ef749e4878
- Set up FastAPI application structure - Implement SQLite database with SQLAlchemy - Create WhatsApp webhook endpoints - Implement message storage and analysis - Integrate Gemini 2.5 Pro for message analysis - Add email delivery of insights - Configure APScheduler for weekend analysis - Add linting with Ruff
77 lines
1.5 KiB
Python
77 lines
1.5 KiB
Python
"""
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Pydantic schemas for WhatsApp messages.
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"""
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from datetime import datetime
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from pydantic import BaseModel, Field
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class MessageBase(BaseModel):
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"""
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Base schema for WhatsApp messages.
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"""
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sender_phone: str = Field(..., description="Phone number of the message sender")
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sender_name: str = Field(..., description="Name of the message sender")
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message_body: str = Field(..., description="Content of the message")
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class MessageCreate(MessageBase):
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"""
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Schema for creating a new message.
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"""
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pass
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class Message(MessageBase):
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"""
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Schema for a message.
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"""
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id: int
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timestamp: datetime
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analyzed: bool
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last_analyzed_at: datetime | None = None
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class Config:
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"""
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Pydantic config for the Message schema.
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"""
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from_attributes = True
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# WhatsApp webhook specific schemas
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class WhatsAppTextMessage(BaseModel):
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"""
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Schema for a WhatsApp text message.
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"""
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from_: str = Field(..., alias="from")
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id: str
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timestamp: str
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text: dict
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type: str
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class WhatsAppValue(BaseModel):
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"""
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Schema for WhatsApp webhook value object.
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"""
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messaging_product: str
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metadata: dict
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contacts: list
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messages: list[WhatsAppTextMessage]
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class WhatsAppWebhookEntry(BaseModel):
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"""
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Schema for a WhatsApp webhook entry.
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"""
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id: str
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changes: list[dict]
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class WhatsAppWebhook(BaseModel):
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"""
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Schema for a WhatsApp webhook.
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"""
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object: str
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entry: list[WhatsAppWebhookEntry]
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