Replace pandas with stdlib csv, apprise with direct Slack webhook, switch to opencv-headless

- Rewrite csv_exporter.py to use stdlib csv.DictWriter instead of pandas DataFrame
- Rewrite notifications.py to use aiohttp direct Slack webhook instead of apprise
- Switch opencv-python to opencv-python-headless in pyproject.toml
- Move httpx from dev to prod dependencies
- Remove pandas and apprise from mypy ignore_missing_imports

csv_exporter.py

@@ -1,43 +1,62 @@
+import csv
+import json
 from pathlib import Path
-import pandas as pd
+
 from models.listing import QueryParameters
 from repositories.listing_repository import ListingRepository
 
 
+DROP_COLUMNS = {"_sa_instance_state", "additional_info"}
+ENSURE_COLUMNS = ("service_charge", "lease_left", "square_meters")
+
+
 async def export_to_csv(
     repository: ListingRepository,
     output_file: Path,
     query_parameters: QueryParameters | None = None,
 ) -> None:
     listings = await repository.get_listings(query_parameters=query_parameters)
-    ds = [listing.__dict__ for listing in listings]
-    df = pd.DataFrame(ds)
+    rows = [
+        {k: v for k, v in listing.__dict__.items() if k not in DROP_COLUMNS}
+        for listing in listings
+    ]
 
-    # read decisions on file
+    if not rows:
+        output_file.write_text("")
+        return
+
+    # Read decisions file if present
+    decisions: dict[str, str] = {}
     decisions_path = Path("data/decisions.json")
     if decisions_path.exists():
-        decisions = pd.read_json(decisions_path)
-        df.loc[:, "decision"] = df.id.apply(lambda x: decisions.get(x))
+        with open(decisions_path) as f:
+            decisions = json.load(f)
 
-    # remove _sa_instance_state column
-    drop_columns = ["_sa_instance_state", "additional_info"]
-    df = df.drop(columns=drop_columns)
+    for row in rows:
+        # Add decision column
+        row["decision"] = decisions.get(str(row.get("id")))
 
-    # Ensure columns exist with NaN defaults for clean CSV output
-    for col in ("service_charge", "lease_left", "square_meters"):
-        if col not in df.columns:
-            df.loc[:, col] = float("nan")
+        # Ensure optional columns exist
+        for col in ENSURE_COLUMNS:
+            row.setdefault(col, None)
 
-    # Replace -1 sentinel values with NaN
-    df.loc[:, "square_meters"] = df.square_meters.replace({-1: float("nan")})
+        # Replace -1 sentinel in square_meters
+        if row.get("square_meters") == -1:
+            row["square_meters"] = None
 
-    # Add price per sqm column (guard against zero/missing square_meters)
-    df.loc[:, "price_per_sqm"] = df.apply(
-        lambda row: round(row.price / row.square_meters, 2)
-        if row.square_meters and row.square_meters > 0
-        else None,
-        axis=1,
-    )
+        # Compute price_per_sqm
+        sqm = row.get("square_meters")
+        price = row.get("price")
+        if sqm and sqm > 0 and price:
+            row["price_per_sqm"] = round(price / sqm, 2)
+        else:
+            row["price_per_sqm"] = None
 
-    df = df.sort_values(by=["price_per_sqm"], ascending=True)
-    df.to_csv(str(output_file), index=False)
+    # Sort by price_per_sqm (None values last)
+    rows.sort(key=lambda r: (r["price_per_sqm"] is None, r["price_per_sqm"] or 0))
+
+    fieldnames = list(rows[0].keys())
+    with open(output_file, "w", newline="") as f:
+        writer = csv.DictWriter(f, fieldnames=fieldnames)
+        writer.writeheader()
+        writer.writerows(rows)