example_usage.py

"""
Example script for using NEED AI model from Hugging Face Hub
"""

import torch
from transformers import AutoTokenizer
from huggingface_hub import hf_hub_download

# Configuration
MODEL_REPO = "yogami9/need-ai-conversational-model"
DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

def setup_model():
    """Download and setup the model"""
    print("📥 Downloading model files...")

    # Download model file
    model_path = hf_hub_download(
        repo_id=MODEL_REPO,
        filename="pytorch_model.bin"
    )

    # Download custom modeling code
    modeling_path = hf_hub_download(
        repo_id=MODEL_REPO,
        filename="modeling_need.py"
    )

    print("✅ Files downloaded")

    # Load tokenizer
    tokenizer = AutoTokenizer.from_pretrained(MODEL_REPO)

    # Import custom model class
    import sys
    import os
    sys.path.insert(0, os.path.dirname(modeling_path))
    from modeling_need import NEEDConversationalModel

    # Load model
    model = NEEDConversationalModel.from_pretrained(model_path)
    model = model.to(DEVICE)
    model.eval()

    print(f"✅ Model loaded on {DEVICE}")

    return model, tokenizer

def generate_response(model, tokenizer, text: str, max_length: int = 100):
    """Generate response for user input"""
    # Prepare input
    input_text = f"Human: {text}"
    input_ids = tokenizer.encode(input_text, return_tensors='pt').to(DEVICE)
    speaker_ids = torch.zeros_like(input_ids)

    # Generate
    with torch.no_grad():
        output_ids = model.generate(
            input_ids=input_ids,
            speaker_ids=speaker_ids,
            max_length=max_length,
            temperature=0.8,
            top_k=50
        )

    # Decode
    response = tokenizer.decode(output_ids[0], skip_special_tokens=True)
    return response

if __name__ == "__main__":
    # Setup
    model, tokenizer = setup_model()

    # Example queries
    queries = [
        "I need a house cleaner in Lagos",
        "How much does tutoring cost?",
        "I need help with plumbing",
    ]

    print("\n" + "="*60)
    print("Testing NEED AI Model")
    print("="*60)

    for query in queries:
        print(f"\n👤 User: {query}")
        response = generate_response(model, tokenizer, query)
        print(f"🤖 Assistant: {response}")