app.py

import os
import subprocess

# Убираем pyenv, если вдруг остался .python-version
os.environ.pop("PYENV_VERSION", None)

# Установка зависимостей
subprocess.run(["pip", "install", "torch", "wheel"], check=True)
subprocess.run([
    "pip", "install", "--no-build-isolation", 
    "diso@git+https://github.com/SarahWeiii/diso.git"
], check=True)

# Импорты
import gradio as gr
import uuid
import torch
import zipfile
import requests
import traceback

from inference_triposg import run_triposg
from triposg.pipelines.pipeline_triposg import TripoSGPipeline
from briarmbg import BriaRMBG

# Настройки устройства
device = "cuda" if torch.cuda.is_available() else "cpu"
dtype = torch.float16 if device == "cuda" else torch.float32

# Загрузка весов
weights_dir = "pretrained_weights"
triposg_path = os.path.join(weights_dir, "TripoSG")
rmbg_path = os.path.join(weights_dir, "RMBG-1.4")

if not (os.path.exists(triposg_path) and os.path.exists(rmbg_path)):
    print("📦 Downloading pretrained weights...")
    url = "https://huggingface.co/datasets/endlesstools/pretrained-assets/resolve/main/pretrained_models.zip"
    zip_path = "pretrained_models.zip"

    with requests.get(url, stream=True) as r:
        r.raise_for_status()
        with open(zip_path, "wb") as f:
            for chunk in r.iter_content(chunk_size=8192):
                f.write(chunk)

    print("📦 Extracting weights...")
    with zipfile.ZipFile(zip_path, "r") as zip_ref:
        zip_ref.extractall(weights_dir)

    os.remove(zip_path)
    print("✅ Weights ready.")

# Загрузка моделей
pipe = TripoSGPipeline.from_pretrained(triposg_path).to(device, dtype)
rmbg_net = BriaRMBG.from_pretrained(rmbg_path).to(device)
rmbg_net.eval()

# Генерация .glb
# def generate(image_path):
def generate(image_path, face_number=50000, guidance_scale=5.0, num_steps=25):
    print("[API CALL] image_path received:", image_path)
    print("[API CALL] File exists:", os.path.exists(image_path))

    temp_id = str(uuid.uuid4())
    output_path = f"/tmp/{temp_id}.glb"

    print("[DEBUG] Generating mesh from:", image_path)

    try:
        # mesh = run_triposg(
        #     pipe=pipe,
        #     image_input=image_path,
        #     rmbg_net=rmbg_net,
        #     seed=42,
        #     num_inference_steps=25,
        #     guidance_scale=5.0,
        #     faces=-1,
        # )
        mesh = run_triposg(
            pipe=pipe,
            image_input=image_path,
            rmbg_net=rmbg_net,
            seed=42,
            num_inference_steps=int(num_steps),
            guidance_scale=float(guidance_scale),
            faces=int(face_number),
        )

        if mesh is None:
            raise ValueError("Mesh generation failed")

        mesh.export(output_path)
        print(f"[DEBUG] Mesh saved to {output_path}")

        return output_path if os.path.exists(output_path) else "Error: output file not found"
    # except Exception as e:
    #     print("[ERROR]", e)
    #     return f"Error: {e}"
    except Exception as e:
        import traceback
        print("[ERROR]", e)
        traceback.print_exc()  # ← выведет полную трассировку в логи
        return f"Error: {e}"

# Интерфейс Gradio
demo = gr.Interface(
    fn=generate,
    inputs=gr.Image(type="filepath", label="Upload image"),
    outputs=gr.File(label="Download .glb"),
    title="TripoSG Image to 3D",
    description="Upload an image to generate a 3D model (.glb)",
)

# Запуск
demo.launch()