From 4f4d1ddf30edd5aba3745614eb559188032dfafd Mon Sep 17 00:00:00 2001 From: Christopher Gondek Date: Wed, 24 Sep 2025 15:38:55 +0200 Subject: [PATCH] feat: add generic preprocessor --- pyproject.toml | 4 + src/dataprocessor/domain/powerbi_reader.py | 17 ++ src/dataprocessor/domain/preprocessor.py | 202 +++++++++++++++++++++ src/dataprocessor/router.py | 4 +- src/dataquery/router.py | 1 + src/dependencies.py | 32 ++++ src/pp-config.yaml | 29 +++ src/settings.py | 16 ++ uv.lock | 166 +++++++++++++++++ 9 files changed, 470 insertions(+), 1 deletion(-) create mode 100644 src/dataprocessor/domain/powerbi_reader.py create mode 100644 src/dataprocessor/domain/preprocessor.py create mode 100644 src/pp-config.yaml diff --git a/pyproject.toml b/pyproject.toml index 69f1319..bf03660 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -6,5 +6,9 @@ readme = "README.md" requires-python = ">=3.13" dependencies = [ "fastapi>=0.117.1", + "pandas>=2.3.2", + "pydantic>=2.11.9", + "pydantic-settings>=2.10.1", + "pyyaml>=6.0.2", "uvicorn>=0.37.0", ] diff --git a/src/dataprocessor/domain/powerbi_reader.py b/src/dataprocessor/domain/powerbi_reader.py new file mode 100644 index 0000000..6984d51 --- /dev/null +++ b/src/dataprocessor/domain/powerbi_reader.py @@ -0,0 +1,17 @@ +from dataclasses import dataclass +import pandas as pd + + +@dataclass +class PowerBIReader: + dataset_id: str + access_token: str + + @classmethod + async def create(cls, dataset_id: str, access_token: str): + instance = cls(dataset_id=dataset_id, access_token=access_token) + return instance + + async def read_data(self) -> pd.DataFrame: + # TODO: Implement actual Power BI data reading logic + return pd.DataFrame({"data": ["sample data"]}) diff --git a/src/dataprocessor/domain/preprocessor.py b/src/dataprocessor/domain/preprocessor.py new file mode 100644 index 0000000..5209b07 --- /dev/null +++ b/src/dataprocessor/domain/preprocessor.py @@ -0,0 +1,202 @@ +# src/preprocessor.py +from dataclasses import dataclass, field +from typing import Any, Dict, Callable, List +from pathlib import Path +import pandas as pd +import yaml + + +def step_keep(df: pd.DataFrame, p: Dict[str, Any], r: List[str]) -> pd.DataFrame: + """Keep only specified columns from the DataFrame. + + Filters the DataFrame to retain only the columns specified in the parameters. + If any specified columns are missing from the DataFrame, they are logged + in the report list but do not cause an error. + + Args: + df: The input DataFrame to filter. + p: Parameters dictionary containing: + - columns: List of column names to keep. + r: Report list to append warning messages to. + + Returns: + DataFrame containing only the specified columns that exist in the input. + + Example: + >>> df = pd.DataFrame({'A': [1, 2], 'B': [3, 4], 'C': [5, 6]}) + >>> params = {'columns': ['A', 'C', 'D']} + >>> report = [] + >>> result = step_keep(df, params, report) + >>> print(report) + ['[keep] missing: ['D']'] + """ + cols = list(p["columns"]) + exist = [c for c in cols if c in df.columns] + miss = [c for c in cols if c not in df.columns] + if miss: + r.append(f"[keep] missing: {miss}") + return df.loc[:, exist] + + +def step_fillna(df: pd.DataFrame, p: Dict[str, Any], r: List[str]) -> pd.DataFrame: + """Fill missing values in a specified column. + + Replaces NaN values in the specified column with the provided value. + If the column does not exist in the DataFrame, logs a warning message + in the report list. + + Args: + df: The input DataFrame to modify. + p: Parameters dictionary containing: + - column: Name of the column to fill. + - value: Value to use for filling NaN entries (optional). + r: Report list to append warning messages to. + + Returns: + DataFrame with NaN values filled in the specified column. + + Example: + >>> df = pd.DataFrame({'A': [1, None, 3]}) + >>> params = {'column': 'A', 'value': 0} + >>> report = [] + >>> result = step_fillna(df, params, report) + """ + col, val = p["column"], p.get("value") + if col in df.columns: + df[col] = df[col].fillna(val) + else: + r.append(f"[fillna] column not found: {col}") + return df + + +def step_to_numeric(df: pd.DataFrame, p: Dict[str, Any], r: List[str]) -> pd.DataFrame: + """Convert a column to numeric type. + + Converts the specified column to numeric type using pandas.to_numeric(). + All parameters except 'column' are passed as keyword arguments to pd.to_numeric(). + If the column does not exist in the DataFrame, logs a warning message + in the report list. + + Args: + df: The input DataFrame to modify. + p: Parameters dictionary containing: + - column: Name of the column to convert. + - Additional keyword arguments for pd.to_numeric() (e.g., errors, downcast). + r: Report list to append warning messages to. + + Returns: + DataFrame with the specified column converted to numeric type. + + Example: + >>> df = pd.DataFrame({'A': ['1', '2', '3']}) + >>> params = {'column': 'A', 'errors': 'coerce'} + >>> report = [] + >>> result = step_to_numeric(df, params, report) + """ + col = p["column"] + if col in df.columns: + kwargs = {k: v for k, v in p.items() if k != "column"} + df[col] = pd.to_numeric(df[col], **kwargs) + else: + r.append(f"[to_numeric] column not found: {col}") + return df + + +def step_dropna(df: pd.DataFrame, p: Dict[str, Any], r: List[str]) -> pd.DataFrame: + """Drop rows with missing values in specified columns. + + Removes rows that contain NaN values in any of the specified subset columns. + If any specified columns are missing from the DataFrame, they are logged + in the report list but do not cause an error. + + Args: + df: The input DataFrame to modify. + p: Parameters dictionary containing: + - subset: List of column names to check for NaN values. + r: Report list to append warning messages to. + + Returns: + DataFrame with rows containing NaN values in the subset columns removed. + + Example: + >>> df = pd.DataFrame({'A': [1, None, 3], 'B': [4, 5, None]}) + >>> params = {'subset': ['A', 'B']} + >>> report = [] + >>> result = step_dropna(df, params, report) + """ + sub = [c for c in p["subset"] if c in df.columns] + miss = [c for c in p["subset"] if c not in df.columns] + if miss: + r.append(f"[dropna] subset missing: {miss}") + return df.dropna(subset=sub) + + +REGISTRY: Dict[ + str, Callable[[pd.DataFrame, Dict[str, Any], List[str]], pd.DataFrame] +] = { + "keep": step_keep, + "fillna": step_fillna, + "to_numeric": step_to_numeric, + "dropna": step_dropna, +} + + +@dataclass +class Preprocessor: + steps: List[Dict[str, Any]] = field(default_factory=list) + last_report: List[str] = field(default_factory=list) + + @classmethod + async def create(cls, config_path: str) -> "Preprocessor": + """Create a Preprocessor instance from a YAML configuration file. + + Loads preprocessing steps from a YAML configuration file and creates + a new Preprocessor instance with those steps. + + Args: + config_path: Path to the YAML configuration file containing preprocessing steps. + + Returns: + A new Preprocessor instance configured with the steps from the file. + + Raises: + FileNotFoundError: If the configuration file does not exist. + yaml.YAMLError: If the configuration file contains invalid YAML. + + Example: + >>> preprocessor = await Preprocessor.create("config.yaml") + """ + path = Path(config_path) + cfg = yaml.safe_load(path.read_text(encoding="utf-8")) or {} + return cls(steps=cfg.get("steps", [])) + + async def preprocess(self, df: pd.DataFrame) -> pd.DataFrame: + """Apply all configured preprocessing steps to a DataFrame. + + Executes each preprocessing step in sequence on the provided DataFrame. + Each step is looked up in the REGISTRY and applied with its parameters. + Any warnings or errors encountered during processing are collected + in the last_report attribute. + + Args: + df: The input DataFrame to preprocess. + + Returns: + The preprocessed DataFrame after applying all configured steps. + + Example: + >>> preprocessor = await Preprocessor.create("config.yaml") + >>> df = pd.DataFrame({'A': [1, 2, None], 'B': ['x', 'y', 'z']}) + >>> result = await preprocessor.preprocess(df) + >>> print(preprocessor.last_report) # Check for any warnings + """ + report: List[str] = [] + for step in self.steps: + name, params = next(iter(step.items())) + fn = REGISTRY.get(name) + if not fn: + report.append(f"[engine] unknown step '{name}'") + continue + df = fn(df, params or {}, report) + self.last_report = report + return df diff --git a/src/dataprocessor/router.py b/src/dataprocessor/router.py index bb586f7..ef63173 100644 --- a/src/dataprocessor/router.py +++ b/src/dataprocessor/router.py @@ -3,8 +3,10 @@ # Set up router import logging from fastapi import APIRouter +from fastapi import Depends from src.dataprocessor.schemas import UpdateDbResponse +from src.dependencies import require_pp_api_key router = APIRouter() @@ -14,5 +16,5 @@ logger = logging.getLogger(__name__) @router.post("/update-db") -async def update_db() -> UpdateDbResponse: +async def update_db(*, _: None = Depends(require_pp_api_key)) -> UpdateDbResponse: """Endpoint to update the AI-database.""" diff --git a/src/dataquery/router.py b/src/dataquery/router.py index e69de29..e006d8d 100644 --- a/src/dataquery/router.py +++ b/src/dataquery/router.py @@ -0,0 +1 @@ +# TODO: Endpoint for SQL queries (read-only) diff --git a/src/dependencies.py b/src/dependencies.py index e69de29..b10b9ed 100644 --- a/src/dependencies.py +++ b/src/dependencies.py @@ -0,0 +1,32 @@ +"""Dependencies for FastAPI routers.""" + +from fastapi import HTTPException +from fastapi import status +from fastapi.security import APIKeyHeader +from fastapi.security.base import SecurityBase + +from src.settings import settings + + +# API key header for authentication +api_key_header: SecurityBase = APIKeyHeader( + name="X-PP-API-Key", + description="API key for preprocessor access", +) + + +def require_pp_api_key(*, api_key: str = api_key_header) -> None: + """Validate the preprocessor API key. + + Args: + api_key: The API key from the X-PP-API-Key header. + + Raises: + HTTPException: If the API key is invalid or missing. + """ + if api_key != settings.PP_API_KEY: + raise HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, + detail="Invalid or missing API key", + headers={"WWW-Authenticate": "ApiKey"}, + ) diff --git a/src/pp-config.yaml b/src/pp-config.yaml new file mode 100644 index 0000000..62981e0 --- /dev/null +++ b/src/pp-config.yaml @@ -0,0 +1,29 @@ +version: 1 +steps: + - keep: + columns: + [ + "Artikelkürzel", + "Artikelnummer", + "Artikelbezeichnung", + "Lieferant", + "Ist-Bestand", + "Einheit", + "EP in CHF", + ] + - fillna: + column: "Lieferant" + value: "Unbekannt" + - to_numeric: + column: "EP in CHF" + errors: "coerce" + - dropna: + subset: + [ + "Artikelkürzel", + "Artikelnummer", + "Artikelbezeichnung", + "Ist-Bestand", + "Einheit", + "EP in CHF", + ] diff --git a/src/settings.py b/src/settings.py index e69de29..28271c6 100644 --- a/src/settings.py +++ b/src/settings.py @@ -0,0 +1,16 @@ +"""General application settings.""" + +from pydantic import Field +from pydantic_settings import BaseSettings + + +class Settings(BaseSettings): + """Application settings.""" + + # Preprocessor API key to access this app. + PP_API_KEY: str = Field( + ..., description="API key to access this app for preprocessing." + ) + + +settings = Settings() diff --git a/uv.lock b/uv.lock index 049ac54..7911f52 100644 --- a/uv.lock +++ b/uv.lock @@ -77,6 +77,85 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/76/c6/c88e154df9c4e1a2a66ccf0005a88dfb2650c1dffb6f5ce603dfbd452ce3/idna-3.10-py3-none-any.whl", hash = "sha256:946d195a0d259cbba61165e88e65941f16e9b36ea6ddb97f00452bae8b1287d3", size = 70442, upload-time = "2024-09-15T18:07:37.964Z" }, ] +[[package]] +name = "numpy" +version = "2.3.3" +source = { registry = "https://pypi.org/simple" } +sdist = { url = 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