app.py

# app.py
from fastapi import FastAPI, File, UploadFile, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
import uvicorn
import time
import base64
from typing import List

from model import load_model, predict, predict_from_frames, DEVICE, _DTYPE

app = FastAPI(
    title="ISL Recognition API",
    description="Indian Sign Language recognition using Swin3D-S",
    version="1.0.0"
)

app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_methods=["*"],
    allow_headers=["*"],
)

#  Global state ─
model        = None
model_loaded = False
model_error  = None


#  Startup 
@app.on_event("startup")
async def startup_event():
    global model, model_loaded, model_error
    try:
        model        = load_model()
        model_loaded = True
        model_error  = None
        print("Model loaded and API is ready!")
    except Exception as e:
        model_loaded = False
        model_error  = str(e)
        print("Model failed to load:", e)


#  Root ─
@app.get("/")
def root():
    return {
        "status":  "ISL API is running",
        "message": "POST to /predict (video file) or /predict_frames (base64 frames)"
    }


#  Health ─
@app.get("/health")
def health():
    if not model_loaded or model is None:
        # Return 503 so the wake_up() retry loop in backend knows to keep waiting
        raise HTTPException(
            status_code=503,
            detail={"status": "error", "model_loaded": False, "error": model_error}
        )
    return {
        "status":       "ok",
        "model_loaded": True,
        "device":       str(DEVICE),
        "fp16":         str(_DTYPE),
    }


#  Deep health 
@app.get("/health/deep")
def health_deep():
    if not model_loaded or model is None:
        raise HTTPException(status_code=503, detail="Model not loaded")

    try:
        import torch
        dummy = torch.zeros(1, 3, 16, 224, 224, device=DEVICE, dtype=_DTYPE)
        with torch.no_grad():
            _ = model(dummy)
        return {"status": "ok", "inference": "working", "device": str(DEVICE)}
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Inference failed: {str(e)}")


#  Predict from frames (real-time path) ─
class FramesPayload(BaseModel):
    frames: List[str]
    top_k:  int = 5

@app.post("/predict_frames")
async def predict_frames_api(payload: FramesPayload):
    if not model_loaded or model is None:
        raise HTTPException(status_code=503, detail="Model is not ready")
    if not payload.frames or len(payload.frames) != 16:
        raise HTTPException(status_code=400, detail="Exactly 16 frames required")

    start_time   = time.time()
    frames_bytes = [base64.b64decode(f) for f in payload.frames]

    try:
        result = predict_from_frames(model, frames_bytes, top_k=payload.top_k)
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Inference error: {str(e)}")

    return {
        "prediction":        result["prediction"],
        "confidence":        result["confidence"],
        "top_k":             result["top_k"],
        "inference_time_ms": round((time.time() - start_time) * 1000, 2),
    }


#  Predict from video file 
ALLOWED_EXTENSIONS = ('.mp4', '.mov', '.avi', '.mkv')

@app.post("/predict")
async def predict_sign(file: UploadFile = File(...), top_k: int = 5):
    if not file.filename.lower().endswith(ALLOWED_EXTENSIONS):
        raise HTTPException(
            status_code=400,
            detail=f"Invalid file type. Allowed: {ALLOWED_EXTENSIONS}"
        )
    if not model_loaded or model is None:
        raise HTTPException(status_code=503, detail="Model is not ready")

    start_time  = time.time()
    video_bytes = await file.read()

    try:
        result = predict(model, video_bytes, top_k=top_k)
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Inference error: {str(e)}")

    return {
        **result,
        "inference_time_ms": round((time.time() - start_time) * 1000, 2),
        "filename":          file.filename,
    }


#  Entry point 
if __name__ == "__main__":
    uvicorn.run("app:app", host="0.0.0.0", port=7860)