"""Provider abstraction for eval scripts. Defaults to HuggingFace Inference API using the HF_TOKEN from .env. Override via environment variables if needed: PROVIDER=anthropic → direct Anthropic SDK PROVIDER=openrouter → OpenRouter (Claude, GPT-4V, Gemini, ...) PROVIDER=hf → HuggingFace Inference API (default) EVAL_MODEL= → override the default model for any provider """ import base64 import io import os from pathlib import Path from PIL import Image # Load .env from repo root so HF_TOKEN etc. are available without manual export. _env_path = Path(__file__).resolve().parents[2] / ".env" if _env_path.exists(): from dotenv import load_dotenv load_dotenv(_env_path, override=False) # don't clobber already-set vars # ── defaults ────────────────────────────────────────────────────────────────── # HF model options by backend: # hf-inference (free tier): meta-llama/Llama-3.2-11B-Vision-Instruct # novita / together backends: Qwen/Qwen2-VL-7B-Instruct (set HF_BACKEND=novita) # # To use a different model: export EVAL_MODEL= # To use a different backend: export HF_BACKEND=novita (or together, fireworks-ai) HF_BACKEND = os.environ.get("HF_BACKEND", "together") HF_MODEL = "google/gemma-4-31B-it" DEFAULTS = { "anthropic": "claude-opus-4-5", "openrouter": "anthropic/claude-opus-4-5", "hf": HF_MODEL, } # Auto-detect provider: prefer whatever key is present. def _default_provider() -> str: if os.environ.get("ANTHROPIC_API_KEY"): return "anthropic" if os.environ.get("OPENROUTER_API_KEY"): return "openrouter" return "hf" # HF_TOKEN loaded from .env def get_provider() -> str: return os.environ.get("PROVIDER", _default_provider()).lower() def get_model() -> str: return os.environ.get("EVAL_MODEL", DEFAULTS[get_provider()]) def get_client(): """Return a client for the configured provider.""" provider = get_provider() print(f"[provider] {provider} / {get_model()}") if provider == "anthropic": import anthropic return _AnthropicWrapper( anthropic.Anthropic(api_key=os.environ["ANTHROPIC_API_KEY"]) ) from openai import OpenAI if provider == "openrouter": return OpenAI( base_url="https://openrouter.ai/api/v1", api_key=os.environ["OPENROUTER_API_KEY"], default_headers={ "HTTP-Referer": "https://github.com/midah/patent-wireframes", "X-Title": "patent-wireframes-eval", }, ) if provider == "hf": token = os.environ.get("HF_TOKEN") if not token: raise RuntimeError("HF_TOKEN not set. Add it to .env or export it.") backend = os.environ.get("HF_BACKEND", HF_BACKEND) return OpenAI( base_url=f"https://router.huggingface.co/{backend}/v1", api_key=token, ) raise ValueError(f"Unknown PROVIDER={provider!r}. Choose: anthropic, openrouter, hf") # ── image encoding ──────────────────────────────────────────────────────────── def encode_image(path: Path, max_long_edge: int = 1024) -> tuple[str, str]: """Return (base64_str, media_type) for an image file, resized to fit.""" img = Image.open(path).convert("RGB") w, h = img.size scale = min(max_long_edge / max(w, h), 1.0) if scale < 1.0: img = img.resize((int(w * scale), int(h * scale)), Image.LANCZOS) buf = io.BytesIO() img.save(buf, format="JPEG", quality=85) return base64.standard_b64encode(buf.getvalue()).decode(), "image/jpeg" # ── unified message API ─────────────────────────────────────────────────────── _NOTHINK_PREFIX = "/nothink\n" def _inject_nothink(messages: list[dict]) -> list[dict]: """Prepend /nothink to any text block so thinking models skip CoT.""" if get_provider() != "hf" or "gemma" not in get_model().lower(): return messages import copy msgs = copy.deepcopy(messages) content = msgs[0].get("content", "") # Plain string content — prepend directly if isinstance(content, str): if not content.startswith("/nothink"): msgs[0]["content"] = _NOTHINK_PREFIX + content return msgs # List of blocks — find first text block for block in content: if isinstance(block, dict) and block.get("type") == "text" and block.get("text", "").strip(): if not block["text"].startswith("/nothink"): block["text"] = _NOTHINK_PREFIX + block["text"] return msgs # No text block found — append one content.append({"type": "text", "text": _NOTHINK_PREFIX}) return msgs def chat(client, messages: list[dict], max_tokens: int = 200) -> str: """Send messages and return the text response. Works with both the Anthropic wrapper and OpenAI-compatible clients. """ if isinstance(client, _AnthropicWrapper): return client.chat(messages, max_tokens) # OpenAI-compatible (OpenRouter, HF) with exponential backoff on rate limits import time messages = _inject_nothink(messages) for attempt in range(4): try: resp = client.chat.completions.create( model=get_model(), messages=messages, max_tokens=max(max_tokens, 600), ) return (resp.choices[0].message.content or "").strip() except Exception as e: if "429" in str(e) or "rate" in str(e).lower(): wait = 4 ** attempt # 1, 4, 16, 64 seconds print(f" Rate limit, retrying in {wait}s...") time.sleep(wait) else: raise return "" def image_message(b64: str, media_type: str, text: str) -> list[dict]: """Build a user message with one image + text, in OpenAI vision format.""" return [{ "role": "user", "content": [ {"type": "image_url", "image_url": {"url": f"data:{media_type};base64,{b64}"}}, {"type": "text", "text": text}, ], }] def multi_image_message( images: list[tuple[str, str]], # list of (b64, media_type) text_before: str = "", text_after: str = "", labels: list[str] | None = None, ) -> list[dict]: """Build a user message with multiple images, interleaved with labels.""" content = [] if text_before: content.append({"type": "text", "text": text_before}) for i, (b64, media_type) in enumerate(images): if labels: content.append({"type": "text", "text": labels[i]}) content.append({"type": "image_url", "image_url": {"url": f"data:{media_type};base64,{b64}"}}) if text_after: content.append({"type": "text", "text": text_after}) return [{"role": "user", "content": content}] # ── Anthropic SDK wrapper (translates to OpenAI message format) ─────────────── class _AnthropicWrapper: """Wraps anthropic.Anthropic to accept OpenAI-format image_url content.""" def __init__(self, client): self._client = client def chat(self, messages: list[dict], max_tokens: int) -> str: converted = self._convert_messages(messages) resp = self._client.messages.create( model=get_model(), max_tokens=max_tokens, messages=converted, ) return resp.content[0].text.strip() @staticmethod def _convert_messages(messages: list[dict]) -> list[dict]: """Convert OpenAI image_url format → Anthropic base64 format.""" out = [] for msg in messages: role = msg["role"] content = msg["content"] if isinstance(content, str): out.append({"role": role, "content": content}) continue new_content = [] for block in content: if block["type"] == "image_url": url = block["image_url"]["url"] # data:image/jpeg;base64, if url.startswith("data:"): meta, data = url.split(",", 1) media_type = meta.split(":")[1].split(";")[0] new_content.append({ "type": "image", "source": { "type": "base64", "media_type": media_type, "data": data, }, }) else: new_content.append({ "type": "image", "source": {"type": "url", "url": url}, }) else: new_content.append(block) out.append({"role": role, "content": new_content}) return out