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app.py

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+"""
+AI 3D Generation Pipeline — Hugging Face Space (ZeroGPU)
+========================================================
+A production-ready Gradio application that generates high-fidelity,
+manifold 3D models from multi-angle images + text instructions.
+
+Pipeline:
+  1. Background removal (RMBG-1.4)
+  2. Multi-image composite / best-view selection
+  3. TripoSG 3D reconstruction
+  4. Manifold repair + scale normalization
+  5. GLB/OBJ/STL export
+
+Designed for Hugging Face ZeroGPU (A100-40GB).
+"""
+
+import os
+import io
+import re
+import sys
+import json
+import uuid
+import time
+import shutil
+import base64
+import logging
+import tempfile
+import zipfile
+from pathlib import Path
+from typing import Optional
+
+import gradio as gr
+import numpy as np
+import torch
+from PIL import Image
+
+# HuggingFace ZeroGPU decorator
+try:
+    import spaces
+    HAS_SPACES = True
+except ImportError:
+    HAS_SPACES = False
+    # Fallback no-op decorator for local dev
+    class spaces:
+        @staticmethod
+        def GPU(fn=None, duration=None):
+            if fn is None:
+                return lambda f: f
+            return fn
+
+# Mesh processing
+import trimesh
+
+# ---------------------------------------------------------------------------
+# Configuration
+# ---------------------------------------------------------------------------
+MODEL_ID = "VAST-AI/TripoSG"
+RMBG_MODEL_ID = "briaai/RMBG-1.4"
+MAX_FACES = 90000
+DEFAULT_FACES = 50000
+OUTPUT_DIR = Path(tempfile.gettempdir()) / "triposg_output"
+OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
+
+logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")
+logger = logging.getLogger(__name__)
+
+# ---------------------------------------------------------------------------
+# Global model references (loaded once)
+# ---------------------------------------------------------------------------
+triposg_pipeline = None
+rmbg_model = None
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+
+def load_models():
+    """Load TripoSG + RMBG models into GPU memory."""
+    global triposg_pipeline, rmbg_model
+
+    if triposg_pipeline is not None:
+        return  # already loaded
+
+    logger.info("Loading TripoSG pipeline from %s ...", MODEL_ID)
+    try:
+        # TripoSG uses a custom pipeline; import from the repo
+        sys.path.insert(0, os.path.dirname(__file__))
+
+        # Method 1: Try to use the HuggingFace model directly
+        from huggingface_hub import snapshot_download
+        model_path = snapshot_download(MODEL_ID)
+        logger.info("Model downloaded to: %s", model_path)
+
+        # Import TripoSG inference utilities
+        # The model uses a custom pipeline - we import it dynamically
+        sys.path.insert(0, model_path)
+
+        try:
+            from triposg import TripoSGPipeline
+            triposg_pipeline = TripoSGPipeline.from_pretrained(model_path).to(device)
+        except ImportError:
+            # Alternative: use the inference script approach
+            logger.warning("Direct import failed, using CLI inference fallback")
+            triposg_pipeline = {"model_path": model_path, "type": "cli"}
+
+    except Exception as e:
+        logger.error("Failed to load TripoSG: %s", e)
+        triposg_pipeline = None
+
+    # Load background remover
+    logger.info("Loading RMBG background remover...")
+    try:
+        from transformers import pipeline as hf_pipeline
+        rmbg_model = hf_pipeline("image-segmentation", model=RMBG_MODEL_ID, device=device)
+        logger.info("RMBG loaded successfully")
+    except Exception as e:
+        logger.warning("RMBG not available, will skip background removal: %s", e)
+        rmbg_model = None
+
+
+# ---------------------------------------------------------------------------
+# Image Processing Utilities
+# ---------------------------------------------------------------------------
+def remove_background(image: Image.Image) -> Image.Image:
+    """Remove background from an image using RMBG-1.4."""
+    if rmbg_model is None:
+        return image
+    try:
+        results = rmbg_model(image)
+        # Find the mask and apply it
+        for result in results:
+            if result.get("label") in ["foreground", "person", "object"]:
+                mask = result["mask"]
+                image = image.convert("RGBA")
+                image.putalpha(mask)
+                return image
+        # If no specific label, use the first result
+        if results:
+            mask = results[0]["mask"]
+            image = image.convert("RGBA")
+            image.putalpha(mask)
+        return image
+    except Exception as e:
+        logger.warning("Background removal failed: %s", e)
+        return image
+
+
+def select_best_view(images: list[Image.Image]) -> Image.Image:
+    """
+    From multiple input images, select the best single view for reconstruction.
+    Heuristic: pick the largest image (highest resolution = most detail).
+    In a production pipeline, you would use a multi-view reconstruction model.
+    """
+    if not images:
+        raise ValueError("No images provided")
+    if len(images) == 1:
+        return images[0]
+
+    # Score images by resolution and sharpness
+    scored = []
+    for img in images:
+        w, h = img.size
+        resolution_score = w * h
+        # Simple Laplacian variance for sharpness
+        gray = np.array(img.convert("L"), dtype=np.float32)
+        laplacian = np.abs(np.roll(gray, 1, 0) + np.roll(gray, -1, 0) +
+                          np.roll(gray, 1, 1) + np.roll(gray, -1, 1) - 4 * gray)
+        sharpness = float(np.var(laplacian))
+        scored.append((img, resolution_score * 0.3 + sharpness * 0.7))
+
+    scored.sort(key=lambda x: x[1], reverse=True)
+    logger.info("Selected best view (score=%.1f) from %d images", scored[0][1], len(images))
+    return scored[0][0]
+
+
+def create_composite_view(images: list[Image.Image], max_images: int = 4) -> Image.Image:
+    """
+    Create a multi-view composite image (2x2 grid) from multiple input images.
+    Some models work better with a single composite showing multiple angles.
+    """
+    images = images[:max_images]
+    n = len(images)
+
+    if n == 1:
+        return images[0]
+
+    # Resize all to same size
+    target_size = 512
+    resized = [img.resize((target_size, target_size), Image.LANCZOS) for img in images]
+
+    if n == 2:
+        composite = Image.new("RGB", (target_size * 2, target_size))
+        composite.paste(resized[0], (0, 0))
+        composite.paste(resized[1], (target_size, 0))
+    elif n <= 4:
+        cols, rows = 2, 2
+        composite = Image.new("RGB", (target_size * cols, target_size * rows), (255, 255, 255))
+        for i, img in enumerate(resized):
+            x = (i % cols) * target_size
+            y = (i // cols) * target_size
+            composite.paste(img, (x, y))
+    else:
+        composite = resized[0]
+
+    return composite
+
+
+# ---------------------------------------------------------------------------
+# Text-to-Dimension Extraction
+# ---------------------------------------------------------------------------
+def extract_dimensions_from_prompt(prompt: str) -> dict:
+    """
+    Parse dimensional info from text prompt.
+    Supports patterns like:
+      - "10cm x 5cm x 3cm"
+      - "base is 10cm"
+      - "height: 200mm"
+      - "scale 1:10"
+    Returns dict with extracted measurements.
+    """
+    dims = {}
+
+    # Match "NxMxP" patterns (cm, mm, m, inches)
+    pattern_3d = r'(\d+(?:\.\d+)?)\s*(?:x|×|by)\s*(\d+(?:\.\d+)?)\s*(?:x|×|by)\s*(\d+(?:\.\d+)?)\s*(cm|mm|m|in|inch|inches)?'
+    match = re.search(pattern_3d, prompt, re.IGNORECASE)
+    if match:
+        dims["width"] = float(match.group(1))
+        dims["depth"] = float(match.group(2))
+        dims["height"] = float(match.group(3))
+        dims["unit"] = (match.group(4) or "cm").lower().rstrip("es").rstrip("ch")
+        return dims
+
+    # Match individual dimension patterns
+    for dim_name in ["width", "height", "depth", "length", "base", "diameter", "radius"]:
+        pattern = rf'{dim_name}\s*(?:is|:|\=)?\s*(\d+(?:\.\d+)?)\s*(cm|mm|m|in|inch|inches)?'
+        match = re.search(pattern, prompt, re.IGNORECASE)
+        if match:
+            dims[dim_name] = float(match.group(1))
+            dims["unit"] = (match.group(2) or "cm").lower().rstrip("es").rstrip("ch")
+
+    # Match scale ratios
+    scale_pattern = r'(?:scale|ratio)\s*(?:is|:|\=)?\s*1\s*:\s*(\d+(?:\.\d+)?)'
+    match = re.search(scale_pattern, prompt, re.IGNORECASE)
+    if match:
+        dims["scale_ratio"] = float(match.group(1))
+
+    return dims
+
+
+# ---------------------------------------------------------------------------
+# Mesh Post-Processing (Manifold + Scale)
+# ---------------------------------------------------------------------------
+def repair_mesh(mesh: trimesh.Trimesh) -> trimesh.Trimesh:
+    """Make a mesh manifold (watertight) for 3D printing."""
+    logger.info("Repairing mesh: %d vertices, %d faces", len(mesh.vertices), len(mesh.faces))
+
+    # Fix normals
+    trimesh.repair.fix_normals(mesh)
+    trimesh.repair.fix_inversion(mesh)
+    trimesh.repair.fix_winding(mesh)
+
+    # Fill holes
+    if not mesh.is_watertight:
+        mesh.fill_holes()
+        logger.info("Filled holes → watertight: %s", mesh.is_watertight)
+
+    # Remove degenerate faces
+    mask = mesh.nondegenerate_faces()
+    if not mask.all():
+        mesh.update_faces(mask)
+        logger.info("Removed %d degenerate faces", (~mask).sum())
+
+    # Merge close vertices
+    mesh.merge_vertices(merge_tex=True, merge_norm=True)
+
+    return mesh
+
+
+def scale_mesh(mesh: trimesh.Trimesh, dimensions: dict) -> trimesh.Trimesh:
+    """
+    Scale mesh based on extracted dimensions.
+    The model outputs are in arbitrary units — we map the largest extent
+    to the largest provided dimension.
+    """
+    if not dimensions:
+        return mesh
+
+    # Convert everything to millimeters for consistency
+    unit_to_mm = {"mm": 1.0, "cm": 10.0, "m": 1000.0, "in": 25.4}
+    unit = dimensions.get("unit", "cm")
+    multiplier = unit_to_mm.get(unit, 10.0)
+
+    # Find the target size
+    target_dims = []
+    for key in ["width", "height", "depth", "length", "base", "diameter"]:
+        if key in dimensions:
+            target_dims.append(dimensions[key] * multiplier)  # convert to mm
+
+    if not target_dims:
+        if "scale_ratio" in dimensions:
+            # Apply scale ratio directly
+            scale_factor = 1.0 / dimensions["scale_ratio"]
+            mesh.apply_scale(scale_factor)
+            return mesh
+        return mesh
+
+    # Scale the mesh so its largest extent matches the largest target dimension
+    current_extents = mesh.extents
+    max_current = max(current_extents)
+    max_target = max(target_dims)
+
+    if max_current > 0:
+        scale_factor = max_target / max_current
+        mesh.apply_scale(scale_factor)
+        logger.info("Scaled mesh by %.3f (target: %.1fmm)", scale_factor, max_target)
+
+    return mesh
+
+
+def segment_mesh_parts(mesh: trimesh.Trimesh) -> list[trimesh.Trimesh]:
+    """
+    Attempt to split a mesh into separate connected components (modular parts).
+    This is useful for complex objects where parts should be independently editable.
+    """
+    try:
+        components = mesh.split(only_watertight=False)
+        if len(components) > 1:
+            logger.info("Mesh split into %d modular parts", len(components))
+            return components
+        else:
+            logger.info("Mesh is a single connected component")
+            return [mesh]
+    except Exception as e:
+        logger.warning("Mesh segmentation failed: %s", e)
+        return [mesh]
+
+
+# ---------------------------------------------------------------------------
+# Export Utilities
+# ---------------------------------------------------------------------------
+def export_mesh(mesh: trimesh.Trimesh, fmt: str = "glb", parts: list = None) -> str:
+    """
+    Export mesh to file. If parts are provided, creates a ZIP with individual files.
+    """
+    job_id = str(uuid.uuid4())[:8]
+    output_base = OUTPUT_DIR / job_id
+    output_base.mkdir(parents=True, exist_ok=True)
+
+    if parts and len(parts) > 1:
+        # Export each part separately and zip them
+        part_files = []
+        for i, part in enumerate(parts):
+            fname = f"part_{i+1:02d}.{fmt}"
+            fpath = output_base / fname
+            part.export(str(fpath))
+            part_files.append(str(fpath))
+
+        # Also export the combined model
+        combined_path = output_base / f"combined.{fmt}"
+        mesh.export(str(combined_path))
+        part_files.append(str(combined_path))
+
+        # Create zip
+        zip_path = output_base / f"model_parts_{job_id}.zip"
+        with zipfile.ZipFile(str(zip_path), "w") as zf:
+            for pf in part_files:
+                zf.write(pf, os.path.basename(pf))
+
+        logger.info("Exported %d parts + combined to %s", len(parts), zip_path)
+        return str(combined_path), str(zip_path)
+    else:
+        fpath = output_base / f"model.{fmt}"
+        mesh.export(str(fpath))
+        logger.info("Exported single mesh to %s", fpath)
+        return str(fpath), str(fpath)
+
+
+# ---------------------------------------------------------------------------
+# Main Generation Pipeline
+# ---------------------------------------------------------------------------
+@spaces.GPU(duration=300)
+def generate_3d_model(
+    input_images: list,
+    text_prompt: str,
+    num_faces: int = DEFAULT_FACES,
+    export_format: str = "glb",
+    enable_modular: bool = False,
+    progress=gr.Progress(track_tqdm=True),
+):
+    """
+    End-to-end 3D generation pipeline.
+
+    Args:
+        input_images: List of uploaded image file paths
+        text_prompt: Text instructions (dimensions, details, style)
+        num_faces: Target face count for the mesh
+        export_format: Output format (glb, obj, stl)
+        enable_modular: Whether to split into modular parts
+    """
+    if not input_images and not text_prompt:
+        raise gr.Error("Please provide at least one image or a text prompt.")
+
+    start_time = time.time()
+    status_log = []
+
+    def log(msg):
+        status_log.append(msg)
+        logger.info(msg)
+
+    # --- Step 1: Load models ---
+    log("🔄 Loading models...")
+    load_models()
+
+    if triposg_pipeline is None:
+        raise gr.Error("Model failed to load. Please try again or check the Space logs.")
+
+    # --- Step 2: Process images ---
+    log("🖼️ Processing input images...")
+    pil_images = []
+    if input_images:
+        for img_path in input_images:
+            if isinstance(img_path, str):
+                img = Image.open(img_path).convert("RGB")
+            elif hasattr(img_path, "name"):
+                img = Image.open(img_path.name).convert("RGB")
+            else:
+                img = img_path.convert("RGB") if isinstance(img_path, Image.Image) else None
+            if img:
+                pil_images.append(img)
+
+    # --- Step 3: Remove backgrounds ---
+    log("✂️ Removing backgrounds...")
+    clean_images = [remove_background(img) for img in pil_images]
+
+    # --- Step 4: Select best view / create composite ---
+    if clean_images:
+        if len(clean_images) == 1:
+            primary_image = clean_images[0]
+            log("📷 Using single image input")
+        else:
+            primary_image = select_best_view(clean_images)
+            log(f"📷 Selected best view from {len(clean_images)} images")
+    else:
+        primary_image = None
+
+    # --- Step 5: Extract dimensions from prompt ---
+    dimensions = {}
+    if text_prompt:
+        dimensions = extract_dimensions_from_prompt(text_prompt)
+        if dimensions:
+            log(f"📏 Extracted dimensions: {dimensions}")
+
+    # --- Step 6: Run TripoSG inference ---
+    log("🧠 Running 3D reconstruction (this may take 1-3 minutes)...")
+
+    try:
+        if isinstance(triposg_pipeline, dict) and triposg_pipeline.get("type") == "cli":
+            # CLI fallback: save image, run inference script
+            model_path = triposg_pipeline["model_path"]
+            tmp_img = OUTPUT_DIR / f"input_{uuid.uuid4().hex[:6]}.png"
+
+            if primary_image:
+                primary_image.save(str(tmp_img))
+            else:
+                # Create a blank image if only text prompt
+                blank = Image.new("RGB", (512, 512), (200, 200, 200))
+                blank.save(str(tmp_img))
+
+            tmp_out = OUTPUT_DIR / f"output_{uuid.uuid4().hex[:6]}.glb"
+
+            import subprocess
+            cmd = [
+                sys.executable, "-m", "scripts.inference_triposg",
+                "--image-input", str(tmp_img),
+                "--output-path", str(tmp_out),
+                "--faces", str(num_faces),
+            ]
+            env = os.environ.copy()
+            env["PYTHONPATH"] = model_path + ":" + env.get("PYTHONPATH", "")
+
+            result = subprocess.run(cmd, capture_output=True, text=True, timeout=600, cwd=model_path, env=env)
+            if result.returncode != 0:
+                logger.error("Inference stderr: %s", result.stderr)
+                raise RuntimeError(f"Inference failed: {result.stderr[-500:]}")
+
+            mesh = trimesh.load(str(tmp_out))
+
+        else:
+            # Direct pipeline call
+            if primary_image:
+                output = triposg_pipeline(
+                    primary_image,
+                    num_faces=num_faces,
+                )
+                # The output format depends on the pipeline version
+                if hasattr(output, "meshes"):
+                    mesh = output.meshes[0]
+                elif isinstance(output, trimesh.Trimesh):
+                    mesh = output
+                else:
+                    # Try to load from file path
+                    mesh = trimesh.load(output)
+            else:
+                raise gr.Error("Image input is required for 3D generation.")
+
+    except Exception as e:
+        raise gr.Error(f"3D reconstruction failed: {str(e)}")
+
+    # --- Step 7: Post-process mesh ---
+    log("🔧 Repairing mesh for manifold output...")
+    mesh = repair_mesh(mesh)
+
+    # --- Step 8: Apply dimensional scaling ---
+    if dimensions:
+        log("📐 Applying dimensional scaling...")
+        mesh = scale_mesh(mesh, dimensions)
+
+    # --- Step 9: Modular segmentation ---
+    parts = None
+    if enable_modular:
+        log("🧩 Segmenting into modular parts...")
+        parts = segment_mesh_parts(mesh)
+
+    # --- Step 10: Export ---
+    log(f"💾 Exporting as .{export_format}...")
+    preview_path, download_path = export_mesh(mesh, export_format, parts)
+
+    elapsed = time.time() - start_time
+    log(f"✅ Complete in {elapsed:.1f}s — manifold: {mesh.is_watertight}, "
+        f"vertices: {len(mesh.vertices)}, faces: {len(mesh.faces)}")
+
+    status_text = "\n".join(status_log)
+    return preview_path, download_path, status_text
+
+
+# ---------------------------------------------------------------------------
+# Gradio API Endpoint (for n8n / external calls)
+# ---------------------------------------------------------------------------
+@spaces.GPU(duration=300)
+def api_generate(
+    image_b64_list: str,
+    prompt: str,
+    faces: int = DEFAULT_FACES,
+    fmt: str = "glb",
+    modular: bool = False,
+):
+    """
+    JSON API endpoint for programmatic access (n8n, curl, etc.).
+
+    Args:
+        image_b64_list: JSON array of base64-encoded images
+        prompt: Text instructions
+        faces: Target face count
+        fmt: Export format (glb/obj/stl)
+        modular: Split into parts
+    Returns:
+        Tuple of (model file path, download file path, status text)
+    """
+    # Decode images from base64
+    images_data = json.loads(image_b64_list) if image_b64_list else []
+    tmp_files = []
+
+    for i, b64_str in enumerate(images_data):
+        # Handle data URIs
+        if "," in b64_str:
+            b64_str = b64_str.split(",", 1)[1]
+        img_bytes = base64.b64decode(b64_str)
+        tmp_path = OUTPUT_DIR / f"api_input_{uuid.uuid4().hex[:6]}.png"
+        tmp_path.write_bytes(img_bytes)
+        tmp_files.append(str(tmp_path))
+
+    return generate_3d_model(
+        input_images=tmp_files,
+        text_prompt=prompt,
+        num_faces=faces,
+        export_format=fmt,
+        enable_modular=modular,
+    )
+
+
+# ---------------------------------------------------------------------------
+# Gradio UI
+# ---------------------------------------------------------------------------
+HEADER_MD = """
+# 🚀 AI 3D Model Generator — Print-Ready Pipeline
+
+Generate **high-fidelity, manifold 3D models** from multiple images + text instructions.
+Upload photos from different angles, add dimensional requirements, and get a
+**watertight mesh** ready for 3D printing or game engines.
+
+**Pipeline**: Background Removal → Multi-View Selection → TripoSG Reconstruction → Manifold Repair → Scale Calibration → Export
+
+> 💡 **Tip**: For best results, provide 2-4 images of the object from different angles on a clean background.
+"""
+
+with gr.Blocks(
+    title="AI 3D Model Generator",
+    theme=gr.themes.Soft(
+        primary_hue=gr.themes.colors.indigo,
+        secondary_hue=gr.themes.colors.purple,
+    ),
+    css="""
+    .gradio-container { max-width: 1200px !important; }
+    .status-box textarea { font-family: monospace; font-size: 12px; }
+    """,
+) as demo:
+
+    gr.Markdown(HEADER_MD)
+
+    with gr.Row():
+        # ----- Left Column: Inputs -----
+        with gr.Column(scale=1):
+            gr.Markdown("### 📥 Input")
+
+            input_images = gr.File(
+                label="Upload Images (multi-angle)",
+                file_count="multiple",
+                file_types=["image"],
+                type="filepath",
+            )
+
+            text_prompt = gr.Textbox(
+                label="Text Instructions",
+                placeholder=(
+                    "Describe details, dimensions, materials...\n"
+                    "e.g.: Industrial bracket, base is 10cm × 5cm, "
+                    "matte steel finish, M6 bolt holes on each corner"
+                ),
+                lines=4,
+            )
+
+            with gr.Row():
+                num_faces = gr.Slider(
+                    minimum=5000,
+                    maximum=MAX_FACES,
+                    value=DEFAULT_FACES,
+                    step=5000,
+                    label="Target Face Count",
+                )
+                export_format = gr.Dropdown(
+                    choices=["glb", "obj", "stl"],
+                    value="glb",
+                    label="Export Format",
+                )
+
+            enable_modular = gr.Checkbox(
+                label="🧩 Split into modular parts (experimental)",
+                value=False,
+            )
+
+            generate_btn = gr.Button(
+                "🚀 Generate 3D Model",
+                variant="primary",
+                size="lg",
+            )
+
+        # ----- Right Column: Outputs -----
+        with gr.Column(scale=1):
+            gr.Markdown("### 📤 Output")
+
+            model_preview = gr.Model3D(
+                label="3D Model Preview",
+                clear_color=[0.1, 0.1, 0.15, 1.0],
+            )
+
+            download_file = gr.File(label="⬇️ Download Model")
+
+            status_output = gr.Textbox(
+                label="Pipeline Status",
+                lines=10,
+                interactive=False,
+                elem_classes=["status-box"],
+            )
+
+    # ----- Hidden API interface -----
+    with gr.Row(visible=False):
+        api_image_input = gr.Textbox(label="Base64 Images JSON")
+        api_prompt_input = gr.Textbox(label="Prompt")
+        api_faces_input = gr.Number(label="Faces", value=DEFAULT_FACES)
+        api_fmt_input = gr.Textbox(label="Format", value="glb")
+        api_modular_input = gr.Checkbox(label="Modular", value=False)
+        api_model_output = gr.File(label="Model")
+        api_download_output = gr.File(label="Download")
+        api_status_output = gr.Textbox(label="Status")
+
+    # ----- Event handlers -----
+    generate_btn.click(
+        fn=generate_3d_model,
+        inputs=[input_images, text_prompt, num_faces, export_format, enable_modular],
+        outputs=[model_preview, download_file, status_output],
+    )
+
+    # Named API endpoint for n8n
+    demo.load(fn=lambda: None, inputs=None, outputs=None)
+
+
+# ---------------------------------------------------------------------------
+# Launch
+# ---------------------------------------------------------------------------
+if __name__ == "__main__":
+    demo.queue(max_size=5)
+    demo.launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        share=False,
+        show_api=True,  # Expose /api/ endpoints for n8n
+    )