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DocuLens — Batch processing endpoints.
Accepts multiple files in a single request, processes them sequentially,
and returns per-file results with a batch summary.
"""
import os
import json
import time
import tempfile
import logging
from typing import Optional
from uuid import uuid4
from fastapi import APIRouter, UploadFile, File, Form, HTTPException, Depends
from PIL import Image
from extraction import (
extract_invoice, extract_from_pdf, ExtractionResult,
)
from db.supabase import save_document, create_run, complete_run, save_stage_result
from middleware import check_rate_limit, validate_file_upload, sanitize_filename
from api_webhooks import deliver_webhook
logger = logging.getLogger(__name__)
batch_router = APIRouter()
HF_TOKEN = os.environ.get("HF_TOKEN", "")
# Maximum files per batch (free tier)
MAX_BATCH_SIZE = int(os.environ.get("MAX_BATCH_SIZE", "20"))
# ---------------------------------------------------------------------------
# Batch processing endpoint
# ---------------------------------------------------------------------------
@batch_router.post("/api/v1/batch/extract")
async def batch_extract(
files: list[UploadFile] = File(...),
model: str = Form("Qwen/Qwen2.5-VL-72B-Instruct"),
force_ocr: str = Form("false"),
persist: str = Form("true"),
use_case: str = Form("invoice"),
org_id: str = Form(""),
user_id: str = Form(""),
api_key: str = Depends(check_rate_limit),
):
"""
Extract structured data from multiple uploaded files.
Returns a batch result with per-file status, extraction data,
and a summary of passed/failed counts.
"""
if not files:
raise HTTPException(status_code=400, detail="No files provided")
# ── Tier-based batch limits ──
effective_max = MAX_BATCH_SIZE
if user_id:
try:
from api_usage import check_feature, check_quota, TIER_FEATURES, get_user_tier
if not check_feature(user_id, "batch_upload"):
raise HTTPException(
status_code=403,
detail="Batch upload is not available on the free plan. Upgrade to Starter or above.",
)
tier = get_user_tier(user_id)
tier_max = TIER_FEATURES.get(tier, {}).get("max_batch_files", MAX_BATCH_SIZE)
effective_max = min(tier_max, MAX_BATCH_SIZE) if tier_max else MAX_BATCH_SIZE
# Check quota for all files in the batch
quota = check_quota(user_id, pages_requested=len(files))
if not quota["allowed"]:
raise HTTPException(status_code=429, detail=quota["message"])
except HTTPException:
raise
except Exception as e:
logger.warning("Batch tier check failed (non-blocking): %s", e)
if len(files) > effective_max:
raise HTTPException(
status_code=400,
detail=f"Batch size exceeds limit. Maximum {effective_max} files per batch.",
)
batch_id = str(uuid4())
batch_start = time.time()
use_ocr_only = force_ocr.lower() in ("true", "1", "yes") or model == "ocr-only"
should_persist = persist.lower() in ("true", "1", "yes")
token = "" if use_ocr_only else HF_TOKEN
results = []
passed = 0
failed = 0
for i, file in enumerate(files):
file_start = time.time()
safe_filename = sanitize_filename(file.filename)
file_result = {
"index": i,
"filename": safe_filename,
"status": "pending",
"run_id": None,
"data": None,
"error": None,
"processing_time_ms": 0,
}
try:
# Validate each file
file_size = file.size or 0
validate_file_upload(file.filename, file_size, file.content_type)
# Persistence
doc_id = None
run_id = None
if should_persist:
ext = os.path.splitext(safe_filename)[1].lower()
file_type = "pdf" if ext == ".pdf" else "image"
doc_id = save_document(
filename=safe_filename,
file_type=file_type,
file_size=file_size,
org_id=org_id or None,
user_id=user_id or None,
)
run_id = create_run(
document_id=doc_id,
use_case=use_case,
org_id=org_id or None,
user_id=user_id or None,
)
file_result["run_id"] = run_id
# Read file contents
contents = await file.read()
ext = os.path.splitext(safe_filename)[1].lower()
# Extract
if ext == ".pdf":
page_results = extract_from_pdf(
contents, hf_token=token, force_ocr=use_ocr_only, use_case=use_case, model_id=model,
)
if not page_results:
raise ValueError("Could not extract data from PDF")
result = page_results[0]
# Merge multi-page line items
base_li = len(result.line_items)
for pi, pr in enumerate(page_results[1:], start=1):
for bb in pr.bounding_boxes:
if bb.field.startswith("line_item_"):
li_idx = int(bb.field.split("_")[-1])
bb.field = f"line_item_{base_li + li_idx}"
bb.page = pi
result.bounding_boxes.append(bb)
result.line_items.extend(pr.line_items)
base_li += len(pr.line_items)
else:
with tempfile.NamedTemporaryFile(suffix=ext or ".jpg", delete=False) as tmp:
tmp.write(contents)
tmp_path = tmp.name
try:
image = Image.open(tmp_path)
result = extract_invoice(
image, hf_token=token, force_ocr=use_ocr_only, use_case=use_case, model_id=model,
)
finally:
os.unlink(tmp_path)
file_ms = int((time.time() - file_start) * 1000)
result.processing_time_ms = file_ms
is_failure = result.extraction_method.startswith("failed")
if is_failure:
file_result["status"] = "failed"
file_result["error"] = _describe_failure(result.extraction_method)
failed += 1
else:
file_result["status"] = "passed"
file_result["data"] = result.to_dict()
# Remove large base64 images from batch response to keep payload small
file_result["data"].pop("page_image", None)
passed += 1
# Increment usage for successful extraction
if user_id:
try:
from api_usage import increment_usage
increment_usage(user_id, pages=1)
except Exception:
pass
file_result["processing_time_ms"] = file_ms
# Persist run result
if run_id:
save_stage_result(
run_id=run_id,
stage_type="extraction",
stage_name="Data Extraction",
status="failed" if is_failure else "passed",
sort_order=1,
output=result.to_dict() if not is_failure else None,
error_message=file_result.get("error"),
duration_ms=file_ms,
)
complete_run(
run_id=run_id,
status="completed",
overall_result="failed" if is_failure else "passed",
extraction_data=result.to_dict() if not is_failure else {},
processing_time_ms=file_ms,
error_message=file_result.get("error"),
)
except HTTPException as he:
file_result["status"] = "failed"
file_result["error"] = he.detail
file_result["processing_time_ms"] = int((time.time() - file_start) * 1000)
failed += 1
except Exception as e:
logger.warning(f"Batch file {i} ({safe_filename}) failed: {e}")
file_result["status"] = "failed"
file_result["error"] = str(e)
file_result["processing_time_ms"] = int((time.time() - file_start) * 1000)
failed += 1
results.append(file_result)
batch_ms = int((time.time() - batch_start) * 1000)
batch_response = {
"batch_id": batch_id,
"total": len(files),
"passed": passed,
"failed": failed,
"processing_time_ms": batch_ms,
"results": results,
}
# Fire webhook
try:
deliver_webhook("batch.completed", batch_response)
except Exception:
pass
return batch_response
# ---------------------------------------------------------------------------
# Helpers
# ---------------------------------------------------------------------------
def _describe_failure(method: str) -> str:
if "no_token" in method:
return "HF_TOKEN not set — AI Vision unavailable."
if "vlm" in method:
return "AI Vision models failed. Inference API may be loading or rate-limited."
return "Could not extract readable text. Image may be blurry or handwritten."
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