"""
Run PALADIM directly from Hugging Face Hub
No need to clone the repository - everything loads from the cloud!
"""

import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from peft import PeftModel

def demo_from_huggingface():
    """
    Load and use PALADIM model directly from Hugging Face
    Perfect for: Notebooks, Colab, or quick testing
    """
    
    print("="*70)
    print("PALADIM Demo - Running from Hugging Face Hub")
    print("="*70)
    print("\n🔗 Model: https://huggingface.co/nickagge/paladim-sentiment")
    print("📦 No installation needed - everything loads from the cloud!\n")
    
    # Step 1: Load base model and tokenizer from Hugging Face
    print("Step 1: Loading base model from Hugging Face...")
    base_model = "distilbert-base-uncased"
    model = AutoModelForSequenceClassification.from_pretrained(
        base_model,
        num_labels=2
    )
    tokenizer = AutoTokenizer.from_pretrained(base_model)
    print("   ✅ Base model loaded")
    
    # Step 2: Load PALADIM LoRA adapters from Hugging Face
    print("\nStep 2: Loading PALADIM adapters from your Hugging Face repo...")
    model = PeftModel.from_pretrained(model, "nickagge/paladim-sentiment")
    print("   ✅ PALADIM model ready!")
    
    # Step 3: Run inference
    print("\n" + "="*70)
    print("Testing PALADIM on various inputs:")
    print("="*70)
    
    test_cases = [
        # Positive examples
        "This product exceeded my expectations! Absolutely love it!",
        "Best purchase I've made this year. Highly recommend!",
        "Amazing quality and fast shipping. Five stars!",
        
        # Negative examples
        "Terrible quality. Complete waste of money.",
        "Very disappointed. Do not buy this product.",
        "Worst experience ever. Will never buy again.",
        
        # Neutral/Mixed
        "It's okay, nothing special.",
        "Good product but overpriced.",
    ]
    
    model.eval()
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    print(f"\n💻 Running on: {device}")
    if device.type == "cuda":
        print(f"   GPU: {torch.cuda.get_device_name(0)}\n")
    
    print("-"*70)
    
    with torch.no_grad():
        for i, text in enumerate(test_cases, 1):
            # Tokenize
            inputs = tokenizer(
                text,
                return_tensors="pt",
                truncation=True,
                max_length=128,
                padding=True
            )
            inputs = {k: v.to(device) for k, v in inputs.items()}
            
            # Predict
            outputs = model(**inputs)
            logits = outputs.logits
            probs = torch.softmax(logits, dim=-1)[0]
            prediction = torch.argmax(logits, dim=-1).item()
            confidence = probs[prediction].item()
            
            # Format output
            sentiment = "👍 Positive" if prediction == 1 else "👎 Negative"
            bar_length = int(confidence * 20)
            bar = "█" * bar_length + "░" * (20 - bar_length)
            
            print(f"\n{i}. \"{text}\"")
            print(f"   → {sentiment}")
            print(f"   Confidence: [{bar}] {confidence:.1%}")
    
    print("\n" + "="*70)
    print("✨ Demo complete!")
    print("\n📚 How to use this in your code:")
    print("-"*70)
    print("""
from transformers import AutoModelForSequenceClassification
from peft import PeftModel
import torch

# Load model
model = AutoModelForSequenceClassification.from_pretrained(
    "distilbert-base-uncased", 
    num_labels=2
)
model = PeftModel.from_pretrained(model, "nickagge/paladim-sentiment")

# Tokenizer
tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")

# Predict
text = "Your text here"
inputs = tokenizer(text, return_tensors="pt", truncation=True)
outputs = model(**inputs)
prediction = torch.argmax(outputs.logits, dim=-1).item()
sentiment = "Positive" if prediction == 1 else "Negative"
print(f"Sentiment: {sentiment}")
    """)
    
    print("="*70)
    print("\n💡 Tips:")
    print("   • Works in Google Colab, Jupyter, or any Python environment")
    print("   • No need to clone repository")
    print("   • Model automatically cached locally after first run")
    print("   • Use GPU for faster inference (CUDA required)")
    print("\n🚀 Ready to use PALADIM in your projects!")

if __name__ == "__main__":
    try:
        demo_from_huggingface()
    except Exception as e:
        print(f"\n❌ Error: {e}")
        print("\nTroubleshooting:")
        print("1. Install required packages:")
        print("   pip install transformers peft torch")
        print("\n2. Check internet connection (model loads from cloud)")
        print("\n3. Verify model exists: https://huggingface.co/nickagge/paladim-sentiment")
        
        import traceback
        traceback.print_exc()