chore(ml): remove machine learning model files

.gitattributes

@@ -1,6 +0,0 @@
-best_model/best_model.keras filter=lfs diff=lfs merge=lfs -text
-model/saved_model/variables/variables.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -text
-model/tfjs_model/*.bin filter=lfs diff=lfs merge=lfs -text
-**/best_model.keras filter=lfs diff=lfs merge=lfs -text
-**/variables.data* filter=lfs diff=lfs merge=lfs -text
-**/tfjs_model/*.bin filter=lfs diff=lfs merge=lfs -text

.github/workflows/deploy.yml

@@ -28,8 +28,6 @@ jobs:
     steps:
       - name: Checkout repository
         uses: actions/checkout@v4
-        with:
-          lfs: true
 
       - name: Set image prefix
         run: echo "IMAGE_PREFIX=ghcr.io/${GITHUB_REPOSITORY,,}" >> "$GITHUB_ENV"
@@ -37,19 +35,19 @@ jobs:
       - name: Set up Docker Buildx
         uses: docker/setup-buildx-action@v3
 
-      - name: Set up Python for ONNX conversion
+      - name: Download ONNX model from Hugging Face Hub
         if: matrix.service.name == 'ml'
-        uses: actions/setup-python@v5
+        env:
-        with:
+          HF_TOKEN: ${{ secrets.HUGGINGFACE_TOKEN }}
-          python-version: '3.11'
-
-      - name: Generate ONNX model artifact
-        if: matrix.service.name == 'ml'
-        working-directory: Machine_Learning
         run: |
-          python -m pip install --upgrade pip
+          mkdir -p Machine_Learning/model
-          python -m pip install 'tensorflow>=2.13.0' 'tf2onnx>=1.16.1' 'onnx>=1.16.0'
+          if [ -n "$HF_TOKEN" ]; then
-          python convert_onnx.py
+            AUTH_HEADER="-H Authorization: Bearer $HF_TOKEN"
+          fi
+          curl -fL $AUTH_HEADER \
+            "https://huggingface.co/MythEclipse2737/zeavis-edu-model/resolve/main/model/model.onnx" \
+            -o "Machine_Learning/model/model.onnx"
+          ls -lh Machine_Learning/model/model.onnx
 
       - name: Log in to GHCR
         uses: docker/login-action@v3

MEMORY.md

@@ -0,0 +1,27 @@
+# Durable Memory Wiki
+
+Consolidated knowledge and long-term facts.
+
+## Core Learnings
+
+- **Topic:** error response daemon
+  **Context:** Failure observation: Exit code 1
+Error response from daemon: No such container: telemetry-prometheus
+  *Promoted on:* 2026-06-11T12:19:50.638Z
+
+- **Topic:** error 32603 pattern
+  **Context:** Failure observation: MCP error -32603: pattern must be a non-empty string.
+  *Promoted on:* 2026-06-11T12:19:50.638Z
+
+- **Topic:** traceback recent string
+  **Context:** Failure observation: Exit code 1
+Traceback (most recent call last):
+  File "<string>", line 25, in <module>
+    with urllib.request.urlopen(req) as resp:
+         ~~~~~~~~~~~~~~~~~~~~~~^^^^^
+  File "/usr/lib/python3.14/ur
+  *Promoted on:* 2026-06-11T12:58:56.451Z
+
+- **Topic:** exist current working
+  **Context:** Failure observation: File does not exist. Note: your current working directory is /mnt/code/ZeaVis-Edu.
+  *Promoted on:* 2026-06-11T13:03:39.178Z

Machine_Learning/.gitignore

@@ -0,0 +1,9 @@
+# Model artifacts — not stored in git. Uploaded to Hugging Face Hub after training.
+best_model/
+model/
+dataset/
+dataset.zip
+venv/
+__pycache__/
+*.pyc
+*.pyo

Machine_Learning/best_model/best_model.keras

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:4e8e3a4f86ae1594aeb221c40db3bc80e87c05b05f386bbb628ff1da16397424
-size 112903200

Machine_Learning/download_model.sh

@@ -0,0 +1,43 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+# Download best_model.keras from Hugging Face Hub.
+#
+# Usage:
+#   bash download_model.sh                 # public repo — no auth needed
+#   HF_TOKEN=hf_xxx bash download_model.sh # private repo
+
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+DEST="${SCRIPT_DIR}/best_model/best_model.keras"
+REPO_ID="MythEclipse2737/zeavis-edu-model"
+
+: "${HF_TOKEN:=}"
+
+FORCE="${1:-}"
+
+if [ -f "$DEST" ] && [ "$FORCE" != "--force" ]; then
+    echo "Model already exists at $DEST (use --force to overwrite)"
+    exit 0
+fi
+
+mkdir -p "$(dirname "$DEST")"
+
+if command -v huggingface-cli &>/dev/null; then
+    huggingface-cli download "$REPO_ID" "best_model/best_model.keras" --local-dir "$(dirname "$DEST")" $( [ -n "$HF_TOKEN" ] && echo "--token $HF_TOKEN" )
+elif command -v hf &>/dev/null; then
+    hf download "$REPO_ID" "best_model/best_model.keras" --output "$DEST"
+elif command -v curl &>/dev/null; then
+    URL="https://huggingface.co/${REPO_ID}/resolve/main/best_model/best_model.keras"
+    curl -fL -o "$DEST" $( [ -n "$HF_TOKEN" ] && echo "-H Authorization: Bearer $HF_TOKEN" ) "$URL"
+else
+    echo "ERROR: Need curl or huggingface-cli installed."
+    exit 1
+fi
+
+if [ -f "$DEST" ]; then
+    echo "Downloaded: $DEST"
+    ls -lh "$DEST"
+else
+    echo "ERROR: Download failed."
+    exit 1
+fi

Machine_Learning/model/saved_model/fingerprint.pb

@@ -1 +0,0 @@
-©©ءّ¸ث‡ق¬
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 ’ڑھ¸¥ؤي—·
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:10851638082828504866

Machine_Learning/model/saved_model/variables/variables.data-00000-of-00001

@@ -1,3 +0,0 @@
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-size 53214594

Machine_Learning/model/tfjs_model/group1-shard10of13.bin

@@ -1,3 +0,0 @@
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-oid sha256:613179718d653f027cf5e70a02f223b423a98abaffe7120ef626793aec23177f
-size 4194304

Machine_Learning/model/tfjs_model/group1-shard11of13.bin

@@ -1,3 +0,0 @@
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Machine_Learning/model/tfjs_model/group1-shard12of13.bin

@@ -1,3 +0,0 @@
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Machine_Learning/model/tfjs_model/group1-shard13of13.bin

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Machine_Learning/model/tfjs_model/group1-shard1of13.bin

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Machine_Learning/model/tfjs_model/group1-shard2of13.bin

@@ -1,3 +0,0 @@
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Machine_Learning/model/tfjs_model/group1-shard3of13.bin

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Machine_Learning/model/tfjs_model/group1-shard4of13.bin

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Machine_Learning/model/tfjs_model/group1-shard5of13.bin

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Machine_Learning/model/tfjs_model/group1-shard6of13.bin

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Machine_Learning/model/tfjs_model/group1-shard7of13.bin

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Machine_Learning/model/tfjs_model/group1-shard8of13.bin

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Machine_Learning/model/tfjs_model/group1-shard9of13.bin

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Machine_Learning/notebook.ipynb

@@ -74,7 +74,7 @@
   },
   {
    "cell_type": "code",
-      "execution_count": 3,
+   "execution_count": 4,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -134,7 +134,7 @@
   },
   {
    "cell_type": "code",
-      "execution_count": 4,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -190,7 +190,7 @@
   },
   {
    "cell_type": "code",
-      "execution_count": 5,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -256,7 +256,7 @@
   },
   {
    "cell_type": "code",
-      "execution_count": 6,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -328,7 +328,7 @@
   },
   {
    "cell_type": "code",
-      "execution_count": 7,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -383,7 +383,7 @@
   },
   {
    "cell_type": "code",
-      "execution_count": 8,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -601,7 +601,7 @@
   },
   {
    "cell_type": "code",
-      "execution_count": 9,
+   "execution_count": null,
    "metadata": {
     "id": "s4Trlvno3wny"
    },
@@ -644,7 +644,7 @@
   },
   {
    "cell_type": "code",
-      "execution_count": 10,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -724,7 +724,7 @@
   },
   {
    "cell_type": "code",
-      "execution_count": 11,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -781,7 +781,7 @@
   },
   {
    "cell_type": "code",
-      "execution_count": 12,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -851,7 +851,7 @@
   },
   {
    "cell_type": "code",
-      "execution_count": 13,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -922,6 +922,44 @@
     "plt.tight_layout()\n",
     "plt.show()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ──────────────────────────────────────────────\n",
+    "# 14. Upload Model ke Hugging Face Hub\n",
+    "# ──────────────────────────────────────────────\n",
+    "print(\"Mengunggah model ke Hugging Face Hub...\")\n",
+    "try:\n",
+    "    from huggingface_hub import HfApi, login\n",
+    "    import os\n",
+    "\n",
+    "    hf_token = os.environ.get(\"HF_TOKEN\")\n",
+    "    if hf_token:\n",
+    "        login(token=hf_token, add_to_git_credential=False)\n",
+    "\n",
+    "    api = HfApi()\n",
+    "    model_path = \"/content/best_model/best_model.keras\"\n",
+    "    repo_id = \"MythEclipse2737/zeavis-edu-model\"\n",
+    "\n",
+    "    if os.path.exists(model_path):\n",
+    "        api.upload_file(\n",
+    "            path_or_fileobj=model_path,\n",
+    "            path_in_repo=\"best_model/best_model.keras\",\n",
+    "            repo_id=repo_id,\n",
+    "            repo_type=\"model\",\n",
+    "        )\n",
+    "        print(f\"✅ Model berhasil diunggah ke https://huggingface.co/{repo_id}\")\n",
+    "    else:\n",
+    "        print(f\"⚠️  {model_path} tidak ditemukan, upload dilewati.\")\n",
+    "except ImportError:\n",
+    "    print(\"⚠️  huggingface_hub tidak terinstal. Jalankan: pip install huggingface_hub\")\n",
+    "except Exception as e:\n",
+    "    print(f\"⚠️  Upload gagal: {e}\")"
+   ]
   }
  ],
  "metadata": {
@@ -931,11 +969,21 @@
    "provenance": []
   },
   "kernelspec": {
-      "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
    "name": "python3"
   },
   "language_info": {
-      "name": "python"
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.13"
   }
  },
  "nbformat": 4,

Machine_Learning/upload_model.sh

@@ -0,0 +1,62 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+# Upload exported model artifacts (ONNX, TFLite) to Hugging Face Hub.
+#
+# Usage:
+#   bash upload_model.sh
+#   HF_TOKEN=hf_xxx bash upload_model.sh
+
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+REPO_ID="MythEclipse2737/zeavis-edu-model"
+: "${HF_TOKEN:=}"
+: "${HF_USER:=MythEclipse2737}"
+
+echo "Uploading model artifacts to Hugging Face Hub..."
+
+if ! command -v huggingface-cli &>/dev/null && ! python3 -c "from huggingface_hub import HfApi" 2>/dev/null; then
+    echo "ERROR: huggingface_hub not installed. pip install huggingface_hub"
+    exit 1
+fi
+
+python3 << PYEOF
+import os, sys
+sys.path.insert(0, "${SCRIPT_DIR}")
+
+from huggingface_hub import HfApi
+
+api = HfApi(token="${HF_TOKEN}" if "${HF_TOKEN}" else None)
+
+model_dir = "${SCRIPT_DIR}/model"
+onnx = os.path.join(model_dir, "model.onnx")
+tflite = os.path.join(model_dir, "model.tflite")
+saved_model_pb = os.path.join(model_dir, "saved_model", "saved_model.pb")
+repo_id = "${REPO_ID}"
+
+files_to_upload = []
+for local, remote in [
+    (onnx, "model/model.onnx"),
+    (tflite, "model/model.tflite"),
+    (saved_model_pb, "model/saved_model/saved_model.pb"),
+]:
+    if os.path.isfile(local):
+        files_to_upload.append((local, remote))
+        print(f"  Queued: {local} -> {remote}")
+    else:
+        print(f"  Skip (not found): {local}")
+
+for local, remote in files_to_upload:
+    print(f"  Uploading {remote}...")
+    api.upload_file(
+        path_or_fileobj=local,
+        path_in_repo=remote,
+        repo_id=repo_id,
+        repo_type="model",
+    )
+    print(f"  ✅ {remote} uploaded")
+
+if not files_to_upload:
+    print("  Nothing to upload.")
+else:
+    print(f"Upload complete: https://huggingface.co/{repo_id}")
+PYEOF