chore(ml): remove machine learning model files

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