init

scripts/upload_instructions.md

@@ -0,0 +1,97 @@
+# Upload Model to Hugging Face Hub
+
+## Quick Start (3 Steps)
+
+### 1. Install Hugging Face Hub
+```bash
+pip install huggingface_hub
+```
+
+### 2. Login to Hugging Face
+```bash
+huggingface-cli login
+```
+Enter your Hugging Face token when prompted. Get your token from: https://huggingface.co/settings/tokens
+
+### 3. Run Upload Script
+```bash
+python upload_to_huggingface.py
+```
+
+---
+
+## Alternative: Manual Upload via Web Interface
+
+1. Go to https://huggingface.co/new
+2. Create a new model repository (e.g., `handwriting-recognition-iam`)
+3. Click "Files" → "Add file" → "Upload files"
+4. Upload:
+   - `best_model.pth`
+   - `README.md`
+   - `requirements.txt`
+   - `train_colab.ipynb`
+   - `training_history.png`
+
+---
+
+## Alternative: Upload from Python (Colab/Script)
+
+```python
+from huggingface_hub import HfApi, create_repo, upload_file
+
+# Login first (in Colab)
+from huggingface_hub import notebook_login
+notebook_login()
+
+# Create repository
+api = HfApi()
+repo_id = "your-username/handwriting-recognition-iam"
+create_repo(repo_id, repo_type="model", exist_ok=True)
+
+# Upload model
+upload_file(
+    path_or_fileobj="best_model.pth",
+    path_in_repo="best_model.pth",
+    repo_id=repo_id,
+    repo_type="model"
+)
+
+print(f"✓ Uploaded! View at: https://huggingface.co/{repo_id}")
+```
+
+---
+
+## What Gets Uploaded
+
+- ✅ `best_model.pth` - Trained model checkpoint (105MB)
+- ✅ `README.md` - Project documentation
+- ✅ `requirements.txt` - Dependencies
+- ✅ `train_colab.ipynb` - Training notebook
+- ✅ `training_history.png` - Training metrics visualization
+
+---
+
+## Customization
+
+Edit `upload_to_huggingface.py` to change:
+- `REPO_NAME` - Your preferred repository name
+- `private=False` - Set to `True` for private repository
+- `FILES_TO_UPLOAD` - Add/remove files to upload
+
+---
+
+## Troubleshooting
+
+### "Authentication required"
+```bash
+huggingface-cli login
+```
+
+### "Repository already exists"
+- The script uses `exist_ok=True`, so it will update existing repo
+- Or change `REPO_NAME` to create a new one
+
+### Large file upload fails
+- Hugging Face supports files up to 50GB
+- Your model (105MB) should upload fine
+- If it fails, try uploading via web interface

scripts/upload_to_huggingface.py

@@ -0,0 +1,95 @@
+"""
+Upload handwriting recognition model to Hugging Face Hub
+"""
+
+import os
+from huggingface_hub import HfApi, create_repo, upload_file
+
+# Configuration
+MODEL_PATH = "best_model.pth"
+REPO_NAME = "handwriting-recognition-iam"  # Change this to your preferred name
+USERNAME = None  # Will use your HF username automatically
+
+# Files to upload
+FILES_TO_UPLOAD = [
+    "best_model.pth",
+    "README.md",
+    "requirements.txt",
+    "train_colab.ipynb",
+    "training_history.png"
+]
+
+def upload_model_to_hf():
+    """Upload model and related files to Hugging Face Hub"""
+
+    # Check if model exists
+    if not os.path.exists(MODEL_PATH):
+        print(f"❌ Error: {MODEL_PATH} not found!")
+        print("   Please ensure the model file exists in the current directory.")
+        return
+
+    print("🚀 Starting upload to Hugging Face Hub...")
+    print(f"   Repository: {REPO_NAME}")
+    print()
+
+    try:
+        # Initialize API
+        api = HfApi()
+
+        # Get username from token
+        user_info = api.whoami()
+        username = user_info['name']
+        repo_id = f"{username}/{REPO_NAME}"
+
+        print(f"✓ Authenticated as: {username}")
+        print(f"✓ Repository ID: {repo_id}")
+        print()
+
+        # Create repository (if it doesn't exist)
+        print("📦 Creating repository...")
+        try:
+            create_repo(
+                repo_id=repo_id,
+                repo_type="model",
+                exist_ok=True,
+                private=False  # Set to True if you want a private repo
+            )
+            print(f"✓ Repository created/verified: https://huggingface.co/{repo_id}")
+        except Exception as e:
+            print(f"⚠️  Repository may already exist: {e}")
+
+        print()
+
+        # Upload files
+        print("📤 Uploading files...")
+        for file_path in FILES_TO_UPLOAD:
+            if os.path.exists(file_path):
+                print(f"   Uploading {file_path}...", end=" ")
+                try:
+                    upload_file(
+                        path_or_fileobj=file_path,
+                        path_in_repo=file_path,
+                        repo_id=repo_id,
+                        repo_type="model"
+                    )
+                    print("✓")
+                except Exception as e:
+                    print(f"❌ Failed: {e}")
+            else:
+                print(f"   ⚠️  Skipping {file_path} (not found)")
+
+        print()
+        print("=" * 60)
+        print("🎉 Upload complete!")
+        print(f"🔗 View your model: https://huggingface.co/{repo_id}")
+        print("=" * 60)
+
+    except Exception as e:
+        print(f"❌ Error during upload: {e}")
+        print()
+        print("Make sure you're logged in to Hugging Face:")
+        print("  Run: huggingface-cli login")
+        print("  Or set HF_TOKEN environment variable")
+
+if __name__ == "__main__":
+    upload_model_to_hf()