feat: add generic preprocessor

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",
 ]

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"]})

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

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."""

src/dataquery/router.py

@@ -0,0 +1 @@
+# TODO: Endpoint for SQL queries (read-only)

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"},
+        )

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",
+        ]

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()

uv.lock

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 ]
 
+[[package]]
+name = "numpy"
+version = "2.3.3"
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 version = "0.37.0"
@@ -181,11 +339,19 @@ version = "0.1.0"
 source = { virtual = "." }
 dependencies = [
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+    { name = "pandas" },
+    { name = "pydantic" },
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+    { name = "pydantic", specifier = ">=2.11.9" },
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