import os
import shutil
from huggingface_hub import HfApi, Repository
import joblib

# Script to upload your models to HuggingFace Model Hub
def upload_sbert_model():
    """Upload your fine-tuned SBERT model to HuggingFace"""
    
    # Your local model path
    local_model_path = "C:\\Users\\TUF_F15\\Downloads\\ANS-master (1)\\ANS-master\\server\\models\\fine_tuned_sbert"
    
    # HuggingFace model repository name (change to your username)
    repo_name = "your-username/po-validator-sbert"
    
    # Initialize HuggingFace API
    api = HfApi()
    
    # Create repository
    try:
        api.create_repo(repo_name, exist_ok=True)
        print(f"Repository {repo_name} created/exists")
    except Exception as e:
        print(f"Error creating repository: {e}")
        return
    
    # Upload model files
    try:
        api.upload_folder(
            folder_path=local_model_path,
            repo_id=repo_name,
            commit_message="Upload fine-tuned SBERT model for PO validation"
        )
        print(f"SBERT model uploaded successfully to {repo_name}")
    except Exception as e:
        print(f"Error uploading SBERT model: {e}")

def upload_xgboost_model():
    """Upload your XGBoost model to HuggingFace"""
    
    # Your local model path
    local_model_path = "C:\\Users\\TUF_F15\\Downloads\\ANS-master (1)\\ANS-master\\server\\models\\po_risk_xgb_model.pkl"
    
    # HuggingFace model repository name
    repo_name = "your-username/po-validator-xgboost"
    
    # Initialize HuggingFace API
    api = HfApi()
    
    # Create repository
    try:
        api.create_repo(repo_name, exist_ok=True)
        print(f"Repository {repo_name} created/exists")
    except Exception as e:
        print(f"Error creating repository: {e}")
        return
    
    # Upload model file
    try:
        api.upload_file(
            path_or_fileobj=local_model_path,
            path_in_repo="po_risk_xgb_model.pkl",
            repo_id=repo_name,
            commit_message="Upload XGBoost model for PO risk prediction"
        )
        print(f"XGBoost model uploaded successfully to {repo_name}")
    except Exception as e:
        print(f"Error uploading XGBoost model: {e}")

def create_model_card():
    """Create a model card for your models"""
    model_card_content = """---
language: en
license: mit
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- purchase-order
- risk-assessment
pipeline_tag: sentence-similarity
---

# PO Validator SBERT Model

This is a fine-tuned Sentence-BERT model for Purchase Order validation and risk assessment.

## Model Description

The model is trained to understand product descriptions and match them with SKU databases for purchase order validation. It's part of a larger system that includes XGBoost for risk prediction.

## Usage

```python
from sentence_transformers import SentenceTransformer

model = SentenceTransformer('your-username/po-validator-sbert')
embeddings = model.encode(['Product description here'])
```

## Training Data

The model was fine-tuned on purchase order data containing product descriptions and their corresponding SKU mappings.

## Performance

The model achieves high accuracy in semantic matching of product descriptions to SKU codes, enabling automated purchase order validation.
"""
    
    with open("model_card.md", "w") as f:
        f.write(model_card_content)
    print("Model card created: model_card.md")

if __name__ == "__main__":
    print("HuggingFace Model Upload Script")
    print("Make sure you have huggingface_hub installed and are logged in")
    print("Run: pip install huggingface_hub")
    print("Run: huggingface-cli login")
    
    # Uncomment the functions you want to run:
    # upload_sbert_model()
    # upload_xgboost_model()
    create_model_card()