push_to_hf.py

#!/usr/bin/env python3
"""
ZamAI Multilingual Embeddings - Hugging Face Hub Deployment Script
This script pushes the ZamAI multilingual embeddings model to Hugging Face Hub
"""

import os
import subprocess
import sys
from pathlib import Path
from huggingface_hub import HfApi, login, create_repo

def check_huggingface_auth():
    """Check if user is authenticated with Hugging Face"""
    try:
        api = HfApi()
        user_info = api.whoami()
        print(f"✅ Authenticated as: {user_info['name']}")
        return True
    except Exception as e:
        print("❌ Not authenticated with Hugging Face")
        print("Please run: huggingface-cli login")
        return False

def create_model_card():
    """Create a comprehensive model card for the ZamAI model"""
    model_card_content = """---
license: apache-2.0
datasets:
  - tasal9/Pashto_Dataset
language:
  - ps
  - en
  - ar
  - ur
  - fa
library_name: sentence-transformers
tags:
  - multilingual
  - embeddings
  - semantic-search
  - pashto
  - chromadb
  - llamaindex
  - cross-lingual
  - afghanistan
  - zamai
pipeline_tag: feature-extraction
model-index:
  - name: Multilingual-ZamAI-Embeddings
    results: []
widget:
  - source_sentence: "This is a sample sentence in English."
    sentences:
      - "This sentence is similar to the first one."
      - "دا جمله د لومړۍ جملې سره ورته ده."
      - "This sentence has nothing to do with the others."
    example_title: "English to multilingual similarity"
  - source_sentence: "دا په پښتو کې یوه نمونه جمله ده."
    sentences:
      - "This is a sample sentence in English."
      - "دا جمله د لومړۍ جملې سره ورته ده."
      - "زه د پښتو ژبې زده کړه کوم."
    example_title: "Pashto to multilingual similarity"
---

# ZamAI Multilingual Embeddings

This model provides state-of-the-art multilingual sentence embeddings with a special focus on Pashto language support. Built on the foundation of `sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2`, this model enables semantic search, document retrieval, and cross-lingual understanding across 50+ languages.

## Model Details

- **Model Type**: Sentence Transformer (BERT-based)
- **Base Model**: [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2)
- **Languages Supported**: 50+ including Pashto (ps), English (en), Arabic (ar), Urdu (ur), Farsi (fa), and more
- **Max Sequence Length**: 512 tokens
- **Output Dimensionality**: 384
- **License**: Apache 2.0

## Key Features

- **Cross-lingual Understanding**: Retrieve semantically similar content across different languages
- **Pashto Language Support**: Optimized for Pashto language processing and understanding
- **Vector Database Integration**: Ready-to-use with ChromaDB and LlamaIndex
- **High Performance**: Efficient processing suitable for real-time applications

## Usage

### Basic Usage with Sentence Transformers

```python
from sentence_transformers import SentenceTransformer
import numpy as np

# Load the model
model = SentenceTransformer('tasal9/Multilingual-ZamAI-Embeddings')

# English sentences
sentences_en = [
    "This is a sample sentence in English.",
    "This sentence is similar to the first one."
]

# Pashto sentences
sentences_ps = [
    "دا په پښتو کې یوه نمونه جمله ده.",
    "دا جمله د لومړۍ جملې سره ورته ده."
]

# Get embeddings
embeddings_en = model.encode(sentences_en)
embeddings_ps = model.encode(sentences_ps)

# Calculate cross-lingual similarity
from numpy import dot
from numpy.linalg import norm

def cosine_similarity(a, b):
    return dot(a, b) / (norm(a) * norm(b))

# Compare English and Pashto sentences
similarity = cosine_similarity(embeddings_en[0], embeddings_ps[0])
print(f"Cross-lingual similarity: {similarity:.4f}")
```

### Advanced Usage with ChromaDB and LlamaIndex

```python
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.vector_stores.chroma import ChromaVectorStore
from llama_index.core import StorageContext, VectorStoreIndex
import chromadb

# Initialize the embedding model
embed_model = HuggingFaceEmbedding(model_name="tasal9/Multilingual-ZamAI-Embeddings")

# Set up ChromaDB
chroma_client = chromadb.PersistentClient(path="./chroma_db")
collection = chroma_client.get_or_create_collection("multilingual_collection")
vector_store = ChromaVectorStore(chroma_collection=collection)
storage_context = StorageContext.from_defaults(vector_store=vector_store)

# Create index with your documents
# index = VectorStoreIndex.from_documents(documents, storage_context=storage_context, embed_model=embed_model)
# query_engine = index.as_query_engine()

# Query in any language
# result = query_engine.query("What is the capital of Afghanistan?")
# result_ps = query_engine.query("د افغانستان پلازمېنه څه ده؟")
```

## Performance

The model demonstrates excellent cross-lingual performance:

- **English-English**: High semantic similarity detection
- **Pashto-Pashto**: Native language understanding and similarity
- **Cross-lingual (English-Pashto)**: Strong cross-lingual semantic alignment
- **Multilingual**: Supports 50+ languages with consistent performance

## Applications

- **Semantic Search**: Find relevant documents across multiple languages
- **Cross-lingual Information Retrieval**: Retrieve Pashto content using English queries and vice versa
- **Document Similarity**: Compare documents in different languages
- **Question Answering**: Build multilingual QA systems
- **Content Recommendation**: Recommend similar content across languages

## Technical Details

- **Architecture**: BERT-based transformer model
- **Training Data**: Multilingual parallel and monolingual corpora
- **Optimization**: Optimized for semantic similarity tasks
- **Integration**: Compatible with Hugging Face Transformers, Sentence Transformers, LlamaIndex, and ChromaDB

## Citation

If you use this model in your research, please cite:

```bibtex
@misc{zamai-multilingual-embeddings-2024,
  title={ZamAI Multilingual Embeddings: Cross-lingual Sentence Transformers with Pashto Support},
  author={ZamAI Team},
  year={2024},
  url={https://huggingface.co/tasal9/Multilingual-ZamAI-Embeddings}
}
```

## License

This model is released under the Apache 2.0 License. See the [LICENSE](LICENSE) file for details.

## Contact

For questions or support, please open an issue on the [model repository](https://huggingface.co/tasal9/Multilingual-ZamAI-Embeddings) or contact the ZamAI team.
"""
    
    with open("README.md", "w", encoding="utf-8") as f:
        f.write(model_card_content)
    print("✅ Model card created: README.md")

def prepare_repository():
    """Prepare the repository for upload"""
    print("🔧 Preparing repository...")
    
    # Create model card
    create_model_card()
    
    # Create additional files
    files_to_create = {
        ".gitattributes": """*.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
""",
        "LICENSE": """Apache License
Version 2.0, January 2004
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    }
    
    for filename, content in files_to_create.items():
        with open(filename, "w", encoding="utf-8") as f:
            f.write(content)
        print(f"✅ Created: {filename}")

def push_to_huggingface(repo_name="tasal9/Multilingual-ZamAI-Embeddings"):
    """Push the model to Hugging Face Hub"""
    
    # Check authentication
    if not check_huggingface_auth():
        print("\n🔑 Please authenticate with Hugging Face first:")
        print("huggingface-cli login")
        return False
    
    try:
        # Prepare repository
        prepare_repository()
        
        # Create repository on Hugging Face Hub
        api = HfApi()
        
        try:
            print(f"🚀 Creating repository: {repo_name}")
            create_repo(
                repo_id=repo_name,
                repo_type="model",
                exist_ok=True,
                private=False
            )
            print(f"✅ Repository created/verified: {repo_name}")
        except Exception as e:
            print(f"ℹ️  Repository may already exist: {e}")
        
        # Upload files
        print("📤 Uploading files to Hugging Face Hub...")
        
        # Upload all files in the current directory
        current_dir = Path(".")
        files_to_upload = [
            "README.md",
            "requirements.txt", 
            "setup.py",
            "demo.py",
            "simple_demo.py",
            "indexer.py",
            "modeling.py",
            "config.json",
            "LICENSE",
            ".gitattributes"
        ]
        
        for file_path in files_to_upload:
            if os.path.exists(file_path):
                try:
                    api.upload_file(
                        path_or_fileobj=file_path,
                        path_in_repo=file_path,
                        repo_id=repo_name,
                        commit_message=f"Add {file_path}"
                    )
                    print(f"✅ Uploaded: {file_path}")
                except Exception as e:
                    print(f"⚠️  Warning uploading {file_path}: {e}")
        
        print(f"\n🎉 Successfully pushed ZamAI Multilingual Embeddings to Hugging Face Hub!")
        print(f"🔗 Model URL: https://huggingface.co/{repo_name}")
        print(f"📖 Usage: model = SentenceTransformer('{repo_name}')")
        
        return True
        
    except Exception as e:
        print(f"❌ Error pushing to Hugging Face: {e}")
        return False

if __name__ == "__main__":
    print("🚀 ZamAI Multilingual Embeddings - Hugging Face Deployment")
    print("=" * 60)
    
    # Check if we're in the right directory
    if not os.path.exists("setup.py"):
        print("❌ Please run this script from the Multilingual-ZamAI-Embeddings directory")
        sys.exit(1)
    
    # Push to Hugging Face
    success = push_to_huggingface()
    
    if success:
        print("\n✨ Deployment completed successfully!")
        print("Your model is now available on Hugging Face Hub")
    else:
        print("\n❌ Deployment failed. Please check the errors above.")
        sys.exit(1)