clove1002/ZHSAPI

1from fastapi import FastAPI, UploadFile, File, Form2from fastapi.responses import FileResponse, JSONResponse3from gradio_client import Client, handle_file4from gradio_client.exceptions import AppError5import tempfile6import os7import shutil8 9HF_TOKEN = os.getenv("HF_TOKEN")10 11app = FastAPI(title="ZHSAPI")12 13 14@app.get("/health")15def health():16    return {"status": "ok"}17 18 19@app.post("/timelapse")20async def generate_timelapse(21    start_frame: UploadFile = File(...),22    end_frame: UploadFile = File(...),23    prompt: str = Form(default="a construction timelapse"),24):25    with tempfile.TemporaryDirectory() as tmpdir:26        start_path = os.path.join(tmpdir, start_frame.filename)27        end_path = os.path.join(tmpdir, end_frame.filename)28 29        with open(start_path, "wb") as f:30            shutil.copyfileobj(start_frame.file, f)31        with open(end_path, "wb") as f:32            shutil.copyfileobj(end_frame.file, f)33 34        # Try diffusion model first, fall back to CPU FILM interpolation on quota error35        try:36            client = Client("linoyts/LTX-2-3-First-Last-Frame", token=HF_TOKEN)37            result = client.predict(38                first_image=handle_file(start_path),39                last_image=handle_file(end_path),40                input_audio=None,41                prompt=prompt,42                api_name="/generate_video",43            )44            output_path = result[0] if isinstance(result, (list, tuple)) else result45        except AppError as e:46            if "GPU quota" not in str(e):47                return JSONResponse(status_code=502, content={"error": str(e)})48 49            # Fallback: CPU-based FILM interpolation (no GPU quota)50            film = Client("freealise/video_frame_interpolation")51            loaded = film.predict(52                f=[handle_file(start_path), handle_file(end_path)],53                r_bg=False,54                api_name="/loadf",55            )56            frames = loaded[0]57            result = film.predict(58                f_in=frames,59                interpolation=4,60                fps_output=0,61                api_name="/infer",62            )63            output_path = result[0]["video"] if isinstance(result[0], dict) else result[0]64 65        return FileResponse(output_path, media_type="video/mp4", filename="timelapse.mp4")66