app.py

import os
import subprocess

# Убираем pyenv
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 (перенесены после установки зависимостей)
import gradio as gr
import uuid
import torch
import zipfile
import requests
import traceback
import trimesh
from trimesh.exchange.gltf import export_glb

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

from pygltflib import GLTF2, Scene, Node, Mesh, Buffer, BufferView, Accessor, BufferTarget, ComponentType, AccessorType
import numpy as np
import base64


print("Trimesh version:", trimesh.__version__)

# 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, 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"

    try:
        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 or mesh.vertices.shape[0] == 0 or mesh.faces.shape[0] == 0:
            raise ValueError("Mesh generation returned an empty mesh")

        vertices = mesh.vertices.astype(np.float32)
        indices = mesh.faces.astype(np.uint32).flatten()

        # Pack binary data
        vertex_bytes = vertices.tobytes()
        index_bytes = indices.tobytes()
        total_bytes = vertex_bytes + index_bytes

        buffer = Buffer(byteLength=len(total_bytes))
        buffer_view_vert = BufferView(
            buffer=0,
            byteOffset=0,
            byteLength=len(vertex_bytes),
            target=BufferTarget.ARRAY_BUFFER.value
        )
        buffer_view_index = BufferView(
            buffer=0,
            byteOffset=len(vertex_bytes),
            byteLength=len(index_bytes),
            target=BufferTarget.ELEMENT_ARRAY_BUFFER.value
        )

        accessor_vert = Accessor(
            bufferView=0,
            byteOffset=0,
            componentType=ComponentType.FLOAT.value,
            count=len(vertices),
            type=AccessorType.VEC3.value,
            min=vertices.min(axis=0).tolist(),
            max=vertices.max(axis=0).tolist()
        )

        accessor_index = Accessor(
            bufferView=1,
            byteOffset=0,
            componentType=ComponentType.UNSIGNED_INT.value,
            count=len(indices),
            type=AccessorType.SCALAR.value
        )

        gltf = GLTF2(
            buffers=[buffer],
            bufferViews=[buffer_view_vert, buffer_view_index],
            accessors=[accessor_vert, accessor_index],
            meshes=[Mesh(primitives=[{
                "attributes": {"POSITION": 0},
                "indices": 1
            }])],
            scenes=[Scene(nodes=[0])],
            nodes=[Node(mesh=0)],
            scene=0
        )

        # Inject binary blob
        gltf.set_binary_blob(total_bytes)
        gltf.save_binary(output_path)

        print(f"[DEBUG] Mesh saved to {output_path}")
        return output_path if os.path.exists(output_path) else None

    except Exception as e:
        print("[ERROR]", e)
        traceback.print_exc()
        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()