import os
import time
import tempfile
from pathlib import Path
from fastapi import FastAPI, File, UploadFile
from fastapi.responses import HTMLResponse, JSONResponse
from fastapi.staticfiles import StaticFiles
from inference import load_model, predict_video
from video_preprocess import PreprocessConfig
APP_DIR = Path(__file__).resolve().parent
UI_DIR = APP_DIR / "ui"
DIST_DIR = UI_DIR / "dist"
app = FastAPI(title="LumiSign Sign Inference")
MODEL = None
LABEL_MAP = None
DEFAULT_LOCAL_CHECKPOINT = APP_DIR / "transformer_large.pth"
DEFAULT_MODEL_CHECKPOINT = (
str(DEFAULT_LOCAL_CHECKPOINT) if DEFAULT_LOCAL_CHECKPOINT.is_file() else None
)
MODEL_DATASET = os.getenv("MODEL_DATASET", "isl_split_dataset")
MODEL_TYPE = os.getenv("MODEL_TYPE", "transformer")
MODEL_TRANSFORMER_SIZE = os.getenv(
"MODEL_TRANSFORMER_SIZE",
"large" if DEFAULT_MODEL_CHECKPOINT else "small",
)
MODEL_CHECKPOINT = os.getenv("MODEL_CHECKPOINT", DEFAULT_MODEL_CHECKPOINT)
MODEL_MAX_FRAME_LEN = int(os.getenv("MODEL_MAX_FRAME_LEN", "169"))
DEFAULT_LABEL_MAP_CANDIDATES = [
APP_DIR / "label_maps" / f"label_map_{MODEL_DATASET}.json",
APP_DIR / "label_maps" / "label_map_isl_split_dataset.json",
APP_DIR / "label_maps" / "label_map_islsplit.json",
]
MODEL_LABEL_MAP_PATH = os.getenv("MODEL_LABEL_MAP_PATH")
if not MODEL_LABEL_MAP_PATH:
for candidate in DEFAULT_LABEL_MAP_CANDIDATES:
if candidate.is_file():
MODEL_LABEL_MAP_PATH = str(candidate)
break
@app.on_event("startup")
def _load():
global MODEL, LABEL_MAP
MODEL, LABEL_MAP = load_model(
dataset=MODEL_DATASET,
model_type=MODEL_TYPE,
transformer_size=MODEL_TRANSFORMER_SIZE,
checkpoint_path=MODEL_CHECKPOINT,
label_map_path=MODEL_LABEL_MAP_PATH,
)
@app.post("/predict")
async def predict(file: UploadFile = File(...)):
if MODEL is None or LABEL_MAP is None:
return JSONResponse({"error": "Model not loaded"}, status_code=500)
suffix = Path(file.filename).suffix if file.filename else ".mp4"
with tempfile.NamedTemporaryFile(delete=False, suffix=suffix) as tmp:
contents = await file.read()
tmp.write(contents)
tmp_path = tmp.name
preprocess_config = PreprocessConfig(
apply_darken=False,
apply_brighten=True,
darken_min=0.3,
darken_max=0.8,
brighten_method="clahe",
brighten_gamma_min=1.2,
brighten_gamma_max=1.8,
)
start = time.time()
try:
result = predict_video(
tmp_path,
dataset=MODEL_DATASET,
model=MODEL,
label_map=LABEL_MAP,
preprocess_config=preprocess_config,
max_frame_len=MODEL_MAX_FRAME_LEN,
)
finally:
try:
os.remove(tmp_path)
except OSError:
pass
elapsed_ms = int((time.time() - start) * 1000)
result["elapsed_ms"] = elapsed_ms
return JSONResponse(result)
def _ui_built() -> bool:
index_path = DIST_DIR / "index.html"
if not index_path.is_file():
return False
src_dir = UI_DIR / "src"
if not src_dir.is_dir():
return True
latest_src_mtime = 0.0
for root, _, files in os.walk(src_dir):
for name in files:
try:
latest_src_mtime = max(
latest_src_mtime,
(Path(root) / name).stat().st_mtime,
)
except OSError:
continue
try:
return index_path.stat().st_mtime >= latest_src_mtime
except OSError:
return True
def _configure_ui(app: FastAPI) -> None:
if _ui_built():
app.mount("/", StaticFiles(directory=DIST_DIR, html=True), name="ui")
return
@app.get("/")
def index():
ui_path = str(UI_DIR)
html = f"""
UI Not Built
UI not built yet
To run the React UI in development:
cd {ui_path}
npm install
npm run dev
Then open http://localhost:5173 (it proxies /predict).
To serve the built UI from FastAPI:
cd {ui_path}
npm run build
Restart the backend after building.
"""
return HTMLResponse(html)
_configure_ui(app)