mirror of
https://github.com/marcogll/telegram_expenses_controller.git
synced 2026-01-13 13:25:15 +00:00
85 lines
2.9 KiB
Python
85 lines
2.9 KiB
Python
"""
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Application entry point.
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Initializes the FastAPI application, sets up logging, database,
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and defines the main API endpoints.
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"""
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import logging
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from fastapi import FastAPI, Depends, HTTPException
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from sqlalchemy.orm import Session
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# It's crucial to set up the config before other imports
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from app.config import config
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# Now, set up logging based on the config
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logging.basicConfig(level=config.LOG_LEVEL.upper())
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logger = logging.getLogger(__name__)
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# Import other components
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from app.schema.base import RawInput
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from app.router import process_expense_input
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from app.persistence import repositories, db
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# Create database tables on startup
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# This is simple, but for production, you'd use migrations (e.g., Alembic)
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repositories.create_tables()
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# Initialize the FastAPI app
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app = FastAPI(
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title="Telegram Expenses Bot API",
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description="Processes and manages expense data from various sources.",
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version="1.0.0"
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)
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@app.on_event("startup")
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async def startup_event():
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logger.info("Application startup complete.")
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logger.info(f"Log level is set to: {config.LOG_LEVEL.upper()}")
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@app.get("/", tags=["Status"])
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async def root():
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"""Health check endpoint."""
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return {"message": "Telegram Expenses Bot API is running."}
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@app.post("/webhook/telegram", tags=["Webhooks"])
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async def process_telegram_update(request: dict):
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"""
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This endpoint would receive updates directly from a Telegram webhook.
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It needs to be implemented to parse the Telegram Update object and
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convert it into our internal RawInput model.
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"""
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logger.info(f"Received Telegram update: {request}")
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# TODO: Implement a parser for the Telegram Update object.
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# For now, this is a placeholder.
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return {"status": "received", "message": "Telegram webhook handler not fully implemented."}
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@app.post("/process-expense", tags=["Processing"])
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async def process_expense(raw_input: RawInput, db_session: Session = Depends(db.get_db)):
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"""
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Receives raw expense data, processes it through the full pipeline,
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and returns the result.
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"""
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logger.info(f"Received raw input for processing: {raw_input.dict()}")
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try:
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result = process_expense_input(db=db_session, raw_input=raw_input)
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if result:
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return {"status": "success", "expense_id": result.id}
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else:
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# This could happen if confidence is low or an error occurred
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raise HTTPException(
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status_code=400,
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detail="Failed to process expense. It may require manual review or had invalid data."
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)
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except ValueError as e:
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logger.error(f"Validation error: {e}")
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raise HTTPException(status_code=422, detail=str(e))
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except Exception as e:
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logger.critical(f"An unexpected error occurred in the processing pipeline: {e}", exc_info=True)
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raise HTTPException(status_code=500, detail="An internal server error occurred.")
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# To run this app:
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# uvicorn app.main:app --reload
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