simple_upload.py

#!/usr/bin/env python3
"""
Simple script to upload model files to Hugging Face Hub
"""

import os
import torch
from transformers import AutoTokenizer
from huggingface_hub import HfApi
import json
from pathlib import Path

# Configuration
REPO_ID = "Adya662/bert-tiny-amd"
MODEL_PATH = "best_enhanced_progressive_amd.pth"
BASE_MODEL = "prajjwal1/bert-tiny"

def create_model_config():
    """Create model configuration"""
    config = {
        "model_type": "bert",
        "architectures": ["BertForSequenceClassification"],
        "attention_proxy_dtype": "float32",
        "attention_dropout": 0.1,
        "classifier_dropout": None,
        "hidden_act": "gelu",
        "hidden_dropout_prob": 0.1,
        "hidden_size": 128,
        "initializer_range": 0.02,
        "intermediate_size": 512,
        "layer_norm_eps": 1e-12,
        "max_position_embeddings": 512,
        "model_type": "bert",
        "num_attention_heads": 2,
        "num_hidden_layers": 2,
        "num_labels": 1,
        "pad_token_id": 0,
        "position_embedding_type": "absolute",
        "problem_type": "single_label_classification",
        "torch_dtype": "float32",
        "transformers_version": "4.21.0",
        "type_vocab_size": 2,
        "use_cache": True,
        "vocab_size": 30522
    }
    return config

def create_training_metadata():
    """Create training metadata"""
    metadata = {
        "model_name": "bert-tiny-amd",
        "base_model": "prajjwal1/bert-tiny",
        "task": "text-classification",
        "dataset": "ElevateNow call center transcripts",
        "language": "en",
        "license": "mit",
        "pipeline_tag": "text-classification",
        "tags": [
            "text-classification",
            "answering-machine-detection",
            "bert-tiny",
            "binary-classification",
            "call-center",
            "voice-processing"
        ],
        "performance": {
            "validation_accuracy": 0.9394,
            "precision": 0.9275,
            "recall": 0.8727,
            "f1_score": 0.8993
        },
        "training_details": {
            "total_samples": 3548,
            "training_samples": 2838,
            "validation_samples": 710,
            "epochs": 15,
            "batch_size": 32,
            "learning_rate": 3e-5,
            "device": "mps"
        }
    }
    return metadata

def upload_files():
    """Upload files to Hugging Face Hub"""
    
    print("🚀 Starting file upload to Hugging Face Hub...")
    
    # Initialize HF API
    api = HfApi()
    
    # Create model configuration
    config = create_model_config()
    
    # Save config
    with open("config.json", "w") as f:
        json.dump(config, f, indent=2)
    
    # Create training metadata
    metadata = create_training_metadata()
    
    # Save training metadata
    with open("training_metadata.json", "w") as f:
        json.dump(metadata, f, indent=2)
    
    # Load and save tokenizer from base model
    print("📥 Loading tokenizer...")
    tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)
    tokenizer.save_pretrained(".")
    
    # Copy model weights
    if os.path.exists(MODEL_PATH):
        print("📥 Copying model weights...")
        import shutil
        shutil.copy2(MODEL_PATH, "pytorch_model.bin")
        print("✅ Model weights copied successfully")
    else:
        print(f"❌ Model file {MODEL_PATH} not found!")
        return False
    
    # Create README.md
    readme_content = """---
license: mit
tags:
- text-classification
- answering-machine-detection
- bert-tiny
- binary-classification
- call-center
- voice-processing
pipeline_tag: text-classification
---

# BERT-Tiny AMD Classifier

A lightweight BERT-Tiny model fine-tuned for Answering Machine Detection (AMD) in call center environments.

## Model Description

This model is based on `prajjwal1/bert-tiny` and fine-tuned to classify phone call transcripts as either human or machine (answering machine/voicemail) responses. It's designed for real-time call center applications where quick and accurate detection of answering machines is crucial.

## Model Architecture

- **Base Model**: `prajjwal1/bert-tiny` (2 layers, 128 hidden size, 2 attention heads)
- **Total Parameters**: ~4.4M (lightweight and efficient)
- **Input**: User transcript text (max 128 tokens)
- **Output**: Single logit with sigmoid activation for binary classification
- **Loss Function**: BCEWithLogitsLoss with positive weight for class imbalance

## Performance

- **Validation Accuracy**: 93.94%
- **Precision**: 92.75%
- **Recall**: 87.27%
- **F1-Score**: 89.93%
- **Training Device**: MPS (Apple Silicon GPU)
- **Best Epoch**: 15 (with early stopping)

## Training Data

- **Total Samples**: 3,548 phone call transcripts
- **Training Set**: 2,838 samples
- **Validation Set**: 710 samples
- **Class Distribution**: 30.8% machine calls, 69.2% human calls
- **Source**: ElevateNow call center data

## Usage

### Basic Inference

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load model and tokenizer
model = AutoModelForSequenceClassification.from_pretrained("Adya662/bert-tiny-amd")
tokenizer = AutoTokenizer.from_pretrained("Adya662/bert-tiny-amd")

# Prepare input
text = "Hello, this is John speaking"
inputs = tokenizer(text, return_tensors="pt", max_length=128, truncation=True, padding=True)

# Make prediction
with torch.no_grad():
    outputs = model(**inputs)
    logits = outputs.logits.squeeze(-1)
    probability = torch.sigmoid(logits).item()
    is_machine = probability >= 0.5

print(f"Prediction: {'Machine' if is_machine else 'Human'}")
print(f"Confidence: {probability:.4f}")
```

## Training Details

- **Optimizer**: AdamW with weight decay (0.01)
- **Learning Rate**: 3e-5 with linear scheduling
- **Batch Size**: 32
- **Epochs**: 15 (with early stopping)
- **Early Stopping**: Patience of 3 epochs
- **Class Imbalance**: Handled with positive weight

## Limitations

- Trained on English phone call transcripts
- May not generalize well to other languages or domains
- Performance may vary with different transcription quality
- Designed for short utterances (max 128 tokens)

## License

MIT License - see LICENSE file for details.
"""
    
    with open("README.md", "w") as f:
        f.write(readme_content)
    
    # Upload to Hub
    print("⬆️ Uploading to Hugging Face Hub...")
    try:
        api.upload_folder(
            folder_path=".",
            repo_id=REPO_ID,
            repo_type="model",
            commit_message="Upload trained BERT-Tiny AMD model"
        )
        print("✅ Model uploaded successfully!")
        print(f"🔗 Model available at: https://huggingface.co/{REPO_ID}")
        return True
    except Exception as e:
        print(f"❌ Upload failed: {e}")
        return False

if __name__ == "__main__":
    success = upload_files()
    if success:
        print("\n🎉 Model deployment completed successfully!")
    else:
        print("\n💥 Model deployment failed!")