app.py

# import os
# import subprocess

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

# # ⚙️ Устанавливаем torch и diso
# 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)

# # ✅ Только теперь импортируем всё остальное
# import gradio as gr
# import uuid
# import torch
# import zipfile
# import requests

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

# # === Проверка и 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 from Hugging Face Dataset...")
#     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()

# # === Функция генерации ===
# def generate(file):
#     temp_id = str(uuid.uuid4())
#     input_path = f"/tmp/{temp_id}.png"
#     output_path = f"/tmp/{temp_id}.glb"

#     with open(input_path, "wb") as f:
#         f.write(file)

#     print("[DEBUG] Generating mesh...")
#     try:
#         mesh = run_triposg(
#             pipe=pipe,
#             image_input=input_path,
#             rmbg_net=rmbg_net,
#             seed=42,
#             num_inference_steps=25,
#             guidance_scale=5.0,
#             faces=-1,
#         )
#         # mesh.export(output_path)
#         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):
#             return output_path
#         else:
#             return "Error: mesh export failed or file not found"
#     except Exception as e:
#         print("[ERROR]", e)
#         return f"Error: {e}"

# # === Gradio-интерфейс ===
# demo = gr.Interface(
#     fn=generate,
#     inputs=gr.File(type="binary", label="Upload image"),
#     outputs=gr.File(label="Generated .glb model"),
#     title="TripoSG Image-to-3D",
#     description="Upload an image and get back a 3D GLB model.",
# )

# # # === ВАЖНО: переменная должна называться `app` ===
# # app = demo.launch(inline=True, share=False, prevent_thread_lock=True)
# demo.launch()




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):
    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,
        )

        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()






# import gradio as gr
# import uuid
# import os
# import traceback

# def generate(image_path):
#     try:
#         print("[DEBUG] got image path:", image_path)
#         print("[DEBUG] file exists:", os.path.exists(image_path))

#         out_path = f"/tmp/{uuid.uuid4()}.txt"
#         with open(out_path, "w") as f:
#             f.write(f"Received: {image_path}")

#         return out_path

#     except Exception as e:
#         print("[ERROR]", e)
#         traceback.print_exc()
#         return f"Error: {e}"

# demo = gr.Interface(
#     fn=generate,
#     inputs=gr.Image(type="filepath"),
#     outputs=gr.File()
# )

# demo.launch()