio-embed.css").read_text(encoding="utf-8") | |
| EMBED_JS = (APP_DIR / "embed-resize.js").read_text(encoding="utf-8") | |
| def build_demo(engine=None): | |
| def respond(audio, question): | |
| try: | |
| wave, question = prepare_input(audio, question) | |
| except ValueError as exc: | |
| raise gr.Error(str(exc)) from None | |
| if engine is None: | |
| raise gr.Error("This is a UI preview. Model inference is not connected yet.") | |
| try: | |
| return engine.generate(wave, question) | |
| except Exception: | |
| # Never return internal paths, tokens, or uploaded content in errors. | |
| raise gr.Error("Could not finish this request. The GPU may be busy; try a shorter clip or try again shortly.") from None | |
| with gr.Blocks( | |
| title="PitchIn · Singing feedback", theme=gr.themes.Soft(primary_hue="teal", neutral_hue="slate"), | |
| css=CSS, js=EMBED_JS, analytics_enabled=False, delete_cache=(300, 3600), | |
| ) as demo: | |
| gr.Markdown("# PitchIn\nListen to your singing. Ask a question. Explore the feedback.\n\n" | |
| "Natural-language singing coaching from community feedback.", elem_id="intro") | |
| if engine is None: | |
| gr.Markdown("**UI preview — model inference is not connected.**") | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| audio = gr.Audio(sources=["upload", "microphone"], type="filepath", label="Your singing", | |
| max_length=MAX_SECONDS) | |
| gr.Markdown("Upload or record **1–60 seconds** of singing (up to 30 MB). Clear vocals work best.") | |
| question = gr.Textbox(label="What would you like feedback on?", value=DEFAULT_QUESTION, | |
| lines=3, max_lines=6, max_length=1000) | |
| gr.Examples(examples=[ | |
| [DEFAULT_QUESTION], ["What should I focus on improving first?"], | |
| ["How is my pitch accuracy?"], ["What practice exercise would you suggest?"], | |
| ], inputs=[question], label="Try a question") | |
| run = gr.Button("Get feedback", variant="primary") | |
| with gr.Column(scale=1): | |
| answer = gr.Textbox(label="PitchIn-Q feedback", placeholder="Your feedback will appear here.", | |
| lines=15, interactive=False, elem_id="answer") | |
| status = gr.Markdown() | |
| with gr.Accordion("About this demo", open=False): | |
| gr.Markdown("PitchIn-Q adapts Qwen2.5-Omni-7B using Focused QA and Overall Feedback targets " | |
| "reconstructed from community feedback. English questions are recommended. " | |
| "Each request uses the current clip and question; previous requests are not conversation history.\n\n" | |
| "This is a research model. It can miss or misidentify issues in a recording. " | |
| "Generated feedback does not establish an improvement in singing ability.\n\n" | |
| "Uploads are processed for your request. This app does not add them to a training dataset " | |
| "or save a response history. Temporary Gradio files are scheduled for cleanup after one hour.") | |
| run.click(respond, inputs=[audio, question], outputs=[answer, status], | |
| api_name="coach", concurrency_limit=1, concurrency_id="pitchin") | |
| return demo.queue(max_size=12, default_concurrency_limit=1) | |
| if __name__ == "__main__": | |
| engine = None | |
| if os.environ.get("PITCHIN_PREVIEW_ONLY") != "1": | |
| from inference import PitchIn | |
| engine = PitchIn() # Load on startup, outside ZeroGPU's decorated function. | |
| build_demo(engine).launch( | |
| server_name=os.environ.get("GRADIO_SERVER_NAME", "127.0.0.1"), | |
| server_port=int(os.environ.get("GRADIO_SERVER_PORT", "7860")), | |
| share=False, show_error=False, max_file_size="30mb", | |
| ssr_mode=False, # Use the validated client renderer for iframe embedding. | |
| ) | |