v1

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

Dockerfile

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+# copiado do build do hugging face em  27/12/2025
+
+FROM docker.io/library/python:3.10@sha256:ad84630de0e8b6f2ee92b4e2996b08c269efa96be13558d0233ed08a0e190606
+	
+# COPY --from=root / /
+COPY requirements.txt .
+
+WORKDIR /app
+
+
+RUN apt-get update && apt-get install -y git git-lfs ffmpeg libsm6 	libxext6 	cmake 	rsync 	libgl1 	\
+	&& rm -rf /var/lib/apt/lists/* 	\
+	&& git lfs install
+RUN pip install --no-cache-dir pip -U && pip install --no-cache-dir datasets "huggingface-hub>=0.30" "hf-transfer>=0.1.4" "protobuf<4" "click<8.1" "pydantic~=1.0"
+RUN apt-get update && 	apt-get install -y curl && 	\
+	curl -fsSL https://deb.nodesource.com/setup_20.x | bash - && \
+	apt-get install -y nodejs && \	
+	rm -rf /var/lib/apt/lists/* && \
+	apt-get clean
+
+
+RUN --mount=target=/tmp/requirements.txt,source=requirements.txt     pip install --no-cache-dir -r /tmp/requirements.txt
+
+# instala o gradio a partir do readm!
+COPY InstallFromReadme.sh .
+COPY README.md .
+RUN chmod +x InstallFromReadme.sh
+RUN ./InstallFromReadme.sh
+
+RUN pip install --no-cache-dir "uvicorn>=0.14.0" spaces
+
+
+RUN mkdir -p /home/user && ( [ -e /home/user/app ] || ln -s /app/ /home/user/app ) || true
+
+# -- o hf faz um --link.
+COPY . /app
+
+CMD ["python","app.py"]

InstallFromReadme.sh

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+version=$(cat README.md | grep -E "sdk_version\: (.+)" | cut -d " " -f 2)
+
+echo "Installing gradio version $version";
+
+pip install gradio==$version

README.md

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+---
+title: SQL ONNX Info
+emoji: ➡️
+colorFrom: blue
+colorTo: yellow
+sdk: gradio
+sdk_version: 6.2.0
+pinned: false
+app_port: 8080
+---
+
+Returns information about supported ONNX models in SQL Server!
+
+

app.py

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+import gradio as gr
+import onnx
+from huggingface_hub import HfApi
+import json
+import sys
+import os
+import io
+import requests
+from urllib.parse import urlparse
+
+
+HfClient = HfApi();
+
+ONNX_PREFERED = [
+    "model.onnx",
+    "onnx/model.onnx"    
+]
+
+ONNX_CACHE = {
+    'models': {
+        
+    }
+}
+
+def is_url(path):
+    try:
+        result = urlparse(path)
+        return all([result.scheme, result.netloc])
+    except ValueError:
+        return False
+
+
+def load_model(path):
+    if is_url(path):
+        print(f"Downloading model from: {path}...", file=sys.stderr)
+        try:
+            response = requests.get(path)
+            response.raise_for_status() # Check for HTTP errors
+            # Load from binary stream
+            return onnx.load(io.BytesIO(response.content))
+        except requests.exceptions.RequestException as e:
+            print(f"Error downloading model: {e}", file=sys.stderr)
+            sys.exit(1)
+    else:
+        # Check if local file exists before loading
+        if not os.path.exists(path):
+            print(f"Error: File not found at {path}", file=sys.stderr)
+            sys.exit(1)
+        
+        return onnx.load(path)
+
+def CheckSqlOnnx(path):
+    
+    OnnxModel = load_model(path);
+    
+    initializer_names = {init.name for init in OnnxModel.graph.initializer}
+    inputs = [inp.name for inp in OnnxModel.graph.input if inp.name not in initializer_names]
+    outputs = [out.name for out in OnnxModel.graph.output]
+
+    required_inputs = {"input_ids", "attention_mask"}
+    required_outputs = {"token_embeddings", "sentence_embedding"}
+
+    is_supported = (
+        required_inputs.issubset(inputs) and
+        required_outputs.issubset(outputs)
+    )
+
+    OnnxInouts = {
+        "supported": is_supported,
+        "inputs": inputs,
+        "outputs": outputs
+    }
+    
+    return OnnxInouts;
+
+def CheckModel(repo_id: str, path: str | None = None):
+    
+    MODELS_CACHE = ONNX_CACHE['models'];
+    
+    CacheSlot = MODELS_CACHE.get(repo_id);
+    
+    if CacheSlot:
+        return json.dumps(CacheSlot, indent = 2);
+    
+    
+    
+    model_info = HfClient.model_info(repo_id=repo_id)
+
+    # Extract filenames from RepoSibling objects
+    sibling_files = [s.rfilename for s in model_info.siblings]
+
+    onnx_path = None
+
+    if path:
+        if path in sibling_files:
+            onnx_path = path
+        else:
+            return f"Error: ONNX file not found: {path}"
+    else:
+        for p in ONNX_PREFERED:
+            if p in sibling_files:
+                onnx_path = p
+                break
+
+        if not onnx_path:
+            onnx_path = next(
+                (f for f in sibling_files if f.lower().endswith(".onnx")),
+                None
+            )
+
+    if not onnx_path:
+        raise "Error: No ONNX model found in repository";
+
+    # Build Hugging Face raw file URL
+    file_url = f"https://huggingface.co/{repo_id}/resolve/main/{onnx_path}"
+
+    # Check SQL ONNX compatibility
+    OnnxInfo = CheckSqlOnnx(file_url)
+    
+    CacheSlot = {
+         'url': file_url
+        ,'onnx': OnnxInfo
+    }
+    
+    MODELS_CACHE[repo_id] = CacheSlot
+
+    return json.dumps({**CacheSlot, 'cached': False}, indent = 2);
+
+
+with gr.Blocks() as demo:
+    
+    gr.Markdown("""
+        This sample app test if a given model repository can be used with SQL Server ONNX.  
+        In some tests, discovered that is not any ONNX model that works with sql server CREATE EXTERNAL MODEL.  
+        For works, the input parameters of neural network must contains specific names, and output also.  
+        I dont know if this behavior will be ketp in future verisons of SQL 2025...  
+        
+        But, while we dont have official doc about this, this repo can help discovery if a given model will work with sql server if you plan download to use with ONNX.  
+        Just input the model name bellow in format user/model-name (check examples).  
+        
+        Look at JSON output. If "supported" is True, then you can use with SQL...
+        
+        Soon bring a default tested list!
+        
+        **IMPORTANT**: To check, this space will attempt donwload the model onnx file. If is big can take several minutes.
+    """)
+    
+    ModelPath = gr.Textbox(label="Model Repository", submit_btn = True);
+    ModelInfoOut = gr.Textbox(label="Model Info", lines = 10)
+    
+    ModelPath.submit(fn=CheckModel, inputs=ModelPath, outputs=ModelInfoOut)
+    
+    gr.Examples([
+         ["intfloat/multilingual-e5-large"]
+        ,["mixedbread-ai/mxbai-embed-xsmall-v1"]
+        ,["nsense/all-MiniLM-L6-v2-onnx"]
+    
+    ], ModelPath)
+
+demo.launch(
+    server_name = '0.0.0.0'
+)

docker-compose.yml

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+volumes:
+    hfdata:
+
+services:
+    app:
+        build: .
+        ports:
+            - 8080:7860
+        stdin_open: true # docker run -i
+        tty: true        # docker run -t
+        deploy:
+          resources:
+            reservations:
+              devices:
+                - driver: nvidia
+                  count: all
+                  capabilities: [gpu]

requirements.txt

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+huggingface_hub
+gradio-client
+onnx