383c1bd966
Add detailed system prompt with strict transcription rules Update user prompt template to enforce consistent Markdown structure Remove redundant title prefix in write_summary helper
62 lines
2.1 KiB
Python
62 lines
2.1 KiB
Python
"""Text summarization with Qwen3 on Apple Silicon via mlx-lm."""
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from mlx_lm import generate, load
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SYSTEM_PROMPT = (
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"You are an expert technical analyst who writes clear, faithful summaries of "
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"transcribed audio. Rules you always follow:\n"
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"- Write the summary in the SAME language as the transcript.\n"
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"- Only use information present in the transcript; never invent facts, names, "
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"or numbers. If something is unclear or cut off, say so rather than guessing.\n"
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"- Preserve technical terms, product names, and acronyms exactly as spoken.\n"
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"- The text is raw speech-to-text: ignore filler, repetitions, and transcription "
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"noise, and focus on the substance."
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)
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USER_PROMPT_TEMPLATE = """Summarize the transcript below as a Markdown document with this exact structure:
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# <a short, descriptive title capturing the main topic>
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**TL;DR:** <2-3 sentence overview a busy reader could read alone>
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## Key points
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- <the most important ideas, one per bullet — aim for 5-8 bullets>
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## Details that matter
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- <specific facts, examples, definitions, numbers, or steps worth keeping>
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## Takeaways
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- <conclusions, recommendations, or open questions raised in the discussion>
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Keep it concise and skimmable. Omit any section that the transcript genuinely does not support.
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Transcript:
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{text}"""
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def summarize_text(text: str, model_repo: str, max_tokens: int = 2048) -> str:
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"""Summarize ``text`` using the Qwen3 model at ``model_repo``."""
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print(f"Loading summarization model: {model_repo}")
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model, tokenizer = load(model_repo)
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messages = [
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{"role": "system", "content": SYSTEM_PROMPT},
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{"role": "user", "content": USER_PROMPT_TEMPLATE.format(text=text)},
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]
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# Qwen3 supports a "thinking" mode; disable it for clean, direct summaries.
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prompt = tokenizer.apply_chat_template(
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messages,
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add_generation_prompt=True,
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enable_thinking=False,
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)
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summary = generate(
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model,
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tokenizer,
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prompt=prompt,
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max_tokens=max_tokens,
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verbose=False,
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)
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return summary.strip()
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