File size: 9,345 Bytes
dd7abff | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 | """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=<model_id> → 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=<model_id>
# 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,<data>
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
|