Initial commit
This commit is contained in:
Zhongwei Li
26
hooks/utils/README.md
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26
hooks/utils/README.md
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# Utils - Shared Utilities
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This directory contains shared utilities and helper functions used by various hooks.
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## Structure:
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- **llm/**: Language model utilities
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- anth.py: Anthropic API utilities
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- oai.py: OpenAI API utilities
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- **tts/**: Text-to-speech utilities
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- elevenlabs_tts.py: ElevenLabs TTS integration
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- openai_tts.py: OpenAI TTS integration
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- pyttsx3_tts.py: Local TTS using pyttsx3
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## Usage:
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These utilities are imported and used by various hooks. They provide common functionality like:
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- API integrations
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- Text-to-speech capabilities
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- Shared helper functions
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- Common validation logic
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## Note:
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Do not run these files directly. They are meant to be imported by hooks.
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115
hooks/utils/llm/anth.py
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115
hooks/utils/llm/anth.py
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#!/usr/bin/env -S uv run --script
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# /// script
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# requires-python = ">=3.8"
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# dependencies = [
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# "anthropic",
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# "python-dotenv",
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# ]
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# ///
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import os
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import sys
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from dotenv import load_dotenv
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def prompt_llm(prompt_text):
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"""
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Base Anthropic LLM prompting method using fastest model.
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Args:
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prompt_text (str): The prompt to send to the model
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Returns:
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str: The model's response text, or None if error
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"""
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load_dotenv()
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api_key = os.getenv("ANTHROPIC_API_KEY")
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if not api_key:
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return None
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try:
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import anthropic
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client = anthropic.Anthropic(api_key=api_key)
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message = client.messages.create(
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model="claude-3-5-haiku-20241022", # Fastest Anthropic model
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max_tokens=100,
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temperature=0.7,
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messages=[{"role": "user", "content": prompt_text}],
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)
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return message.content[0].text.strip()
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except Exception:
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return None
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def generate_completion_message():
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"""
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Generate a completion message using Anthropic LLM.
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Returns:
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str: A natural language completion message, or None if error
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"""
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engineer_name = os.getenv("ENGINEER_NAME", "").strip()
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if engineer_name:
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name_instruction = f"Sometimes (about 30% of the time) include the engineer's name '{engineer_name}' in a natural way."
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examples = f"""Examples of the style:
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- Standard: "Work complete!", "All done!", "Task finished!", "Ready for your next move!"
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- Personalized: "{engineer_name}, all set!", "Ready for you, {engineer_name}!", "Complete, {engineer_name}!", "{engineer_name}, we're done!" """
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else:
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name_instruction = ""
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examples = """Examples of the style: "Work complete!", "All done!", "Task finished!", "Ready for your next move!" """
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prompt = f"""Generate a short, friendly completion message for when an AI coding assistant finishes a task.
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Requirements:
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- Keep it under 10 words
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- Make it positive and future focused
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- Use natural, conversational language
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- Focus on completion/readiness
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- Do NOT include quotes, formatting, or explanations
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- Return ONLY the completion message text
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{name_instruction}
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{examples}
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Generate ONE completion message:"""
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response = prompt_llm(prompt)
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# Clean up response - remove quotes and extra formatting
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if response:
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response = response.strip().strip('"').strip("'").strip()
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# Take first line if multiple lines
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response = response.split("\n")[0].strip()
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return response
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def main():
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"""Command line interface for testing."""
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if len(sys.argv) > 1:
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if sys.argv[1] == "--completion":
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message = generate_completion_message()
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if message:
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print(message)
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else:
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print("Error generating completion message")
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else:
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prompt_text = " ".join(sys.argv[1:])
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response = prompt_llm(prompt_text)
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if response:
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print(response)
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else:
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print("Error calling Anthropic API")
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else:
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print("Usage: ./anth.py 'your prompt here' or ./anth.py --completion")
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if __name__ == "__main__":
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main()
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115
hooks/utils/llm/oai.py
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115
hooks/utils/llm/oai.py
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#!/usr/bin/env -S uv run --script
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# /// script
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# requires-python = ">=3.8"
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# dependencies = [
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# "openai",
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# "python-dotenv",
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# ]
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# ///
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import os
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import sys
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from dotenv import load_dotenv
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def prompt_llm(prompt_text):
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"""
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Base OpenAI LLM prompting method using fastest model.
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Args:
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prompt_text (str): The prompt to send to the model
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Returns:
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str: The model's response text, or None if error
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"""
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load_dotenv()
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api_key = os.getenv("OPENAI_API_KEY")
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if not api_key:
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return None
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try:
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from openai import OpenAI
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client = OpenAI(api_key=api_key)
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response = client.chat.completions.create(
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model="gpt-4.1-nano", # Fastest OpenAI model
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messages=[{"role": "user", "content": prompt_text}],
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max_tokens=100,
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temperature=0.7,
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)
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return response.choices[0].message.content.strip()
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except Exception:
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return None
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def generate_completion_message():
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"""
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Generate a completion message using OpenAI LLM.
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Returns:
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str: A natural language completion message, or None if error
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"""
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engineer_name = os.getenv("ENGINEER_NAME", "").strip()
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if engineer_name:
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name_instruction = f"Sometimes (about 30% of the time) include the engineer's name '{engineer_name}' in a natural way."
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examples = f"""Examples of the style:
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- Standard: "Work complete!", "All done!", "Task finished!", "Ready for your next move!"
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- Personalized: "{engineer_name}, all set!", "Ready for you, {engineer_name}!", "Complete, {engineer_name}!", "{engineer_name}, we're done!" """
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else:
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name_instruction = ""
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examples = """Examples of the style: "Work complete!", "All done!", "Task finished!", "Ready for your next move!" """
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prompt = f"""Generate a short, friendly completion message for when an AI coding assistant finishes a task.
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|
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Requirements:
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- Keep it under 10 words
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- Make it positive and future focused
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- Use natural, conversational language
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- Focus on completion/readiness
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- Do NOT include quotes, formatting, or explanations
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- Return ONLY the completion message text
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{name_instruction}
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{examples}
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Generate ONE completion message:"""
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response = prompt_llm(prompt)
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# Clean up response - remove quotes and extra formatting
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if response:
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response = response.strip().strip('"').strip("'").strip()
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# Take first line if multiple lines
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response = response.split("\n")[0].strip()
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return response
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def main():
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"""Command line interface for testing."""
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if len(sys.argv) > 1:
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if sys.argv[1] == "--completion":
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message = generate_completion_message()
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if message:
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print(message)
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else:
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print("Error generating completion message")
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else:
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prompt_text = " ".join(sys.argv[1:])
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response = prompt_llm(prompt_text)
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if response:
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print(response)
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else:
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print("Error calling OpenAI API")
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else:
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print("Usage: ./oai.py 'your prompt here' or ./oai.py --completion")
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if __name__ == "__main__":
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main()
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90
hooks/utils/tts/elevenlabs_tts.py
Executable file
90
hooks/utils/tts/elevenlabs_tts.py
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#!/usr/bin/env -S uv run --script
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# /// script
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# requires-python = ">=3.8"
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# dependencies = [
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# "elevenlabs",
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# "python-dotenv",
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# ]
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# ///
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import os
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import sys
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from dotenv import load_dotenv
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def main():
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"""
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ElevenLabs Turbo v2.5 TTS Script
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Uses ElevenLabs' Turbo v2.5 model for fast, high-quality text-to-speech.
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Accepts optional text prompt as command-line argument.
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Usage:
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- ./eleven_turbo_tts.py # Uses default text
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- ./eleven_turbo_tts.py "Your custom text" # Uses provided text
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Features:
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- Fast generation (optimized for real-time use)
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- High-quality voice synthesis
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- Stable production model
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- Cost-effective for high-volume usage
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"""
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# Load environment variables
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load_dotenv()
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# Get API key from environment
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api_key = os.getenv("ELEVENLABS_API_KEY")
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if not api_key:
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print("❌ Error: ELEVENLABS_API_KEY not found in environment variables")
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print("Please add your ElevenLabs API key to .env file:")
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print("ELEVENLABS_API_KEY=your_api_key_here")
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sys.exit(1)
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try:
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from elevenlabs import play
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from elevenlabs.client import ElevenLabs
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# Initialize client
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elevenlabs = ElevenLabs(api_key=api_key)
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print("🎙️ ElevenLabs Turbo v2.5 TTS")
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print("=" * 40)
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# Get text from command line argument or use default
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if len(sys.argv) > 1:
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text = " ".join(sys.argv[1:]) # Join all arguments as text
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else:
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text = "The first move is what sets everything in motion."
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print(f"🎯 Text: {text}")
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print("🔊 Generating and playing...")
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try:
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# Generate and play audio directly
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audio = elevenlabs.text_to_speech.convert(
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text=text,
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voice_id="9BWtsMINqrJLrRacOk9x", # Aria voice
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model_id="eleven_turbo_v2_5",
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output_format="mp3_44100_128",
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)
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play(audio)
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print("✅ Playback complete!")
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except Exception as e:
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print(f"❌ Error: {e}")
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except ImportError:
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print("❌ Error: elevenlabs package not installed")
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print("This script uses UV to auto-install dependencies.")
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print("Make sure UV is installed: https://docs.astral.sh/uv/")
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sys.exit(1)
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except Exception as e:
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print(f"❌ Unexpected error: {e}")
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sys.exit(1)
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if __name__ == "__main__":
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main()
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107
hooks/utils/tts/openai_tts.py
Executable file
107
hooks/utils/tts/openai_tts.py
Executable file
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#!/usr/bin/env -S uv run --script
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# /// script
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# requires-python = ">=3.8"
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# dependencies = [
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# "openai",
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# "openai[voice_helpers]",
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# "python-dotenv",
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# ]
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# ///
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import asyncio
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import os
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import subprocess
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import sys
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import tempfile
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from dotenv import load_dotenv
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async def main():
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"""
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OpenAI TTS Script
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Uses OpenAI's latest TTS model for high-quality text-to-speech.
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Accepts optional text prompt as command-line argument.
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Usage:
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- ./openai_tts.py # Uses default text
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- ./openai_tts.py "Your custom text" # Uses provided text
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Features:
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- OpenAI gpt-4o-mini-tts model (latest)
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- Nova voice (engaging and warm)
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- Streaming audio with instructions support
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- Live audio playback via afplay (macOS)
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"""
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# Load environment variables
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load_dotenv()
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# Get API key from environment
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api_key = os.getenv("OPENAI_API_KEY")
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if not api_key:
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print("❌ Error: OPENAI_API_KEY not found in environment variables")
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print("Please add your OpenAI API key to .env file:")
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print("OPENAI_API_KEY=your_api_key_here")
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sys.exit(1)
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try:
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from openai import AsyncOpenAI
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# Initialize OpenAI client
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openai = AsyncOpenAI(api_key=api_key)
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print("🎙️ OpenAI TTS")
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print("=" * 20)
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# Get text from command line argument or use default
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if len(sys.argv) > 1:
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text = " ".join(sys.argv[1:]) # Join all arguments as text
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else:
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text = "Today is a wonderful day to build something people love!"
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print(f"🎯 Text: {text}")
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print("🔊 Generating and streaming...")
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try:
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# Generate and stream audio using OpenAI TTS
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async with openai.audio.speech.with_streaming_response.create(
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model="gpt-4o-mini-tts",
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voice="nova",
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input=text,
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instructions="Speak in a cheerful, positive yet professional tone.",
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response_format="mp3",
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) as response:
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# Create a temporary file to store the audio
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with tempfile.NamedTemporaryFile(
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delete=False, suffix=".mp3"
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) as temp_file:
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# Write the audio stream to the temporary file
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async for chunk in response.iter_bytes():
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temp_file.write(chunk)
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temp_file_path = temp_file.name
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try:
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# Play the audio using afplay
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subprocess.run(["afplay", temp_file_path], check=True)
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print("✅ Playback complete!")
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finally:
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# Clean up the temporary file
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os.unlink(temp_file_path)
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except Exception as e:
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print(f"❌ Error: {e}")
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||||
|
||||
except ImportError:
|
||||
print("❌ Error: Required package not installed")
|
||||
print("This script uses UV to auto-install dependencies.")
|
||||
print("Make sure UV is installed: https://docs.astral.sh/uv/")
|
||||
sys.exit(1)
|
||||
except Exception as e:
|
||||
print(f"❌ Unexpected error: {e}")
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sys.exit(1)
|
||||
|
||||
|
||||
if __name__ == "__main__":
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asyncio.run(main())
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77
hooks/utils/tts/pyttsx3_tts.py
Executable file
77
hooks/utils/tts/pyttsx3_tts.py
Executable file
@@ -0,0 +1,77 @@
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#!/usr/bin/env -S uv run --script
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# /// script
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# requires-python = ">=3.8"
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# dependencies = [
|
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# "pyttsx3",
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# ]
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# ///
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|
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import random
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import sys
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|
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|
||||
def main():
|
||||
"""
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pyttsx3 TTS Script
|
||||
|
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Uses pyttsx3 for offline text-to-speech synthesis.
|
||||
Accepts optional text prompt as command-line argument.
|
||||
|
||||
Usage:
|
||||
- ./pyttsx3_tts.py # Uses default text
|
||||
- ./pyttsx3_tts.py "Your custom text" # Uses provided text
|
||||
|
||||
Features:
|
||||
- Offline TTS (no API key required)
|
||||
- Cross-platform compatibility
|
||||
- Configurable voice settings
|
||||
- Immediate audio playback
|
||||
"""
|
||||
|
||||
try:
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import pyttsx3
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||||
# Initialize TTS engine
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||||
engine = pyttsx3.init()
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||||
|
||||
# Configure engine settings
|
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engine.setProperty("rate", 180) # Speech rate (words per minute)
|
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engine.setProperty("volume", 0.8) # Volume (0.0 to 1.0)
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print("🎙️ pyttsx3 TTS")
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print("=" * 15)
|
||||
|
||||
# Get text from command line argument or use default
|
||||
if len(sys.argv) > 1:
|
||||
text = " ".join(sys.argv[1:]) # Join all arguments as text
|
||||
else:
|
||||
# Default completion messages
|
||||
completion_messages = [
|
||||
"Work complete!",
|
||||
"All done!",
|
||||
"Task finished!",
|
||||
"Job complete!",
|
||||
"Ready for next task!",
|
||||
]
|
||||
text = random.choice(completion_messages)
|
||||
|
||||
print(f"🎯 Text: {text}")
|
||||
print("🔊 Speaking...")
|
||||
|
||||
# Speak the text
|
||||
engine.say(text)
|
||||
engine.runAndWait()
|
||||
|
||||
print("✅ Playback complete!")
|
||||
|
||||
except ImportError:
|
||||
print("❌ Error: pyttsx3 package not installed")
|
||||
print("This script uses UV to auto-install dependencies.")
|
||||
sys.exit(1)
|
||||
except Exception as e:
|
||||
print(f"❌ Error: {e}")
|
||||
sys.exit(1)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user