update req

app.py

@@ -1,17 +1,3 @@
-import sys, subprocess
-
-# 1) Try importing; if it fails, install your wheel
-try:
-    import torchmcubes
-except ImportError:
-    subprocess.check_call([
-        sys.executable, "-m", "pip", "install",
-        "./wheels/torchmcubes-0.1.0-cp310-cp310-linux_x86_64.whl"
-    ])
-    # now it's installed, so re-import
-    import torchmcubes
-
-# 2) Now safe to import the rest of your app
 import io
 import os
 import shlex
@@ -35,6 +21,9 @@ from tsr.utils import remove_background, resize_foreground, to_gradio_3d_orienta
 # ------------------------------------------------------------
 # 1. Model & utils initialization (runs at startup)
 # ------------------------------------------------------------
+# Install any local wheels (if needed)
+
+
 # device
 device = "cuda:0" if torch.cuda.is_available() else "cpu"
 

main.py

@@ -1,171 +0,0 @@
-import io
-import os
-import shlex
-import subprocess
-import tempfile
-import zipfile
-from functools import partial
-
-from fastapi import FastAPI, File, UploadFile, Form, HTTPException
-from fastapi.responses import StreamingResponse
-from fastapi.middleware.cors import CORSMiddleware
-
-import numpy as np
-import rembg
-import torch
-from PIL import Image
-
-from tsr.system import TSR
-from tsr.utils import remove_background, resize_foreground, to_gradio_3d_orientation
-
-# ------------------------------------------------------------
-# 1. Model & utils initialization (runs at startup)
-# ------------------------------------------------------------
-# Install any local wheels (if needed)
-subprocess.run(
-    shlex.split(
-        "pip install wheel/torchmcubes-0.1.0-cp310-cp310-linux_x86_64.whl"
-    ),
-    check=False,
-)
-
-# device
-device = "cuda:0" if torch.cuda.is_available() else "cpu"
-
-# load model
-model = TSR.from_pretrained(
-    "stabilityai/TripoSR",
-    config_name="config.yaml",
-    weight_name="model.ckpt",
-)
-model.renderer.set_chunk_size(131072)
-model.to(device)
-
-# background removal
-rembg_session = rembg.new_session()
-
-
-def check_input_image(image: Image.Image):
-    if image is None:
-        raise HTTPException(status_code=400, detail="No image uploaded!")
-
-
-def preprocess(
-    input_image: Image.Image, do_remove_background: bool, foreground_ratio: float
-) -> Image.Image:
-    """
-    Mimics the Gradio preprocess(...) function.
-    """
-    def fill_background(image: Image.Image) -> Image.Image:
-        arr = np.array(image).astype(np.float32) / 255.0
-        arr = arr[:, :, :3] * arr[:, :, 3:4] + (1 - arr[:, :, 3:4]) * 0.5
-        out = (arr * 255.0).astype(np.uint8)
-        return Image.fromarray(out)
-
-    if do_remove_background:
-        image = input_image.convert("RGB")
-        image = remove_background(image, rembg_session)
-        image = resize_foreground(image, foreground_ratio)
-        image = fill_background(image)
-    else:
-        image = input_image
-        if image.mode == "RGBA":
-            image = fill_background(image)
-
-    return image
-
-
-def generate(
-    image: Image.Image, mc_resolution: int, formats=["obj", "glb"]
-) -> tuple[str, str]:
-    """
-    Mimics the Gradio generate(...) function.
-    Returns paths to .obj and .glb on disk.
-    """
-    # 1. inference
-    scene_codes = model(image, device=device)
-    mesh = model.extract_mesh(scene_codes, resolution=mc_resolution)[0]
-    mesh = to_gradio_3d_orientation(mesh)
-
-    # 2. export GLB
-    glb_tmp = tempfile.NamedTemporaryFile(suffix=".glb", delete=False)
-    mesh.export(glb_tmp.name)
-
-    # 3. export OBJ (flip x-axis so OBJ is not mirrored)
-    obj_tmp = tempfile.NamedTemporaryFile(suffix=".obj", delete=False)
-    mesh.apply_scale([-1, 1, 1])
-    mesh.export(obj_tmp.name)
-
-    return obj_tmp.name, glb_tmp.name
-
-
-# ------------------------------------------------------------
-# 2. FastAPI app
-# ------------------------------------------------------------
-app = FastAPI(title="TripoSR FastAPI Demo")
-
-# If you need CORS (e.g. calling from a browser-based front-end)
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],
-    allow_methods=["POST", "GET", "OPTIONS"],
-    allow_headers=["*"],
-)
-
-
-@app.post("/generate", response_class=StreamingResponse)
-async def generate_endpoint(
-    image_file: UploadFile = File(...),
-    do_remove_background: bool = Form(True),
-    foreground_ratio: float = Form(0.85),
-    mc_resolution: int = Form(256),
-):
-    """
-    1. Read & validate image
-    2. Preprocess
-    3. Generate mesh
-    4. Package processed image + .obj + .glb into a ZIP
-    """
-    # 1) Read image bytes
-    contents = await image_file.read()
-    try:
-        pil_img = Image.open(io.BytesIO(contents))
-    except Exception:
-        raise HTTPException(status_code=400, detail="Invalid image file")
-
-    check_input_image(pil_img)
-
-    # 2) Preprocess
-    processed = preprocess(pil_img, do_remove_background, foreground_ratio)
-
-    # 3) Generate mesh
-    obj_path, glb_path = generate(processed, mc_resolution)
-
-    # 4) Create in-memory ZIP
-    zip_buffer = io.BytesIO()
-    with zipfile.ZipFile(zip_buffer, mode="w") as zf:
-        # processed image
-        buf = io.BytesIO()
-        processed.save(buf, format="PNG")
-        zf.writestr("processed.png", buf.getvalue())
-
-        # .obj
-        with open(obj_path, "rb") as f:
-            zf.writestr(os.path.basename(obj_path), f.read())
-
-        # .glb
-        with open(glb_path, "rb") as f:
-            zf.writestr(os.path.basename(glb_path), f.read())
-
-    zip_buffer.seek(0)
-
-    # Cleanup temp files
-    os.remove(obj_path)
-    os.remove(glb_path)
-
-    headers = {
-        "Content-Disposition": 'attachment; filename="tripo_output.zip"'
-    }
-    return StreamingResponse(
-        zip_buffer, media_type="application/zip", headers=headers
-    ) 

requirements.txt

@@ -8,6 +8,6 @@ rembg
 huggingface-hub
 gradio
 onnxruntime
-
+./wheel/torchmcubes-0.1.0-cp310-cp310-linux_x86_64.whl
 fastapi
 uvicorn[standard]