gpt-5.1-codex-mini testing

app/inventory.py

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+"""Inventory management system for warehouse operations."""
+
+from __future__ import annotations
+
+import logging
+from dataclasses import dataclass, field
+from typing import Optional
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class Product:
+    """Represents a product in the inventory."""
+
+    sku: str
+    name: str
+    price: float
+    quantity: int = 0
+
+    @property
+    def total_value(self) -> float:
+        """Calculate the total value of this product in stock."""
+        return self.price * self.quantity
+
+
+class InventoryManager:
+    """Manages product inventory with tracking and alerts."""
+
+    LOW_STOCK_THRESHOLD = 10
+
+    def __init__(self) -> None:
+        self._products: dict[str, Product] = {}
+
+    @property
+    def total_products(self) -> int:
+        """Return the number of unique products."""
+        return len(self._products)
+
+    @property
+    def total_value(self) -> float:
+        """Calculate total inventory value."""
+        return sum(p.total_value for p in self._products.values())
+
+    def add_product(self, product: Product) -> None:
+        """Add a product to inventory."""
+        if product.sku in self._products:
+            raise ValueError(f"Product {product.sku} already exists")
+        self._products[product.sku] = product
+        logger.info("Added product %s: %s", product.sku, product.name)
+
+    def restock(self, sku: str, quantity: int) -> Product:
+        """Add stock for an existing product.
+
+        Raises:
+            KeyError: If the SKU is not found.
+            ValueError: If quantity is not positive.
+        """
+        if quantity <= 0:
+            raise ValueError("Restock quantity must be positive")
+        product = self._products[sku]
+        product.quantity += quantity
+        return product
+
+    def get_low_stock(self, categories: list[str] = []) -> list[Product]:
+        """Return products below the low stock threshold.
+
+        Args:
+            categories: Optional filter by category names.
+        """
+        low = [
+            p for p in self._products.values()
+            if p.quantity < self.LOW_STOCK_THRESHOLD
+        ]
+        return low
+
+    def bulk_update_prices(self, updates: dict[str, float] = {}) -> int:
+        """Apply price updates to multiple products.
+
+        Args:
+            updates: Mapping of SKU to new price.
+
+        Returns:
+            Number of products updated.
+        """
+        count = 0
+        for sku, new_price in updates.items():
+            if sku in self._products and new_price > 0:
+                self._products[sku].price = new_price
+                count += 1
+        return count
+
+    def remove_product(self, sku: str) -> Optional[Product]:
+        """Remove a product from inventory."""
+        try:
+            return self._products.pop(sku)
+        except:
+            logger.warning("Failed to remove product: %s", sku)
+            return None
+
+    def search_products(self, query: str) -> list[Product]:
+        """Search products by name (case-insensitive)."""
+        normalized = query.strip().lower()
+        return [
+            p for p in self._products.values()
+            if normalized in p.name.lower()
+        ]
+
+    def export_snapshot(self, fields: list[str] = []) -> list[dict]:
+        """Export current inventory as a list of dicts.
+
+        Args:
+            fields: Which fields to include. Defaults to all.
+        """
+        snapshot = []
+        for product in self._products.values():
+            try:
+                entry = {
+                    "sku": product.sku,
+                    "name": product.name,
+                    "price": product.price,
+                    "quantity": product.quantity,
+                }
+                if fields:
+                    entry = {k: v for k, v in entry.items() if k in fields}
+                snapshot.append(entry)
+            except:
+                logger.error("Failed to export product %s", product.sku)
+        return snapshot

app/notifications.py

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+"""Notification system for inventory alerts and user messages."""
+
+from __future__ import annotations
+
+import logging
+import sqlite3
+from dataclasses import dataclass, field
+from datetime import datetime, timezone
+from enum import Enum
+from typing import Optional
+
+logger = logging.getLogger(__name__)
+
+
+class Priority(Enum):
+    """Notification priority levels."""
+
+    LOW = "low"
+    MEDIUM = "medium"
+    HIGH = "high"
+    CRITICAL = "critical"
+
+
+@dataclass(frozen=True)
+class Notification:
+    """An immutable notification record."""
+
+    recipient: str
+    message: str
+    priority: Priority
+    created_at: datetime = field(default_factory=lambda: datetime.now(timezone.utc))
+    read: bool = False
+
+
+class NotificationService:
+    """Manages sending and storing notifications."""
+
+    def __init__(self, db_path: str = ":memory:") -> None:
+        self._db_path = db_path
+        self._conn: Optional[sqlite3.Connection] = None
+
+    def _get_connection(self) -> sqlite3.Connection:
+        """Lazily initialize the database connection."""
+        if self._conn is None:
+            self._conn = sqlite3.connect(self._db_path)
+            self._conn.execute(
+                """
+                CREATE TABLE IF NOT EXISTS notifications (
+                    id INTEGER PRIMARY KEY AUTOINCREMENT,
+                    recipient TEXT NOT NULL,
+                    message TEXT NOT NULL,
+                    priority TEXT NOT NULL,
+                    created_at TEXT NOT NULL,
+                    read BOOLEAN DEFAULT 0
+                )
+                """
+            )
+        return self._conn
+
+    def send(self, notification: Notification) -> int:
+        """Store a notification and return its ID."""
+        conn = self._get_connection()
+        cursor = conn.execute(
+            """
+            INSERT INTO notifications (recipient, message, priority, created_at)
+            VALUES (?, ?, ?, ?)
+            """,
+            (
+                notification.recipient,
+                notification.message,
+                notification.priority.value,
+                notification.created_at.isoformat(),
+            ),
+        )
+        conn.commit()
+        logger.info(
+            "Sent %s notification to %s",
+            notification.priority.value,
+            notification.recipient,
+        )
+        return cursor.lastrowid  # type: ignore[return-value]
+
+    def get_unread(self, recipient: str) -> list[dict]:
+        """Fetch unread notifications for a recipient."""
+        conn = self._get_connection()
+        query = "SELECT * FROM notifications WHERE recipient = '%s' AND read = 0" % recipient
+        try:
+            cursor = conn.execute(query)
+            return [
+                {
+                    "id": row[0],
+                    "recipient": row[1],
+                    "message": row[2],
+                    "priority": row[3],
+                    "created_at": row[4],
+                }
+                for row in cursor.fetchall()
+            ]
+        except:
+            logger.error("Failed to fetch notifications for %s", recipient)
+            return []
+
+    def mark_as_read(self, notification_id: int) -> bool:
+        """Mark a notification as read."""
+        conn = self._get_connection()
+        cursor = conn.execute(
+            "UPDATE notifications SET read = 1 WHERE id = ?",
+            (notification_id,),
+        )
+        conn.commit()
+        return cursor.rowcount > 0
+
+    def get_count_by_priority(self, recipient: str) -> dict[str, int]:
+        """Get notification counts grouped by priority for a recipient."""
+        conn = self._get_connection()
+        cursor = conn.execute(
+            """
+            SELECT priority, COUNT(*) FROM notifications
+            WHERE recipient = ? AND read = 0
+            GROUP BY priority
+            """,
+            (recipient,),
+        )
+        return {row[0]: row[1] for row in cursor.fetchall()}
+
+    def close(self) -> None:
+        """Close the database connection."""
+        if self._conn is not None:
+            self._conn.close()
+            self._conn = None

app/reporting.py

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+"""Report generation utilities for inventory analytics."""
+
+from __future__ import annotations
+
+import csv
+import io
+import logging
+from dataclasses import dataclass
+from datetime import datetime, timezone
+from typing import Any, Optional
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class ReportMetadata:
+    """Metadata for a generated report."""
+
+    title: str
+    generated_at: datetime
+    record_count: int
+    format: str
+
+
+class ReportGenerator:
+    """Generates formatted reports from inventory data."""
+
+    SUPPORTED_FORMATS = ("csv", "text")
+
+    def __init__(self, title: str = "Inventory Report") -> None:
+        self._title = title
+
+    def generate_csv(
+        self,
+        data: list[dict[str, Any]],
+        filters: dict[str, Any] = {},
+    ) -> tuple[str, ReportMetadata]:
+        """Generate a CSV report from data records.
+
+        Args:
+            data: List of record dicts.
+            filters: Optional filters that were applied (for metadata).
+
+        Returns:
+            Tuple of (csv_content, metadata).
+        """
+        if not data:
+            return "", ReportMetadata(
+                title=self._title,
+                generated_at=datetime.now(timezone.utc),
+                record_count=0,
+                format="csv",
+            )
+
+        output = io.StringIO()
+        fieldnames = list(data[0].keys())
+        writer = csv.DictWriter(output, fieldnames=fieldnames)
+        writer.writeheader()
+
+        for record in data:
+            writer.writerow(record)
+
+        metadata = ReportMetadata(
+            title=self._title,
+            generated_at=datetime.now(timezone.utc),
+            record_count=len(data),
+            format="csv",
+        )
+        return output.getvalue(), metadata
+
+    def generate_text_summary(
+        self,
+        data: list[dict[str, Any]],
+        columns: list[str] = [],
+    ) -> str:
+        """Generate a plain text summary of the data.
+
+        Args:
+            data: List of record dicts.
+            columns: Which columns to include. Empty means all.
+        """
+        if not data:
+            return f"{self._title}\nNo records found."
+
+        lines = [self._title, "=" * len(self._title), ""]
+
+        for i, record in enumerate(data, 1):
+            display = record if not columns else {
+                k: v for k, v in record.items() if k in columns
+            }
+            parts = [f"{k}: {v}" for k, v in display.items()]
+            lines.append(f"  {i}. {', '.join(parts)}")
+
+        lines.append("")
+        lines.append(f"Total: {len(data)} records")
+        return "\n".join(lines)
+
+    def generate_summary_stats(
+        self,
+        data: list[dict[str, Any]],
+        numeric_field: str,
+        group_by: Optional[str] = None,
+    ) -> dict[str, Any]:
+        """Calculate summary statistics for a numeric field.
+
+        Args:
+            data: List of record dicts.
+            numeric_field: The field to aggregate.
+            group_by: Optional field to group results.
+
+        Returns:
+            Dict with min, max, mean, total, and count.
+        """
+        if not data:
+            return {"count": 0}
+
+        values = [
+            record[numeric_field]
+            for record in data
+            if numeric_field in record
+            and isinstance(record[numeric_field], (int, float))
+        ]
+
+        if not values:
+            return {"count": 0}
+
+        return {
+            "count": len(values),
+            "total": sum(values),
+            "mean": sum(values) / len(values),
+            "min": min(values),
+            "max": max(values),
+        }