Permalink Update

app.py

@@ -34,9 +34,12 @@ import gc
 # Import functions from modules
 from utils.file_utils import (
     cleanup_temp_files, 
-    get_file_parts, 
-    generate_permalink_from_urls
+    get_file_parts
 )
+from utils.storage import (
+    generate_permalink_from_urls,
+    upload_files_to_repo,
+    )
 
 from utils.color_utils import (
     hex_to_rgb,
@@ -133,6 +136,7 @@ user_dir = constants.TMPDIR
 lora_models = get_lora_models()
 selected_index = gr.State(value=-1)
 #html_versions = version_info.versions_html()
+default_folder = "saved_models/3d_model_" + format(random.randint(1, 999999), "06d")
 
 image_processor: Optional[DPTImageProcessor] = None
 depth_model: Optional[DPTForDepthEstimation] = None
@@ -804,7 +808,7 @@ def load_trellis_model():
     loaded = False
     if TRELLIS_PIPELINE == None:
         try:
-            TRELLIS_PIPELINE = TrellisImageTo3DPipeline.from_pretrained("jetx/TRELLIS-image-large")
+            TRELLIS_PIPELINE = TrellisImageTo3DPipeline.from_pretrained("Surn/TRELLIS-image-large")
             TRELLIS_PIPELINE.cuda()
             # Preload with a dummy image to finalize initialization
             try:
@@ -1175,6 +1179,42 @@ def update_permalink(glb, gaussian, depth_out, depth_src_file):
     permalink = generate_permalink_from_urls(smallest_file, depth_out, depth_src_file)
     return gr.update(visible=True), permalink
 
+def create_permalink(glb, gaussian, depth_out, depth_src_file, folder_name_permalink):
+    """
+    create a permalink for the 3D model and depth image.
+    Args:
+        glb (str): Path to the extracted GLB file.
+        gaussian (str): Path to the extracted Gaussian file.
+        depth_out (str): File path for the depth image.
+        depth_src_file (str): File path for the image used to generate the 3D model.
+        folder_path (str): The folder path where the files are stored.
+    Returns:
+        str: The permalink URL.
+    """
+    file_candidates = {}
+    if glb and os.path.exists(glb):
+        file_candidates[glb] = os.path.getsize(glb)
+    if gaussian and os.path.exists(gaussian):
+        file_candidates[gaussian] = os.path.getsize(gaussian)
+
+    # if not file_candidates:
+    #     return gr.update(visible=False), ""
+    smallest_file = min(file_candidates, key=file_candidates.get)
+
+    permalink = upload_files_to_repo(
+            files=[smallest_file, depth_out, depth_src_file],
+            repo_id="Surn/Storage",
+            folder_name=folder_name_permalink,
+            create_permalink=True,
+            repo_type="dataset"
+        )[1]
+    return permalink
+
+def open_permalink(url: str) -> str:
+    if url and url.strip():
+        return f'<script>window.open("{url}", "_blank");</script>'
+    return '<script>alert("Permalink textbox is empty!");</script>'
+
 @spaces.GPU()
 def getVersions():
     #return html_versions
@@ -1595,9 +1635,28 @@ with gr.Blocks(css_paths="style_20250314.css", title=title, theme='Surn/beeuty',
                             gr.Markdown("""
                             ### Files over 10 MB may not display in the 3D model viewer
                             """, elem_id="file_size_info", elem_classes="intro" )
+  
+                        # New UI elements for general file upload and permalink generation
+                        with gr.Column(scale=1):
+                            folder_name_permalink = gr.Textbox(
+                                label="Folder Name in Permalink Repo",
+                                placeholder="e.g., my_cool_model_set",
+                                value=default_folder, # Default folder name
+                                elem_classes="solid small centered"
+                            )
+                            permalink_button = gr.Button(
+                                "Generate Permalink with Folder Name in Repo",
+                                elem_classes="solid small centered"
+                            )
                             with gr.Row(visible=False) as permalink_row:
-                                permalink_button = gr.Button("View in Permalink", elem_classes="solid small centered", variant="secondary")
-                                permalink_text = gr.Textbox(label="Permalink", elem_classes="solid small centered")
+                               permalink = gr.Textbox(
+                                    label="Viewer Permalink",
+                                    show_copy_button=True,
+                                    elem_classes="solid small centered",
+                                    max_lines=3
+                                )
+                               view_permalink_button = gr.Button("View in Permalink", elem_classes="solid small centered", variant="secondary")
+                               hidden_html = gr.HTML(visible=False)
 
     is_multiimage = gr.State(False)
     output_buf = gr.State()
@@ -1847,8 +1906,8 @@ with gr.Blocks(css_paths="style_20250314.css", title=title, theme='Surn/beeuty',
         outputs=[glb_file]
     ).then(
         fn=update_permalink,
-        inputs=[glb_file, gaussian_file, depth_output, ddd_image_path],  # ddd_image_path now holds the source file
-        outputs=[permalink_row, permalink_text]
+        inputs=[glb_file, gaussian_file, depth_output, ddd_image_path],  # Use the actual depth source file here
+        outputs=[permalink_row, permalink] # This updates the GLB/Gaussian specific permalink
     )
 
     extract_gaussian_btn.click(
@@ -1861,9 +1920,29 @@ with gr.Blocks(css_paths="style_20250314.css", title=title, theme='Surn/beeuty',
     ).then(
         fn=update_permalink,
         inputs=[glb_file, gaussian_file, depth_output, ddd_image_path],  # Use the actual depth source file here
-        outputs=[permalink_row, permalink_text]
+        outputs=[permalink_row, permalink] # This updates the GLB/Gaussian specific permalink
     )
 
+    # Create a permalink based on the current model, images, and folder name.
+    permalink_button.click(
+        fn=create_permalink,
+        inputs=[glb_file, gaussian_file, depth_output, ddd_image_path, folder_name_permalink],
+        outputs=[permalink],
+        scroll_to_output=True
+    ).then(
+        lambda link: gr.update(visible=True) if link and len(link) > 0 else gr.update(visible=False),
+        inputs=[permalink],
+        outputs=[permalink_row]
+    )
+
+    view_permalink_button.click(
+        fn=open_permalink,
+        inputs=[permalink],
+        outputs=[hidden_html],
+        show_progress=False
+    )
+
+
 if __name__ == "__main__":
     constants.load_env_vars(constants.dotenv_path)
     logging.basicConfig(
@@ -1896,7 +1975,7 @@ if __name__ == "__main__":
     # image_processor = DPTImageProcessor.from_pretrained("Intel/dpt-large")
     # depth_model = DPTForDepthEstimation.from_pretrained("Intel/dpt-large", ignore_mismatched_sizes=True)
     if constants.IS_SHARED_SPACE:
-        TRELLIS_PIPELINE = TrellisImageTo3DPipeline.from_pretrained("jetx/TRELLIS-image-large")
+        TRELLIS_PIPELINE = TrellisImageTo3DPipeline.from_pretrained("Surn/TRELLIS-image-large")
         TRELLIS_PIPELINE.to(device)
         try:
            TRELLIS_PIPELINE.preprocess_image(Image.fromarray(np.zeros((512, 512, 3), dtype=np.uint8)), 512, True)    # Preload rembg

requirements.txt

@@ -73,6 +73,6 @@ https://github.com/Dao-AILab/flash-attention/releases/download/v2.7.3/flash_attn
 https://huggingface.co/spaces/Surn/HexaGrid/resolve/main/wheels/diff_gaussian_rasterization-0.0.0-cp310-cp310-linux_x86_64.whl?download=true
 https://huggingface.co/spaces/Surn/Hexagrid/resolve/main/wheels/nvdiffrast-0.3.3-cp310-cp310-linux_x86_64.whl?download=true
 #Windows only
-#https://huggingface.co/spaces/Surn/HexaGrid/main/wheels/flash_attn-2.7.4.post1-cp312-cp312-win_amd64.whl?download=true
-#https://huggingface.co/spaces/Surn/HexaGrid/main/wheels/diff_gaussian_rasterization-0.0.0-cp312-cp312-win_amd64.whl?download=true
-#https://huggingface.co/spaces/Surn/HexaGrid/main/wheels/nvdiffrast-0.3.3-py3-none-any.whl?download=true
+#https://huggingface.co/spaces/Surn/HexaGrid/resolve/main/wheels/flash_attn-2.7.4.post1-cp312-cp312-win_amd64.whl?download=true
+#https://huggingface.co/spaces/Surn/HexaGrid/resolve/main/wheels/diff_gaussian_rasterization-0.0.0-cp312-cp312-win_amd64.whl?download=true
+#https://huggingface.co/spaces/Surn/HexaGrid/resolve/main/wheels/nvdiffrast-0.3.3-py3-none-any.whl

utils/constants.py

@@ -67,6 +67,12 @@ SCALE_FACTOR = (12/5)
 TMPDIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'tmp')
 os.makedirs(TMPDIR, exist_ok=True)
 
+model_extensions = {".glb", ".gltf", ".obj", ".ply"}
+model_extensions_list = list(model_extensions)
+image_extensions = {".png", ".jpg", ".jpeg", ".webp"}
+image_extensions_list = list(image_extensions)
+upload_file_types = model_extensions_list + image_extensions_list
+
 
 PROMPTS = {
     "Mecha Wasteland Arena": "Regional overhead view, directly from above, centered on the map, orthographic Mecha battlefield map. post-industrial wasteland with crumbling structures, volcanic ridges, scrapyards, and ash plains. Features elevated overwatch positions for long-range combat and tight brawling areas for close-quarters engagements. Partial edge hexes are black. Colors: red, gray, muted orange, ash white, dark brown.",

utils/file_utils.py

@@ -103,23 +103,4 @@ def get_unique_file_path(directory, filename, file_ext, counter=0):
         return get_unique_file_path(directory, filename, file_ext, counter + 1)
 
 # Example usage:
-# new_file_path = get_unique_file_path(video_dir, title_file_name, video_new_ext)
-
-def generate_permalink_from_urls(model_url, hm_url, img_url, permalink_viewer_url="surn-3d-viewer.hf.space"):
-    """
-    Constructs and returns a permalink URL with query string parameters for the viewer.
-    Each parameter is passed separately so that the image positions remain consistent.
-    
-    Parameters:
-        model_url (str): Processed URL for the 3D model.
-        hm_url (str): Processed URL for the height map image.
-        img_url (str): Processed URL for the main image.
-        permalink_viewer_url (str): The base viewer URL.
-    
-    Returns:
-        str: The generated permalink URL.
-    """
-    import urllib.parse
-    params = {"3d": model_url, "hm": hm_url, "image": img_url}
-    query_str = urllib.parse.urlencode(params)
-    return f"https://{permalink_viewer_url}/?{query_str}"
+# new_file_path = get_unique_file_path(video_dir, title_file_name, video_new_ext)

utils/storage.py

@@ -0,0 +1,127 @@
+# utils/storage.py
+import os
+import urllib.parse
+import tempfile
+import shutil
+from huggingface_hub import login, upload_folder
+from utils.constants import HF_API_TOKEN, upload_file_types, model_extensions, image_extensions
+
+def generate_permalink(valid_files, base_url_external, permalink_viewer_url="surn-3d-viewer.hf.space"):
+    """
+    Given a list of valid files, checks if they contain exactly 1 model file and 2 image files.
+    Constructs and returns a permalink URL with query parameters if the criteria is met.
+    Otherwise, returns None.
+    """
+    model_link = None
+    images_links = []
+    for f in valid_files:
+        filename = os.path.basename(f)
+        ext = os.path.splitext(filename)[1].lower()
+        if ext in model_extensions:
+            if model_link is None:
+                model_link = f"{base_url_external}/{filename}"
+        elif ext in image_extensions:
+            images_links.append(f"{base_url_external}/{filename}")
+    if model_link and len(images_links) == 2:
+        # Construct a permalink to the viewer project with query parameters.
+        permalink_viewer_url = f"https://{permalink_viewer_url}/"
+        params = {"3d": model_link, "hm": images_links[0], "image": images_links[1]}
+        query_str = urllib.parse.urlencode(params)
+        return f"{permalink_viewer_url}?{query_str}"
+    return None
+
+def generate_permalink_from_urls(model_url, hm_url, img_url, permalink_viewer_url="surn-3d-viewer.hf.space"):
+    """
+    Constructs and returns a permalink URL with query string parameters for the viewer.
+    Each parameter is passed separately so that the image positions remain consistent.
+    
+    Parameters:
+        model_url (str): Processed URL for the 3D model.
+        hm_url (str): Processed URL for the height map image.
+        img_url (str): Processed URL for the main image.
+        permalink_viewer_url (str): The base viewer URL.
+    
+    Returns:
+        str: The generated permalink URL.
+    """
+    import urllib.parse
+    params = {"3d": model_url, "hm": hm_url, "image": img_url}
+    query_str = urllib.parse.urlencode(params)
+    return f"https://{permalink_viewer_url}/?{query_str}"
+
+def upload_files_to_repo(files, repo_id, folder_name, create_permalink=False, repo_type="dataset", permalink_viewer_url="surn-3d-viewer.hf.space"):
+    """
+    Uploads multiple files to a Hugging Face repository using a batch upload approach via upload_folder.
+
+    Parameters:
+        files (list): A list of file paths (str) to upload.
+        repo_id (str): The repository ID on Hugging Face for storage, e.g. "Surn/Storage".
+        folder_name (str): The subfolder within the repository where files will be saved.
+        create_permalink (bool): If True and if exactly three files are uploaded (1 model and 2 images),
+                                 returns a single permalink to the project with query parameters.
+                                 Otherwise, returns individual permalinks for each file.
+        repo_type (str): Repository type ("space", "dataset", etc.). Default is "dataset".
+
+    Returns:
+        If create_permalink is True and files match the criteria:
+            tuple: (response, permalink) where response is the output of the batch upload
+                   and permalink is the URL string (with fully qualified file paths) for the project.
+        Otherwise:
+            list: A list of tuples (response, permalink) for each file.
+    """
+    # Log in using the HF API token.
+    login(token=HF_API_TOKEN)
+    
+    valid_files = []
+    
+    # Ensure folder_name does not have a trailing slash.
+    folder_name = folder_name.rstrip("/")
+    
+    # Filter for valid files based on allowed extensions.
+    for f in files:
+        file_name = f if isinstance(f, str) else f.name if hasattr(f, "name") else None
+        if file_name is None:
+            continue
+        ext = os.path.splitext(file_name)[1].lower()
+        if ext in upload_file_types:
+            valid_files.append(f)
+    
+    if not valid_files:
+        return []  # or raise an exception
+    
+    # Create a temporary directory; copy valid files directly into it.
+    with tempfile.TemporaryDirectory() as temp_dir:
+        for file_path in valid_files:
+            filename = os.path.basename(file_path)
+            dest_path = os.path.join(temp_dir, filename)
+            shutil.copy(file_path, dest_path)
+        
+        # Batch upload all files in the temporary folder.
+        # Files will be uploaded under the folder (path_in_repo) given by folder_name.
+        response = upload_folder(
+            folder_path=temp_dir,
+            repo_id=repo_id,
+            repo_type=repo_type,
+            path_in_repo=folder_name,
+            commit_message="Batch upload files"
+        )
+    
+    # Construct external URLs for each uploaded file.
+    # For datasets, files are served at:
+    # https://huggingface.co/datasets/<repo_id>/resolve/main/<folder_name>/<filename>
+    base_url_external = f"https://huggingface.co/datasets/{repo_id}/resolve/main/{folder_name}"
+    individual_links = []
+    for file_path in valid_files:
+        filename = os.path.basename(file_path)
+        link = f"{base_url_external}/{filename}"
+        individual_links.append(link)
+    
+    # If permalink creation is requested and exactly 3 valid files are provided,
+    # try to generate a permalink using generate_permalink().
+    if create_permalink and len(valid_files) == 3:
+        permalink = generate_permalink_from_urls(individual_links[0], individual_links[1], individual_links[2], permalink_viewer_url)
+        if permalink:
+            return [response, permalink]
+
+    # Otherwise, return individual tuples for each file.
+    return [(response, link) for link in individual_links]