| |
| """Generic OpenAI-chat OCR client for kbench engine comparison. Resumable (skips ids already in OUT). |
| Usage: engine_client.py PAGES_JSON OUT_JSONL URL MODEL PROMPT_KEY MAX_TOKENS(0=server default) WORKERS |
| Row out: {id, text, finish, error, seconds}. temperature 0, repetition_penalty 1.0, no logits processors.""" |
| import base64, json, mimetypes, sys, time, urllib.request |
| from concurrent.futures import ThreadPoolExecutor |
| from pathlib import Path |
| PROMPTS = { |
| |
| "unlimited": "<image>document parsing.", |
| |
| "deepseek": "<|grounding|>Convert the document to markdown. ", |
| |
| "ovis": ("Extract all readable content from the image in natural human reading order and output the result as a " |
| "single Markdown document. For charts or images, represent them using an HTML image tag: <img src=\"images/" |
| "bbox_{left}_{top}_{right}_{bottom}.jpg\" />, where left, top, right, bottom are bounding box coordinates " |
| "scaled to [0, 1000). Format formulas as LaTeX. Format tables as HTML: <table>...</table>. Transcribe all " |
| "other text as standard Markdown. Preserve the original text without translation or paraphrasing."), |
| } |
| pages_path, out_path, url, model, pkey, max_tokens, workers = sys.argv[1:8] |
| max_tokens, workers = int(max_tokens), int(workers); prompt = PROMPTS[pkey] |
| pages = json.load(open(pages_path))["pages"] |
| out = Path(out_path); done = {json.loads(l)["id"] for l in out.open()} if out.exists() else set() |
| todo = [p for p in pages if p["id"] not in done] |
| print(f"pages {len(pages)} done {len(done)} todo {len(todo)}", flush=True) |
| def one(p): |
| t = time.time(); img = Path(p["image"]) |
| mime = mimetypes.guess_type(img.name)[0] or "image/png" |
| payload = {"model": model, "messages": [{"role": "user", "content": [ |
| {"type": "image_url", "image_url": {"url": f"data:{mime};base64,{base64.b64encode(img.read_bytes()).decode()}"}}, |
| {"type": "text", "text": prompt}]}], |
| "temperature": 0, "repetition_penalty": 1.0, "skip_special_tokens": False} |
| if max_tokens: payload["max_tokens"] = max_tokens |
| try: |
| req = urllib.request.Request(url, data=json.dumps(payload).encode(), headers={"Content-Type": "application/json"}) |
| with urllib.request.urlopen(req, timeout=1200) as r: body = json.loads(r.read()) |
| ch = body["choices"][0] |
| return {"id": p["id"], "text": ch["message"]["content"] or "", "finish": ch.get("finish_reason"), "error": None, "seconds": round(time.time() - t, 2)} |
| except Exception as e: |
| msg = repr(e) |
| if hasattr(e, "read"): |
| try: msg += " " + e.read().decode()[:300] |
| except Exception: pass |
| return {"id": p["id"], "text": "", "finish": None, "error": msg[:500], "seconds": round(time.time() - t, 2)} |
| with out.open("a") as f, ThreadPoolExecutor(workers) as ex: |
| for k, row in enumerate(ex.map(one, todo), 1): |
| f.write(json.dumps(row, ensure_ascii=False) + "\n"); f.flush() |
| if k % 200 == 0: print(k, row["id"], row["error"] or row["finish"], row["seconds"], flush=True) |
| print("CLIENT_DONE", flush=True) |
|
|
