Move the normalizer to the ML API

[matthewkennedy5]

Description

• Introduced a new /normalize endpoint to the ML router for text normalization, improving the text processing capabilities.
• Created a shared TextInput BaseModel to standardize text input across different endpoints.
• Refactored the text_to_speech endpoint to utilize the new TextInput model.
• Implemented conditional logic to load the text normalizer only when not in development mode, controlled by the IS_DEV environment variable.
• Removed redundant text normalization code from the voice router and added a new async function to call the ML service for normalization.
• Updated environment variable handling in settings to simplify the development mode check.
• Corrected the docker-compose configuration to start the ML service on the right port with the updated application path.

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Changes walkthrough

	Relevant files
Enhancement	ml.py Add text normalization endpoint and refactor TTS endpoint            openduck-py/openduck_py/routers/ml.py Added a new endpoint /normalize to the ML router for text normalization. Introduced TextInput BaseModel for text input to endpoints. Refactored the text_to_speech endpoint to use the new TextInput model. Implemented conditional loading of the text normalizer based on the IS_DEV environment variable. +21/-8    voice.py Integrate new normalization endpoint in voice router                      openduck-py/openduck_py/routers/voice.py Removed local text normalization logic. Added _normalize_text async function to call the new ML service normalization endpoint. Updated speak_response to use the new _normalize_text function. +13/-14
Configuration changes	__init__.py Update environment variable handling for development mode            openduck-py/openduck_py/settings/init.py Removed DEPLOY_ENV variable. Changed IS_DEV to be a boolean directly read from the environment variable. +1/-2      docker-compose.yml Fix docker-compose command for ML service startup                            docker-compose.yml Updated the command to start the ML service with the correct application path and port. +2/-2

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