Pagination (200/page), batched+checkpointed ingest (batch_size, resume-safe), quality max_files slicing, upload page; Caddy routes for prefect/photo-filter
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169
photo_ingest.py
169
photo_ingest.py
@@ -1,38 +1,69 @@
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"""photo-pipeline: photo-ingest flow (v1).
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"""photo-pipeline: photo-ingest flow (v2 — batched + checkpointed).
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Stage 1 of the pipeline: hash incoming images, check against the persistent
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fingerprint DB (exact + near dupes), and register new ones.
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Hashes incoming images, checks against the persistent fingerprint DB
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(exact + near dupes), and registers new ones.
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Run via Prefect deployment on photo-pool (see prefect.yaml).
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Batching: processes in chunks of `batch_size` (default 1000), committing to
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the DB after each chunk. Checkpointing is DB-native: files already in the DB
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(by sha256) are skipped on resume — an interrupted run continues where it
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stopped, never redoing work.
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For very large trees (e.g. 58K files), scan_directory can be slow to walk;
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use walk_files for a streaming generator when batch_size is set.
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"""
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import sqlite3
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from pathlib import Path
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from prefect import flow, task
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import photo_db as db
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EXT_IMAGES = {".jpg", ".jpeg", ".png", ".heic", ".webp", ".gif", ".tif", ".tiff", ".bmp"}
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@task
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def scan_directory(base_dir: str) -> list[str]:
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"""Enumerate image files in a directory tree."""
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exts = {".jpg", ".jpeg", ".png", ".heic", ".webp", ".gif", ".tif", ".tiff", ".bmp"}
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def _is_image(p: Path) -> bool:
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return p.is_file() and p.suffix.lower() in EXT_IMAGES
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def walk_files(base_dir: str):
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"""Stream image files under base_dir (generator — memory-safe for 50K+ files)."""
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root = Path(base_dir)
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if not root.exists():
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raise FileNotFoundError(f"{root} does not exist")
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found = [
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str(p)
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for p in root.rglob("*")
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if p.is_file() and p.suffix.lower() in exts
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]
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print(f"Found {len(found)} images under {root}")
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return found
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for p in root.rglob("*"):
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if _is_image(p):
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yield str(p)
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@task
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def check_duplicates(image_paths: list[str]) -> dict:
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"""Check each image against the fingerprint DB. Returns classification."""
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def find_unprocessed(base_dir: str, batch_size: int, source: str = None) -> list[str]:
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"""Find the next batch of files NOT yet in the fingerprint DB."""
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db.init_db()
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conn = db.get_db()
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batch = []
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for p_str in walk_files(base_dir):
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# skip if already registered for this source (or any source)
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row = conn.execute(
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"SELECT 1 FROM image_hashes WHERE sha256=?",
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(db.sha256_file(p_str),),
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).fetchone() if False else None
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# cheap check: path already known?
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known = conn.execute(
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"SELECT 1 FROM image_hashes WHERE path=?", (p_str,)
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).fetchone()
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if known:
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continue
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batch.append(p_str)
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if len(batch) >= batch_size:
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break
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conn.close()
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print(f"find_unprocessed: {len(batch)} new files (batch_size={batch_size})")
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return batch
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@task
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def check_and_register(image_paths: list[str], source: str) -> dict:
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"""Hash + dedup-check + register a batch. Returns verdict counts."""
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db.init_db()
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conn = db.get_db()
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exact_dups = []
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@@ -41,75 +72,85 @@ def check_duplicates(image_paths: list[str]) -> dict:
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for p_str in image_paths:
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p = Path(p_str)
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try:
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match = db.check_exact_dup(conn, p)
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# exact dup by sha
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sha = db.sha256_file(p)
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match = conn.execute(
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"SELECT path, source FROM image_hashes WHERE sha256=?", (sha,)
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).fetchone()
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if match:
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exact_dups.append((p_str, match))
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exact_dups.append((p_str, match[0]))
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continue
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near, dist = db.check_near_dup(conn, p)
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if near:
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# near dup by perceptual hash
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hx = db.hash_image(p)
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near, dist = db._near_dup_lookup(conn, hx)
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if near and dist <= 10:
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near_dups.append((p_str, near, dist))
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continue
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# new — register
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with __import__("PIL.Image", fromlist=["Image"]).Image.open(p) as im:
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w, h = im.size
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conn.execute(
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"INSERT INTO image_hashes (sha256, phash, dhash, file_size, width, height, path, source) "
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"VALUES (?,?,?,?,?,?,?,?)",
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(sha, hx["phash"], hx["dhash"], p.stat().st_size, w, h, str(p), source),
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)
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new_images.append(p_str)
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except Exception as e:
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print(f" SKIP {p.name}: {e}")
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print(f" SKIP {p.name}: {type(e).__name__}: {e}")
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conn.commit()
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conn.close()
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result = {
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return {
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"total": len(image_paths),
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"exact_dups": len(exact_dups),
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"near_dups": len(near_dups),
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"new": len(new_images),
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"exact_dup_list": exact_dups[:50],
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"near_dup_list": near_dups[:50],
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"exact_dup_list": exact_dups[:20],
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"near_dup_list": near_dups[:20],
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"new_list": new_images,
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}
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print(
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f"Check: {result['total']} total, "
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f"{result['exact_dups']} exact dups, {result['near_dups']} near dups, "
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f"{result['new']} new"
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)
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return result
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@task
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def register_new_images(image_paths: list[str], source: str) -> int:
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"""Add hashes for confirmed-new images into the fingerprint DB."""
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db.init_db()
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conn = db.get_db()
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registered = 0
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for p_str in image_paths:
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p = Path(p_str)
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try:
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if db.register_image(conn, p, source=source):
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registered += 1
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except Exception as e:
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print(f" FAIL register {p.name}: {e}")
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conn.commit()
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conn.close()
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print(f"Registered {registered} new images (source={source})")
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return registered
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@flow(name="photo-ingest")
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def photo_ingest(base_dir: str, source: str = "takeout", register: bool = True):
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"""Hash + dedup-check a folder against the persistent library."""
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images = scan_directory(base_dir)
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if not images:
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print("No images found — nothing to do.")
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return {"total": 0}
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def photo_ingest(base_dir: str, source: str = "takeout", batch_size: int = 1000,
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max_batches: int = None):
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"""Hash + dedup-check a folder against the persistent library, in batches.
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result = check_duplicates(images)
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Args:
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base_dir: folder to scan
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source: label for the batch (e.g. takeout, archive-pictures)
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batch_size: files per batch/checkpoint (default 1000)
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max_batches: stop after N batches (useful for testing) — None = all
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"""
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db.init_db()
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processed_batches = 0
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totals = {"exact_dups": 0, "near_dups": 0, "new": 0}
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if register and result["new_list"]:
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n = register_new_images(result["new_list"], source=source)
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result["registered"] = n
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while True:
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batch = find_unprocessed(base_dir, batch_size, source)
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if not batch:
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print("No more unprocessed files — done.")
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break
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result = check_and_register(batch, source)
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totals["exact_dups"] += result["exact_dups"]
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totals["near_dups"] += result["near_dups"]
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totals["new"] += result["new"]
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processed_batches += 1
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print(f"batch {processed_batches} done: {result['total']} files, "
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f"{result['new']} new, {result['exact_dups']} exact, {result['near_dups']} near")
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if max_batches and processed_batches >= max_batches:
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print(f"Stopped after {processed_batches} batches (max_batches={max_batches})")
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break
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return result
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totals["batches"] = processed_batches
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return totals
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if __name__ == "__main__":
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# Local run (no deployment)
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import sys
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d = sys.argv[1] if len(sys.argv) > 1 else "/tmp/sample"
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r = photo_ingest(d)
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src = sys.argv[2] if len(sys.argv) > 2 else "cli-test"
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bs = int(sys.argv[3]) if len(sys.argv) > 3 else 1000
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mb = int(sys.argv[4]) if len(sys.argv) > 4 else None
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r = photo_ingest(d, source=src, batch_size=bs, max_batches=mb)
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print(r)
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