this repo has no description
Add FastAPI web app and refine probe/eval utilities
Canvas-based frontend for drawing math expressions and getting Typst
output from the model. Includes probe.py cleanup and eff_mer/infer.py
whitespace normalization fix.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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-46
+4
pyproject.toml
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pyproject.toml
···
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"tqdm",
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"numpy",
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"torchvision>=0.18",
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"fastapi>=0.111",
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"uvicorn[standard]>=0.29",
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"python-multipart>=0.0.9",
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]
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[project.scripts]
···
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export = "src.export:main"
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generate-typeset = "src.generate_typeset:main"
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probe = "src.probe:main"
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app = "src.app:main"
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eff-mer-evaluate = "src.eff_mer.infer:main"
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[build-system]
+105
src/app.py
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src/app.py
···
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"""
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FastAPI web app: draw math on canvas, get Typst output from Gemma 4.
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Usage:
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uv run app [--checkpoint checkpoints/baseline/final] [--host 0.0.0.0] [--port 8000]
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"""
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import argparse
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import io
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import sys
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from contextlib import asynccontextmanager
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from pathlib import Path
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import torch
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from PIL import Image
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from fastapi import FastAPI, HTTPException, Request
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from fastapi.responses import HTMLResponse, JSONResponse
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from fastapi.staticfiles import StaticFiles
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from .eval import extract_assistant, normalize
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from .data import PROMPT
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_state: dict = {}
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def _load_model(checkpoint: str) -> None:
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from unsloth import FastVisionModel
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model, processor = FastVisionModel.from_pretrained(checkpoint, load_in_4bit=True)
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FastVisionModel.for_inference(model)
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model.eval()
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_state["model"] = model
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_state["processor"] = processor
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def _infer(img: Image.Image) -> str:
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model = _state["model"]
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processor = _state["processor"]
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messages = [
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{
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"role": "user",
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"content": [
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{"type": "image", "image": img},
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{"type": "text", "text": PROMPT},
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],
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}
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]
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inputs = processor(
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images=[img],
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text=processor.apply_chat_template(messages, add_generation_prompt=True),
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return_tensors="pt",
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).to("cuda")
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with torch.no_grad():
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out = model.generate(**inputs, max_new_tokens=256, do_sample=False)
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decoded = processor.decode(out[0], skip_special_tokens=False)
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return extract_assistant(decoded)
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def create_app(checkpoint: str) -> FastAPI:
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@asynccontextmanager
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async def lifespan(app: FastAPI):
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print(f"Loading model from {checkpoint} ...")
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_load_model(checkpoint)
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print("Model ready.")
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yield
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app = FastAPI(lifespan=lifespan)
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static_dir = Path(__file__).parent / "static"
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app.mount("/static", StaticFiles(directory=static_dir), name="static")
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@app.get("/", response_class=HTMLResponse)
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async def index():
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return (static_dir / "index.html").read_text()
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@app.post("/infer")
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async def infer(request: Request):
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if "model" not in _state:
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raise HTTPException(503, "Model not loaded")
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body = await request.body()
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if not body:
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raise HTTPException(400, "Empty body")
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try:
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img = Image.open(io.BytesIO(body)).convert("RGB")
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except Exception as e:
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raise HTTPException(400, f"Invalid image: {e}")
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result = _infer(img)
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return JSONResponse({"typst": result})
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return app
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def main() -> None:
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parser = argparse.ArgumentParser()
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parser.add_argument("--checkpoint", default="checkpoints/baseline/final")
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parser.add_argument("--host", default="127.0.0.1")
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parser.add_argument("--port", type=int, default=8000)
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args = parser.parse_args()
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import uvicorn
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app = create_app(args.checkpoint)
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uvicorn.run(app, host=args.host, port=args.port)
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if __name__ == "__main__":
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main()
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-4
src/eff_mer/infer.py
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-4
src/eff_mer/infer.py
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import argparse
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import json
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import re
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from pathlib import Path
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import numpy as np
···
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import torchvision.transforms.functional as TF
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from PIL import Image, ImageOps
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from tqdm import tqdm
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def _ws_norm(s: str) -> str:
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return re.sub(r"\s+", " ", s).strip()
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# eff-mer data lives in the sibling eff-mer repo
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_EFF_MER_ROOT = Path(__file__).parent.parent.parent.parent / "eff-mer"
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Pad a list of (1, H, W) tensors to the batch-max width.
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Returns (imgs, mem_mask) where mem_mask is None if all widths are equal.
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"""
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from eff_mer.data import STRIDE, _spatial_mask
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from .data import STRIDE, _spatial_mask
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H = tensors[0].shape[1]
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W_max = max(t.shape[2] for t in tensors)
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out = torch.zeros(len(tensors), 1, H, W_max)
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preds = decode_batch(model, imgs, vocab, mem_mask)
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for (_, gt), pred in zip(batch, preds):
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gt_norm = vocab.decode(vocab.encode(gt))
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if pred == gt_norm:
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gt_norm = _ws_norm(gt)
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pred_norm = _ws_norm(pred)
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if pred_norm == gt_norm:
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exact += 1
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if len(examples) < 5:
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examples.append((gt_norm, pred))
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examples.append((gt_norm, pred_norm))
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bar.set_postfix(ExpRate=f"{exact/(start+len(batch)):.3f}")
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+42
-35
src/probe.py
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-35
src/probe.py
···
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"""
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Quick inference probe: run N examples and print raw output vs ground truth.
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Quick inference probe: run against test set examples or arbitrary images.
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Usage: uv run probe [--checkpoint checkpoints/baseline/final] [--n 5]
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Usage:
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uv run probe [--checkpoint checkpoints/baseline/final] [--n 5]
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uv run probe --images path/to/a.png path/to/b.png
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"""
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import argparse
···
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from .eval import extract_assistant, normalize
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def _run_image(img: Image.Image, model, processor) -> str:
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messages = [
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{
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"role": "user",
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"content": [
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{"type": "image", "image": img},
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{"type": "text", "text": PROMPT},
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],
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}
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]
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inputs = processor(
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images=[img],
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text=processor.apply_chat_template(messages, add_generation_prompt=True),
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return_tensors="pt",
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).to("cuda")
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with torch.no_grad():
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out = model.generate(**inputs, max_new_tokens=256, do_sample=False)
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decoded_full = processor.decode(out[0], skip_special_tokens=False)
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return extract_assistant(decoded_full)
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def main() -> None:
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parser = argparse.ArgumentParser()
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parser.add_argument("--checkpoint", default="checkpoints/baseline/final")
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parser.add_argument("--n", type=int, default=5)
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parser.add_argument("--images", nargs="+", metavar="IMG",
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help="Arbitrary image files to run inference on")
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args = parser.parse_args()
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model, processor = FastVisionModel.from_pretrained(args.checkpoint, load_in_4bit=True)
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FastVisionModel.for_inference(model)
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model.eval()
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records = load_records(TEST_SPLITS, dedupe=False)[: args.n]
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-
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for i, r in enumerate(records):
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img = Image.open(r["image_path"]).convert("RGB")
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messages = [
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{
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"role": "user",
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"content": [
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{"type": "image", "image": img},
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{"type": "text", "text": PROMPT},
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],
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}
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]
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inputs = processor(
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images=[img],
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text=processor.apply_chat_template(messages, add_generation_prompt=True),
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return_tensors="pt",
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).to("cuda")
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with torch.no_grad():
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out = model.generate(**inputs, max_new_tokens=256, do_sample=False)
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decoded_full = processor.decode(out[0], skip_special_tokens=False)
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decoded_clean = processor.decode(out[0], skip_special_tokens=True)
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pred = extract_assistant(decoded_full)
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print(f"\n{'='*60}")
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print(f"[{i}] image: {r['image_path']}")
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print(f" EXPECTED : {repr(r['typst'])}")
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print(f" EXTRACTED: {repr(pred)}")
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print(f" CLEAN : {repr(decoded_clean)}")
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print(f" MATCH : {normalize(pred) == normalize(r['typst'])}")
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print(f" RAW (last 300 chars): {repr(decoded_full[-300:])}")
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if args.images:
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for path in args.images:
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img = Image.open(path).convert("RGB")
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pred = _run_image(img, model, processor)
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print(f"{path}: {pred}")
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else:
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records = load_records(TEST_SPLITS, dedupe=False)[: args.n]
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for i, r in enumerate(records):
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img = Image.open(r["image_path"]).convert("RGB")
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pred = _run_image(img, model, processor)
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print(f"\n{'='*60}")
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print(f"[{i}] {r['image_path']}")
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print(f" EXPECTED : {repr(r['typst'])}")
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print(f" PREDICTED: {repr(pred)}")
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print(f" MATCH : {normalize(pred) == normalize(r['typst'])}")
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if __name__ == "__main__":
+185
src/static/index.html
+185
src/static/index.html
···
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<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="UTF-8">
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<title>Typst Math OCR</title>
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<style>
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* { box-sizing: border-box; margin: 0; padding: 0; }
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body {
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background: #1a1a1a;
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color: #e0e0e0;
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font-family: monospace;
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display: flex;
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flex-direction: column;
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align-items: center;
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padding: 2rem;
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gap: 1.2rem;
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min-height: 100vh;
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}
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h1 { font-size: 1.2rem; color: #aaa; letter-spacing: 0.05em; }
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#canvas {
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background: #fff;
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cursor: crosshair;
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border-radius: 4px;
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touch-action: none;
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}
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.controls {
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display: flex;
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gap: 0.8rem;
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align-items: center;
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}
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button {
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background: #333;
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color: #e0e0e0;
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border: 1px solid #555;
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padding: 0.5rem 1.2rem;
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font-family: monospace;
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font-size: 0.9rem;
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cursor: pointer;
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border-radius: 3px;
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}
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button:hover { background: #444; }
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button#run {
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background: #2a4a2a;
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border-color: #4a8a4a;
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color: #8ffc8f;
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}
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button#run:hover { background: #335533; }
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button#run:disabled { opacity: 0.5; cursor: wait; }
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#result {
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width: 512px;
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min-height: 2.5rem;
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background: #111;
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border: 1px solid #333;
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border-radius: 3px;
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padding: 0.6rem 0.8rem;
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font-size: 1rem;
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color: #c8ffb0;
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word-break: break-all;
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white-space: pre-wrap;
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}
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#status { font-size: 0.8rem; color: #666; }
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label { font-size: 0.85rem; color: #888; }
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input[type=range] { width: 80px; accent-color: #4a8a4a; }
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</style>
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</head>
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<body>
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<h1>Typst Math OCR</h1>
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<canvas id="canvas" width="512" height="512"></canvas>
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<div class="controls">
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<button id="run">Run</button>
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<button id="clear">Clear</button>
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<label>Brush <input type="range" id="brush" min="2" max="30" value="8"></label>
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</div>
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<div id="result">—</div>
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<div id="status"></div>
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+
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<script>
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const canvas = document.getElementById('canvas');
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const ctx = canvas.getContext('2d');
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const brushSlider = document.getElementById('brush');
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const runBtn = document.getElementById('run');
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const clearBtn = document.getElementById('clear');
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const resultEl = document.getElementById('result');
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const statusEl = document.getElementById('status');
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+
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// White background
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ctx.fillStyle = '#fff';
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ctx.fillRect(0, 0, canvas.width, canvas.height);
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ctx.strokeStyle = '#000';
90
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ctx.lineCap = 'round';
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ctx.lineJoin = 'round';
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+
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let drawing = false;
94
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let lastX = 0, lastY = 0;
95
+
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function getPos(e) {
97
+
const rect = canvas.getBoundingClientRect();
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const src = e.touches ? e.touches[0] : e;
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return [src.clientX - rect.left, src.clientY - rect.top];
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}
101
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function startDraw(e) {
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e.preventDefault();
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drawing = true;
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[lastX, lastY] = getPos(e);
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// Dot on click
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ctx.beginPath();
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ctx.arc(lastX, lastY, brushSlider.value / 2, 0, Math.PI * 2);
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ctx.fillStyle = '#000';
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ctx.fill();
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}
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+
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function draw(e) {
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if (!drawing) return;
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e.preventDefault();
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const [x, y] = getPos(e);
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ctx.lineWidth = brushSlider.value;
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ctx.strokeStyle = '#000';
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ctx.beginPath();
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ctx.moveTo(lastX, lastY);
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ctx.lineTo(x, y);
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ctx.stroke();
123
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[lastX, lastY] = [x, y];
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+
}
125
+
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function stopDraw() { drawing = false; }
127
+
128
+
canvas.addEventListener('mousedown', startDraw);
129
+
canvas.addEventListener('mousemove', draw);
130
+
canvas.addEventListener('mouseup', stopDraw);
131
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canvas.addEventListener('mouseleave', stopDraw);
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canvas.addEventListener('touchstart', startDraw, { passive: false });
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canvas.addEventListener('touchmove', draw, { passive: false });
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canvas.addEventListener('touchend', stopDraw);
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+
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clearBtn.addEventListener('click', () => {
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ctx.fillStyle = '#fff';
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ctx.fillRect(0, 0, canvas.width, canvas.height);
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resultEl.textContent = '—';
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statusEl.textContent = '';
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});
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+
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+
document.addEventListener('paste', e => {
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const item = [...e.clipboardData.items].find(i => i.type.startsWith('image/'));
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if (!item) return;
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+
const url = URL.createObjectURL(item.getAsFile());
147
+
const img = new Image();
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img.onload = () => {
149
+
ctx.fillStyle = '#fff';
150
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ctx.fillRect(0, 0, canvas.width, canvas.height);
151
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const scale = Math.min(canvas.width / img.width, canvas.height / img.height);
152
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const w = img.width * scale, h = img.height * scale;
153
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ctx.drawImage(img, (canvas.width - w) / 2, (canvas.height - h) / 2, w, h);
154
+
URL.revokeObjectURL(url);
155
+
};
156
+
img.src = url;
157
+
});
158
+
159
+
runBtn.addEventListener('click', async () => {
160
+
runBtn.disabled = true;
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statusEl.textContent = 'Running...';
162
+
resultEl.textContent = '—';
163
+
try {
164
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const blob = await new Promise(resolve => canvas.toBlob(resolve, 'image/png'));
165
+
const resp = await fetch('/infer', {
166
+
method: 'POST',
167
+
headers: { 'Content-Type': 'image/png' },
168
+
body: blob,
169
+
});
170
+
if (!resp.ok) {
171
+
const err = await resp.json().catch(() => ({ detail: resp.statusText }));
172
+
throw new Error(err.detail || resp.statusText);
173
+
}
174
+
const data = await resp.json();
175
+
resultEl.textContent = data.typst;
176
+
statusEl.textContent = '';
177
+
} catch (err) {
178
+
statusEl.textContent = `Error: ${err.message}`;
179
+
} finally {
180
+
runBtn.disabled = false;
181
+
}
182
+
});
183
+
</script>
184
+
</body>
185
+
</html>
+308
-7
uv.lock
+308
-7
uv.lock
···
335
335
version = "12.9.4"
336
336
source = { registry = "https://pypi.org/simple" }
337
337
dependencies = [
338
-
{ name = "cuda-pathfinder" },
338
+
{ name = "cuda-pathfinder", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" },
339
339
]
340
340
wheels = [
341
341
{ url = "https://files.pythonhosted.org/packages/45/e7/b47792cc2d01c7e1d37c32402182524774dadd2d26339bd224e0e913832e/cuda_bindings-12.9.4-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:c912a3d9e6b6651853eed8eed96d6800d69c08e94052c292fec3f282c5a817c9", size = 12210593, upload-time = "2025-10-21T14:51:36.574Z" },
···
461
461
]
462
462
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463
[[package]]
464
+
name = "fastapi"
465
+
version = "0.135.3"
466
+
source = { registry = "https://pypi.org/simple" }
467
+
dependencies = [
468
+
{ name = "annotated-doc" },
469
+
{ name = "pydantic" },
470
+
{ name = "starlette" },
471
+
{ name = "typing-extensions" },
472
+
{ name = "typing-inspection" },
473
+
]
474
+
sdist = { url = "https://files.pythonhosted.org/packages/f7/e6/7adb4c5fa231e82c35b8f5741a9f2d055f520c29af5546fd70d3e8e1cd2e/fastapi-0.135.3.tar.gz", hash = "sha256:bd6d7caf1a2bdd8d676843cdcd2287729572a1ef524fc4d65c17ae002a1be654", size = 396524, upload-time = "2026-04-01T16:23:58.188Z" }
475
+
wheels = [
476
+
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477
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478
+
479
+
[[package]]
464
480
name = "filelock"
465
481
version = "3.25.2"
466
482
source = { registry = "https://pypi.org/simple" }
···
595
611
dependencies = [
596
612
{ name = "datasets" },
597
613
{ name = "editdistance" },
614
+
{ name = "fastapi" },
615
+
{ name = "numpy" },
598
616
{ name = "pillow" },
617
+
{ name = "python-multipart" },
599
618
{ name = "torch" },
619
+
{ name = "torchvision" },
600
620
{ name = "tqdm" },
601
621
{ name = "trl" },
602
622
{ name = "unsloth", extra = ["colab-new"] },
623
+
{ name = "uvicorn", extra = ["standard"] },
603
624
]
604
625
605
626
[package.metadata]
606
627
requires-dist = [
607
628
{ name = "datasets", specifier = ">=2.19" },
608
629
{ name = "editdistance" },
630
+
{ name = "fastapi", specifier = ">=0.111" },
631
+
{ name = "numpy" },
609
632
{ name = "pillow", specifier = ">=10" },
633
+
{ name = "python-multipart", specifier = ">=0.0.9" },
610
634
{ name = "torch", specifier = ">=2.3" },
635
+
{ name = "torchvision", specifier = ">=0.18" },
611
636
{ name = "tqdm" },
612
637
{ name = "trl", specifier = ">=0.15" },
613
638
{ name = "unsloth", extras = ["colab-new"] },
639
+
{ name = "uvicorn", extras = ["standard"], specifier = ">=0.29" },
614
640
]
615
641
616
642
[[package]]
···
700
726
]
701
727
702
728
[[package]]
729
+
name = "httptools"
730
+
version = "0.7.1"
731
+
source = { registry = "https://pypi.org/simple" }
732
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733
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740
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741
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743
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744
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745
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746
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747
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748
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749
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750
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751
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752
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753
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754
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755
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756
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757
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758
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759
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760
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761
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762
+
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763
+
764
+
[[package]]
703
765
name = "httpx"
704
766
version = "0.28.1"
705
767
source = { registry = "https://pypi.org/simple" }
···
1186
1248
version = "9.10.2.21"
1187
1249
source = { registry = "https://pypi.org/simple" }
1188
1250
dependencies = [
1189
-
{ name = "nvidia-cublas-cu12" },
1251
+
{ name = "nvidia-cublas-cu12", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" },
1190
1252
]
1191
1253
wheels = [
1192
1254
{ url = "https://files.pythonhosted.org/packages/ba/51/e123d997aa098c61d029f76663dedbfb9bc8dcf8c60cbd6adbe42f76d049/nvidia_cudnn_cu12-9.10.2.21-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:949452be657fa16687d0930933f032835951ef0892b37d2d53824d1a84dc97a8", size = 706758467, upload-time = "2025-06-06T21:54:08.597Z" },
···
1197
1259
version = "11.3.3.83"
1198
1260
source = { registry = "https://pypi.org/simple" }
1199
1261
dependencies = [
1200
-
{ name = "nvidia-nvjitlink-cu12" },
1262
+
{ name = "nvidia-nvjitlink-cu12", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" },
1201
1263
]
1202
1264
wheels = [
1203
1265
{ url = "https://files.pythonhosted.org/packages/1f/13/ee4e00f30e676b66ae65b4f08cb5bcbb8392c03f54f2d5413ea99a5d1c80/nvidia_cufft_cu12-11.3.3.83-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:4d2dd21ec0b88cf61b62e6b43564355e5222e4a3fb394cac0db101f2dd0d4f74", size = 193118695, upload-time = "2025-03-07T01:45:27.821Z" },
···
1224
1286
version = "11.7.3.90"
1225
1287
source = { registry = "https://pypi.org/simple" }
1226
1288
dependencies = [
1227
-
{ name = "nvidia-cublas-cu12" },
1228
-
{ name = "nvidia-cusparse-cu12" },
1229
-
{ name = "nvidia-nvjitlink-cu12" },
1289
+
{ name = "nvidia-cublas-cu12", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" },
1290
+
{ name = "nvidia-cusparse-cu12", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" },
1291
+
{ name = "nvidia-nvjitlink-cu12", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" },
1230
1292
]
1231
1293
wheels = [
1232
1294
{ url = "https://files.pythonhosted.org/packages/85/48/9a13d2975803e8cf2777d5ed57b87a0b6ca2cc795f9a4f59796a910bfb80/nvidia_cusolver_cu12-11.7.3.90-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:4376c11ad263152bd50ea295c05370360776f8c3427b30991df774f9fb26c450", size = 267506905, upload-time = "2025-03-07T01:47:16.273Z" },
···
1237
1299
version = "12.5.8.93"
1238
1300
source = { registry = "https://pypi.org/simple" }
1239
1301
dependencies = [
1240
-
{ name = "nvidia-nvjitlink-cu12" },
1302
+
{ name = "nvidia-nvjitlink-cu12", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" },
1241
1303
]
1242
1304
wheels = [
1243
1305
{ url = "https://files.pythonhosted.org/packages/c2/f5/e1854cb2f2bcd4280c44736c93550cc300ff4b8c95ebe370d0aa7d2b473d/nvidia_cusparse_cu12-12.5.8.93-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:1ec05d76bbbd8b61b06a80e1eaf8cf4959c3d4ce8e711b65ebd0443bb0ebb13b", size = 288216466, upload-time = "2025-03-07T01:48:13.779Z" },
···
1786
1848
]
1787
1849
1788
1850
[[package]]
1851
+
name = "python-dotenv"
1852
+
version = "1.2.2"
1853
+
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1854
+
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1855
+
wheels = [
1856
+
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1857
+
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1858
+
1859
+
[[package]]
1860
+
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1861
+
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1862
+
source = { registry = "https://pypi.org/simple" }
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