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"""
Hebrew Intent Classification Demo - Debug Version
"""

import gradio as gr
import sys
import traceback


def test_model_loading():
    """Test if model can be loaded"""
    try:
        print("๐Ÿ”„ Testing model loading...")
        from transformers import AutoTokenizer, AutoModelForSequenceClassification

        model_name = "humy65/hebrew-intent-classifier"
        print(f"๐Ÿ“ก Attempting to load: {model_name}")

        tokenizer = AutoTokenizer.from_pretrained(model_name)
        print("โœ… Tokenizer loaded")

        model = AutoModelForSequenceClassification.from_pretrained(model_name)
        print("โœ… Model loaded")

        print(f"๐Ÿ“‹ Labels: {model.config.id2label}")
        return True, "Model loaded successfully!", model, tokenizer

    except Exception as e:
        error_msg = f"โŒ Error: {str(e)}\n\nTraceback:\n{traceback.format_exc()}"
        print(error_msg)
        return False, error_msg, None, None


def classify_text(text):
    """Classification function with lazy loading"""
    if not text or not text.strip():
        return "โš ๏ธ Please enter Hebrew text", {}

    try:
        # Try to load model on demand
        success, message, model, tokenizer = test_model_loading()

        if not success:
            return f"Model Loading Failed:\n{message}", {}

        # Perform classification
        import torch

        inputs = tokenizer(text, return_tensors="pt",
                           padding=True, truncation=True, max_length=128)

        with torch.no_grad():
            outputs = model(**inputs)
            logits = outputs.logits
            probabilities = torch.softmax(logits, dim=-1)

        # Get results
        predicted_id = torch.argmax(logits, dim=-1).item()
        predicted_label = model.config.id2label[predicted_id]
        confidence = probabilities[0][predicted_id].item()

        # Create confidence scores for all labels
        all_scores = {}
        for i, prob in enumerate(probabilities[0]):
            intent_name = model.config.id2label[i]
            all_scores[intent_name] = float(prob)

        result = f"""
๐ŸŽฏ Predicted Intent: {predicted_label}
๐ŸŽฒ Confidence: {confidence:.1%}

๐Ÿ“Š All Predictions:
"""

        # Sort and display
        sorted_scores = sorted(
            all_scores.items(), key=lambda x: x[1], reverse=True)
        for intent, score in sorted_scores:
            bar = "โ–ˆ" * max(1, int(score * 20))
            result += f"\n{intent}: {score:.1%} {bar}"

        return result, all_scores

    except Exception as e:
        error_msg = f"Classification Error: {str(e)}\n\nTraceback:\n{traceback.format_exc()}"
        print(error_msg)
        return error_msg, {}


def test_connection():
    """Test Hugging Face connection"""
    try:
        from huggingface_hub import HfApi
        api = HfApi()
        info = api.model_info("humy65/hebrew-intent-classifier")
        return f"โœ… Model repository accessible\nModel ID: {info.modelId}\nLast Modified: {info.lastModified}"
    except Exception as e:
        return f"โŒ Repository access failed: {str(e)}"

def get_training_data():
    """Display the training data used for the model"""
    training_data = [
        ("ืฉื›ื—ืชื™ ืืช ื”ืกื™ืกืžื” ืฉืœื™", "ืฉื›ื—ืช ืกื™ืกืžื”"),
        ("ืื™ืš ืื ื™ ืžื‘ื˜ืœ ืืช ื”ืžื ื•ื™?", "ื‘ื™ื˜ื•ืœ ืžื ื•ื™"),
        ("ืžื” ื”ืžื—ื™ืจ ืฉืœ ื”ืชื•ื›ื ื™ืช?", "ืฉืืœื” ื›ืœืœื™ืช"),
        ("ื”ืืชืจ ืœื ืขื•ื‘ื“ ืœื™", "ืชืžื™ื›ื” ื˜ื›ื ื™ืช"),
        ("ืื ื™ ืœื ืžืฆืœื™ื— ืœื”ืชื—ื‘ืจ", "ืชืžื™ื›ื” ื˜ื›ื ื™ืช"),
        ("ืื™ืš ืื ื™ ืžืฉื ื” ืืช ื›ืชื•ื‘ืช ื”ืื™ืžื™ื™ืœ?", "ืฉืืœื” ื›ืœืœื™ืช"),
        ("ืื ื™ ืจื•ืฆื” ืœืฉื“ืจื’ ืืช ื”ืชื•ื›ื ื™ืช ืฉืœื™", "ืฉืืœื” ื›ืœืœื™ืช"),
        ("ื”ื—ืฉื‘ื•ืŸ ืฉืœื™ ื ื ืขืœ", "ืชืžื™ื›ื” ื˜ื›ื ื™ืช"),
        ("ืื ื™ ืœื ืžืงื‘ืœ ืžื™ื™ืœื™ื", "ืชืžื™ื›ื” ื˜ื›ื ื™ืช"),
        ("ืื™ืš ืื ื™ ืจื•ืื” ืืช ื”ื—ืฉื‘ื•ื ื™ืช ืฉืœื™?", "ืฉืืœื” ื›ืœืœื™ืช"),
        ("ืื ื™ ืจื•ืฆื” ืœื‘ื˜ืœ ืืช ื”ืฉื™ืจื•ืช", "ื‘ื™ื˜ื•ืœ ืžื ื•ื™"),
        ("ืฉื›ื—ืชื™ ืืช ืคืจื˜ื™ ื”ื”ืชื—ื‘ืจื•ืช", "ืฉื›ื—ืช ืกื™ืกืžื”"),
        ("ืื™ื‘ื“ืชื™ ืืช ื”ืกื™ืกืžื”", "ืฉื›ื—ืช ืกื™ืกืžื”"),
        ("ืœื ื–ื•ื›ืจ ืืช ื”ืกื™ืกืžื”", "ืฉื›ื—ืช ืกื™ืกืžื”"),
        ("ื”ืกื™ืกืžื” ืœื ืขื•ื‘ื“ืช", "ืฉื›ื—ืช ืกื™ืกืžื”"),
        ("ืœื ืžืฆืœื™ื— ืœื”ื™ื›ื ืก ืขื ื”ืกื™ืกืžื”", "ืฉื›ื—ืช ืกื™ืกืžื”"),
        ("ืฆืจื™ืš ืœืืคืก ืืช ื”ืกื™ืกืžื”", "ืฉื›ื—ืช ืกื™ืกืžื”"),
        ("ื‘ืขื™ื” ืขื ื”ืกื™ืกืžื”", "ืฉื›ื—ืช ืกื™ืกืžื”"),
        ("ื”ืกื™ืกืžื” ืฉืœื™ ืœื ื ื›ื•ื ื”", "ืฉื›ื—ืช ืกื™ืกืžื”"),
        ("ืฉื›ื—ืชื™ ืžื” ื”ืกื™ืกืžื”", "ืฉื›ื—ืช ืกื™ืกืžื”"),
        ("ืื™ืš ืื ื™ ืžืฉื—ื–ืจ ืืช ื”ืกื™ืกืžื”", "ืฉื›ื—ืช ืกื™ืกืžื”"),
        ("ืจื•ืฆื” ืœืฉื ื•ืช ืืช ื”ืกื™ืกืžื”", "ืฉื›ื—ืช ืกื™ืกืžื”"),
        ("ื”ืกื™ืกืžื” ืœื ืžืชืงื‘ืœืช", "ืฉื›ื—ืช ืกื™ืกืžื”"),
        ("ื‘ืขื™ื™ืช ื”ืชื—ื‘ืจื•ืช - ืกื™ืกืžื”", "ืฉื›ื—ืช ืกื™ืกืžื”"),
        ("ืฆืจื™ืš ืขื–ืจื” ืขื ื”ืกื™ืกืžื”", "ืฉื›ื—ืช ืกื™ืกืžื”"),
        ("ืœื ื™ื•ื“ืข ืžื” ื”ืกื™ืกืžื” ืฉืœื™", "ืฉื›ื—ืช ืกื™ืกืžื”"),
        ("ืจื•ืฆื” ืœื‘ื˜ืœ ืืช ื”ืฉื™ืจื•ืช", "ื‘ื™ื˜ื•ืœ ืžื ื•ื™"),
        ("ืื™ืš ืžืคืกื™ืงื™ื ืืช ื”ืžื ื•ื™", "ื‘ื™ื˜ื•ืœ ืžื ื•ื™"),
        ("ืจื•ืฆื” ืœื”ืคืกื™ืง ืืช ื”ืชืฉืœื•ื", "ื‘ื™ื˜ื•ืœ ืžื ื•ื™"),
        ("ืื™ืš ื™ื•ืฆืื™ื ืžื”ืžื ื•ื™", "ื‘ื™ื˜ื•ืœ ืžื ื•ื™"),
        ("ื‘ืงืฉื” ืœื‘ื™ื˜ื•ืœ ืžื ื•ื™", "ื‘ื™ื˜ื•ืœ ืžื ื•ื™"),
        ("ืœื ืจื•ืฆื” ื™ื•ืชืจ ืืช ื”ืฉื™ืจื•ืช", "ื‘ื™ื˜ื•ืœ ืžื ื•ื™"),
        ("ืื™ืš ืžื‘ื˜ืœื™ื ืืช ื”ื—ืฉื‘ื•ืŸ", "ื‘ื™ื˜ื•ืœ ืžื ื•ื™"),
        ("ืจื•ืฆื” ืœืกื’ื•ืจ ืืช ื”ื—ืฉื‘ื•ืŸ", "ื‘ื™ื˜ื•ืœ ืžื ื•ื™"),
        ("ืขื–ืจื” ื‘ื‘ื™ื˜ื•ืœ ืžื ื•ื™", "ื‘ื™ื˜ื•ืœ ืžื ื•ื™"),
        ("ื”ืœื™ืš ื‘ื™ื˜ื•ืœ ื”ืžื ื•ื™", "ื‘ื™ื˜ื•ืœ ืžื ื•ื™"),
        ("ืžืขื•ื ื™ื™ืŸ ืœื‘ื˜ืœ", "ื‘ื™ื˜ื•ืœ ืžื ื•ื™"),
        ("ืื™ืš ืžืคืกื™ืงื™ื ืืช ื”ืฉื™ืจื•ืช", "ื‘ื™ื˜ื•ืœ ืžื ื•ื™"),
        ("ืจื•ืฆื” ืœื”ืคืกื™ืง ืืช ื”ื”ืจืฉืžื”", "ื‘ื™ื˜ื•ืœ ืžื ื•ื™"),
        ("ื‘ืงืฉื” ืœื”ืคืกืงืช ืฉื™ืจื•ืช", "ื‘ื™ื˜ื•ืœ ืžื ื•ื™"),
        ("ืžื” ื›ื•ืœืœ ื”ืฉื™ืจื•ืช", "ืฉืืœื” ื›ืœืœื™ืช"),
        ("ืื™ืœื• ืชื•ื›ื ื™ื•ืช ื™ืฉ ืœื›ื", "ืฉืืœื” ื›ืœืœื™ืช"),
        ("ื›ืžื” ืขื•ืœื” ื”ื—ื‘ื™ืœื”", "ืฉืืœื” ื›ืœืœื™ืช"),
        ("ืžื” ื”ื”ื‘ื“ืœ ื‘ื™ืŸ ื”ืชื•ื›ื ื™ื•ืช", "ืฉืืœื” ื›ืœืœื™ืช"),
        ("ืื™ืš ืื ื™ ืžืฉื ื” ืืช ื”ืคืจื˜ื™ื ืฉืœื™", "ืฉืืœื” ื›ืœืœื™ืช"),
        ("ืื™ืš ืืคืฉืจ ืœืฉื“ืจื’", "ืฉืืœื” ื›ืœืœื™ืช"),
        ("ืžื” ื”ืืคืฉืจื•ื™ื•ืช ืฉืœื›ื", "ืฉืืœื” ื›ืœืœื™ืช"),
        ("ืื ื™ ืจื•ืฆื” ืœืขื“ื›ืŸ ืคืจื˜ื™ื", "ืฉืืœื” ื›ืœืœื™ืช"),
        ("ืื™ืš ืจื•ืื™ื ืืช ื”ื”ื™ืกื˜ื•ืจื™ื”", "ืฉืืœื” ื›ืœืœื™ืช"),
        ("ื”ืืคืœื™ืงืฆื™ื” ืงื•ืจืกืช", "ืชืžื™ื›ื” ื˜ื›ื ื™ืช"),
        ("ื™ืฉ ื‘ืื’ ื‘ืืชืจ", "ืชืžื™ื›ื” ื˜ื›ื ื™ืช"),
        ("ื”ื“ืฃ ืœื ื ื˜ืขืŸ", "ืชืžื™ื›ื” ื˜ื›ื ื™ืช"),
        ("ืฉื’ื™ืื” ื‘ืžืขืจื›ืช", "ืชืžื™ื›ื” ื˜ื›ื ื™ืช"),
        ("ื”ื˜ื•ืขืŸ ืœื ืขื•ื‘ื“", "ืชืžื™ื›ื” ื˜ื›ื ื™ืช"),
        ("ื‘ืขื™ื” ื˜ื›ื ื™ืช", "ืชืžื™ื›ื” ื˜ื›ื ื™ืช"),
        ("ื”ืžืขืจื›ืช ืœื ืžื’ื™ื‘ื”", "ืชืžื™ื›ื” ื˜ื›ื ื™ืช"),
        ("ืฉื’ื™ืืช ื—ื™ื‘ื•ืจ", "ืชืžื™ื›ื” ื˜ื›ื ื™ืช"),
        ("ื”ื›ืคืชื•ืจ ืœื ืขื•ื‘ื“", "ืชืžื™ื›ื” ื˜ื›ื ื™ืช"),
        ("ื”ืชืžื•ื ื•ืช ืœื ื ื˜ืขื ื•ืช", "ืชืžื™ื›ื” ื˜ื›ื ื™ืช"),
        ("ื”ื•ื•ื™ื“ืื• ืœื ืžืชื ื’ืŸ", "ืชืžื™ื›ื” ื˜ื›ื ื™ืช"),
        ("ืื™ื˜ื™ื•ืช ื‘ืืชืจ", "ืชืžื™ื›ื” ื˜ื›ื ื™ืช")
    ]
    
    # Count examples per category
    category_counts = {}
    for _, label in training_data:
        category_counts[label] = category_counts.get(label, 0) + 1
    
    result = f"""
๐Ÿ“Š **Training Data Summary**
Total Examples: {len(training_data)}

๐Ÿ“ˆ **Examples per Category:**
"""
    
    # Add category statistics
    for category, count in sorted(category_counts.items()):
        percentage = (count / len(training_data)) * 100
        result += f"\nโ€ข {category}: {count} examples ({percentage:.1f}%)"
    
    result += f"""

๐Ÿ“ **Sample Training Examples:**

๐Ÿ” **ืฉื›ื—ืช ืกื™ืกืžื” (Password Reset):**
โ€ข ืฉื›ื—ืชื™ ืืช ื”ืกื™ืกืžื” ืฉืœื™
โ€ข ืœื ื–ื•ื›ืจ ืืช ื”ืกื™ืกืžื”  
โ€ข ื”ืกื™ืกืžื” ืœื ืขื•ื‘ื“ืช
โ€ข ืฆืจื™ืš ืœืืคืก ืืช ื”ืกื™ืกืžื”
โ€ข ืื™ืš ืื ื™ ืžืฉื—ื–ืจ ืืช ื”ืกื™ืกืžื”

โŒ **ื‘ื™ื˜ื•ืœ ืžื ื•ื™ (Cancel Subscription):**
โ€ข ืื™ืš ืื ื™ ืžื‘ื˜ืœ ืืช ื”ืžื ื•ื™?
โ€ข ืจื•ืฆื” ืœื”ืคืกื™ืง ืืช ื”ืชืฉืœื•ื
โ€ข ืœื ืจื•ืฆื” ื™ื•ืชืจ ืืช ื”ืฉื™ืจื•ืช
โ€ข ืื™ืš ืžื‘ื˜ืœื™ื ืืช ื”ื—ืฉื‘ื•ืŸ
โ€ข ื‘ืงืฉื” ืœื‘ื™ื˜ื•ืœ ืžื ื•ื™

โ“ **ืฉืืœื” ื›ืœืœื™ืช (General Question):**
โ€ข ืžื” ื”ืžื—ื™ืจ ืฉืœ ื”ืชื•ื›ื ื™ืช?
โ€ข ื›ืžื” ืขื•ืœื” ื”ื—ื‘ื™ืœื”
โ€ข ืื™ืœื• ืชื•ื›ื ื™ื•ืช ื™ืฉ ืœื›ื
โ€ข ืื™ืš ืื ื™ ืžืฉื ื” ืืช ื”ืคืจื˜ื™ื ืฉืœื™
โ€ข ืžื” ื›ื•ืœืœ ื”ืฉื™ืจื•ืช

๐Ÿ”ง **ืชืžื™ื›ื” ื˜ื›ื ื™ืช (Technical Support):**
โ€ข ื”ืืชืจ ืœื ืขื•ื‘ื“ ืœื™
โ€ข ื”ืืคืœื™ืงืฆื™ื” ืงื•ืจืกืช
โ€ข ื™ืฉ ื‘ืื’ ื‘ืืชืจ
โ€ข ื”ื“ืฃ ืœื ื ื˜ืขืŸ
โ€ข ืฉื’ื™ืื” ื‘ืžืขืจื›ืช

---
๐Ÿ’ก **Model was trained with data augmentation techniques:**
โ€ข Synonym replacement
โ€ข Paraphrasing
โ€ข Context variation
โ€ข Original 12 examples โ†’ Enhanced to {len(training_data)} examples
"""
    
    return result


def get_training_data():
    """Display the training data used for the model"""
    training_data = [
        ("ืฉื›ื—ืชื™ ืืช ื”ืกื™ืกืžื” ืฉืœื™", "ืฉื›ื—ืช ืกื™ืกืžื”"),
        ("ืื™ืš ืื ื™ ืžื‘ื˜ืœ ืืช ื”ืžื ื•ื™?", "ื‘ื™ื˜ื•ืœ ืžื ื•ื™"),
        ("ืžื” ื”ืžื—ื™ืจ ืฉืœ ื”ืชื•ื›ื ื™ืช?", "ืฉืืœื” ื›ืœืœื™ืช"),
        ("ื”ืืชืจ ืœื ืขื•ื‘ื“ ืœื™", "ืชืžื™ื›ื” ื˜ื›ื ื™ืช"),
        ("ืื ื™ ืœื ืžืฆืœื™ื— ืœื”ืชื—ื‘ืจ", "ืชืžื™ื›ื” ื˜ื›ื ื™ืช"),
        ("ืื™ืš ืื ื™ ืžืฉื ื” ืืช ื›ืชื•ื‘ืช ื”ืื™ืžื™ื™ืœ?", "ืฉืืœื” ื›ืœืœื™ืช"),
        ("ืื ื™ ืจื•ืฆื” ืœืฉื“ืจื’ ืืช ื”ืชื•ื›ื ื™ืช ืฉืœื™", "ืฉืืœื” ื›ืœืœื™ืช"),
        ("ื”ื—ืฉื‘ื•ืŸ ืฉืœื™ ื ื ืขืœ", "ืชืžื™ื›ื” ื˜ื›ื ื™ืช"),
        ("ืื ื™ ืœื ืžืงื‘ืœ ืžื™ื™ืœื™ื", "ืชืžื™ื›ื” ื˜ื›ื ื™ืช"),
        ("ืื™ืš ืื ื™ ืจื•ืื” ืืช ื”ื—ืฉื‘ื•ื ื™ืช ืฉืœื™?", "ืฉืืœื” ื›ืœืœื™ืช"),
        ("ืื ื™ ืจื•ืฆื” ืœื‘ื˜ืœ ืืช ื”ืฉื™ืจื•ืช", "ื‘ื™ื˜ื•ืœ ืžื ื•ื™"),
        ("ืฉื›ื—ืชื™ ืืช ืคืจื˜ื™ ื”ื”ืชื—ื‘ืจื•ืช", "ืฉื›ื—ืช ืกื™ืกืžื”"),
        ("ืื™ื‘ื“ืชื™ ืืช ื”ืกื™ืกืžื”", "ืฉื›ื—ืช ืกื™ืกืžื”"),
        ("ืœื ื–ื•ื›ืจ ืืช ื”ืกื™ืกืžื”", "ืฉื›ื—ืช ืกื™ืกืžื”"),
        ("ื”ืกื™ืกืžื” ืœื ืขื•ื‘ื“ืช", "ืฉื›ื—ืช ืกื™ืกืžื”"),
        ("ืœื ืžืฆืœื™ื— ืœื”ื™ื›ื ืก ืขื ื”ืกื™ืกืžื”", "ืฉื›ื—ืช ืกื™ืกืžื”"),
        ("ืฆืจื™ืš ืœืืคืก ืืช ื”ืกื™ืกืžื”", "ืฉื›ื—ืช ืกื™ืกืžื”"),
        ("ื‘ืขื™ื” ืขื ื”ืกื™ืกืžื”", "ืฉื›ื—ืช ืกื™ืกืžื”"),
        ("ื”ืกื™ืกืžื” ืฉืœื™ ืœื ื ื›ื•ื ื”", "ืฉื›ื—ืช ืกื™ืกืžื”"),
        ("ืฉื›ื—ืชื™ ืžื” ื”ืกื™ืกืžื”", "ืฉื›ื—ืช ืกื™ืกืžื”"),
        ("ืื™ืš ืื ื™ ืžืฉื—ื–ืจ ืืช ื”ืกื™ืกืžื”", "ืฉื›ื—ืช ืกื™ืกืžื”"),
        ("ืจื•ืฆื” ืœืฉื ื•ืช ืืช ื”ืกื™ืกืžื”", "ืฉื›ื—ืช ืกื™ืกืžื”"),
        ("ื”ืกื™ืกืžื” ืœื ืžืชืงื‘ืœืช", "ืฉื›ื—ืช ืกื™ืกืžื”"),
        ("ื‘ืขื™ื™ืช ื”ืชื—ื‘ืจื•ืช - ืกื™ืกืžื”", "ืฉื›ื—ืช ืกื™ืกืžื”"),
        ("ืฆืจื™ืš ืขื–ืจื” ืขื ื”ืกื™ืกืžื”", "ืฉื›ื—ืช ืกื™ืกืžื”"),
        ("ืœื ื™ื•ื“ืข ืžื” ื”ืกื™ืกืžื” ืฉืœื™", "ืฉื›ื—ืช ืกื™ืกืžื”"),
        ("ืจื•ืฆื” ืœื‘ื˜ืœ ืืช ื”ืฉื™ืจื•ืช", "ื‘ื™ื˜ื•ืœ ืžื ื•ื™"),
        ("ืื™ืš ืžืคืกื™ืงื™ื ืืช ื”ืžื ื•ื™", "ื‘ื™ื˜ื•ืœ ืžื ื•ื™"),
        ("ืจื•ืฆื” ืœื”ืคืกื™ืง ืืช ื”ืชืฉืœื•ื", "ื‘ื™ื˜ื•ืœ ืžื ื•ื™"),
        ("ืื™ืš ื™ื•ืฆืื™ื ืžื”ืžื ื•ื™", "ื‘ื™ื˜ื•ืœ ืžื ื•ื™"),
        ("ื‘ืงืฉื” ืœื‘ื™ื˜ื•ืœ ืžื ื•ื™", "ื‘ื™ื˜ื•ืœ ืžื ื•ื™"),
        ("ืœื ืจื•ืฆื” ื™ื•ืชืจ ืืช ื”ืฉื™ืจื•ืช", "ื‘ื™ื˜ื•ืœ ืžื ื•ื™"),
        ("ืื™ืš ืžื‘ื˜ืœื™ื ืืช ื”ื—ืฉื‘ื•ืŸ", "ื‘ื™ื˜ื•ืœ ืžื ื•ื™"),
        ("ืจื•ืฆื” ืœืกื’ื•ืจ ืืช ื”ื—ืฉื‘ื•ืŸ", "ื‘ื™ื˜ื•ืœ ืžื ื•ื™"),
        ("ืขื–ืจื” ื‘ื‘ื™ื˜ื•ืœ ืžื ื•ื™", "ื‘ื™ื˜ื•ืœ ืžื ื•ื™"),
        ("ื”ืœื™ืš ื‘ื™ื˜ื•ืœ ื”ืžื ื•ื™", "ื‘ื™ื˜ื•ืœ ืžื ื•ื™"),
        ("ืžืขื•ื ื™ื™ืŸ ืœื‘ื˜ืœ", "ื‘ื™ื˜ื•ืœ ืžื ื•ื™"),
        ("ืื™ืš ืžืคืกื™ืงื™ื ืืช ื”ืฉื™ืจื•ืช", "ื‘ื™ื˜ื•ืœ ืžื ื•ื™"),
        ("ืจื•ืฆื” ืœื”ืคืกื™ืง ืืช ื”ื”ืจืฉืžื”", "ื‘ื™ื˜ื•ืœ ืžื ื•ื™"),
        ("ื‘ืงืฉื” ืœื”ืคืกืงืช ืฉื™ืจื•ืช", "ื‘ื™ื˜ื•ืœ ืžื ื•ื™"),
        ("ืžื” ื›ื•ืœืœ ื”ืฉื™ืจื•ืช", "ืฉืืœื” ื›ืœืœื™ืช"),
        ("ืื™ืœื• ืชื•ื›ื ื™ื•ืช ื™ืฉ ืœื›ื", "ืฉืืœื” ื›ืœืœื™ืช"),
        ("ื›ืžื” ืขื•ืœื” ื”ื—ื‘ื™ืœื”", "ืฉืืœื” ื›ืœืœื™ืช"),
        ("ืžื” ื”ื”ื‘ื“ืœ ื‘ื™ืŸ ื”ืชื•ื›ื ื™ื•ืช", "ืฉืืœื” ื›ืœืœื™ืช"),
        ("ืื™ืš ืื ื™ ืžืฉื ื” ืืช ื”ืคืจื˜ื™ื ืฉืœื™", "ืฉืืœื” ื›ืœืœื™ืช"),
        ("ืื™ืš ืืคืฉืจ ืœืฉื“ืจื’", "ืฉืืœื” ื›ืœืœื™ืช"),
        ("ืžื” ื”ืืคืฉืจื•ื™ื•ืช ืฉืœื›ื", "ืฉืืœื” ื›ืœืœื™ืช"),
        ("ืื ื™ ืจื•ืฆื” ืœืขื“ื›ืŸ ืคืจื˜ื™ื", "ืฉืืœื” ื›ืœืœื™ืช"),
        ("ืื™ืš ืจื•ืื™ื ืืช ื”ื”ื™ืกื˜ื•ืจื™ื”", "ืฉืืœื” ื›ืœืœื™ืช"),
        ("ื”ืืคืœื™ืงืฆื™ื” ืงื•ืจืกืช", "ืชืžื™ื›ื” ื˜ื›ื ื™ืช"),
        ("ื™ืฉ ื‘ืื’ ื‘ืืชืจ", "ืชืžื™ื›ื” ื˜ื›ื ื™ืช"),
        ("ื”ื“ืฃ ืœื ื ื˜ืขืŸ", "ืชืžื™ื›ื” ื˜ื›ื ื™ืช"),
        ("ืฉื’ื™ืื” ื‘ืžืขืจื›ืช", "ืชืžื™ื›ื” ื˜ื›ื ื™ืช"),
        ("ื”ื˜ื•ืขืŸ ืœื ืขื•ื‘ื“", "ืชืžื™ื›ื” ื˜ื›ื ื™ืช"),
        ("ื‘ืขื™ื” ื˜ื›ื ื™ืช", "ืชืžื™ื›ื” ื˜ื›ื ื™ืช"),
        ("ื”ืžืขืจื›ืช ืœื ืžื’ื™ื‘ื”", "ืชืžื™ื›ื” ื˜ื›ื ื™ืช"),
        ("ืฉื’ื™ืืช ื—ื™ื‘ื•ืจ", "ืชืžื™ื›ื” ื˜ื›ื ื™ืช"),
        ("ื”ื›ืคืชื•ืจ ืœื ืขื•ื‘ื“", "ืชืžื™ื›ื” ื˜ื›ื ื™ืช"),
        ("ื”ืชืžื•ื ื•ืช ืœื ื ื˜ืขื ื•ืช", "ืชืžื™ื›ื” ื˜ื›ื ื™ืช"),
        ("ื”ื•ื•ื™ื“ืื• ืœื ืžืชื ื’ืŸ", "ืชืžื™ื›ื” ื˜ื›ื ื™ืช"),
        ("ืื™ื˜ื™ื•ืช ื‘ืืชืจ", "ืชืžื™ื›ื” ื˜ื›ื ื™ืช")
    ]

    # Count examples per category
    category_counts = {}
    for _, label in training_data:
        category_counts[label] = category_counts.get(label, 0) + 1

    result = f"""
๐Ÿ“Š **Training Data Summary**
Total Examples: {len(training_data)}

๐Ÿ“ˆ **Examples per Category:**
"""

    # Add category statistics
    for category, count in sorted(category_counts.items()):
        percentage = (count / len(training_data)) * 100
        result += f"\nโ€ข {category}: {count} examples ({percentage:.1f}%)"

    result += f"""

๐Ÿ“ **Sample Training Examples:**

๐Ÿ” **ืฉื›ื—ืช ืกื™ืกืžื” (Password Reset):**
โ€ข ืฉื›ื—ืชื™ ืืช ื”ืกื™ืกืžื” ืฉืœื™
โ€ข ืœื ื–ื•ื›ืจ ืืช ื”ืกื™ืกืžื”  
โ€ข ื”ืกื™ืกืžื” ืœื ืขื•ื‘ื“ืช
โ€ข ืฆืจื™ืš ืœืืคืก ืืช ื”ืกื™ืกืžื”
โ€ข ืื™ืš ืื ื™ ืžืฉื—ื–ืจ ืืช ื”ืกื™ืกืžื”

โŒ **ื‘ื™ื˜ื•ืœ ืžื ื•ื™ (Cancel Subscription):**
โ€ข ืื™ืš ืื ื™ ืžื‘ื˜ืœ ืืช ื”ืžื ื•ื™?
โ€ข ืจื•ืฆื” ืœื”ืคืกื™ืง ืืช ื”ืชืฉืœื•ื
โ€ข ืœื ืจื•ืฆื” ื™ื•ืชืจ ืืช ื”ืฉื™ืจื•ืช
โ€ข ืื™ืš ืžื‘ื˜ืœื™ื ืืช ื”ื—ืฉื‘ื•ืŸ
โ€ข ื‘ืงืฉื” ืœื‘ื™ื˜ื•ืœ ืžื ื•ื™

โ“ **ืฉืืœื” ื›ืœืœื™ืช (General Question):**
โ€ข ืžื” ื”ืžื—ื™ืจ ืฉืœ ื”ืชื•ื›ื ื™ืช?
โ€ข ื›ืžื” ืขื•ืœื” ื”ื—ื‘ื™ืœื”
โ€ข ืื™ืœื• ืชื•ื›ื ื™ื•ืช ื™ืฉ ืœื›ื
โ€ข ืื™ืš ืื ื™ ืžืฉื ื” ืืช ื”ืคืจื˜ื™ื ืฉืœื™
โ€ข ืžื” ื›ื•ืœืœ ื”ืฉื™ืจื•ืช

๐Ÿ”ง **ืชืžื™ื›ื” ื˜ื›ื ื™ืช (Technical Support):**
โ€ข ื”ืืชืจ ืœื ืขื•ื‘ื“ ืœื™
โ€ข ื”ืืคืœื™ืงืฆื™ื” ืงื•ืจืกืช
โ€ข ื™ืฉ ื‘ืื’ ื‘ืืชืจ
โ€ข ื”ื“ืฃ ืœื ื ื˜ืขืŸ
โ€ข ืฉื’ื™ืื” ื‘ืžืขืจื›ืช

---
๐Ÿ’ก **Model was trained with data augmentation techniques:**
โ€ข Synonym replacement
โ€ข Paraphrasing
โ€ข Context variation
โ€ข Original 12 examples โ†’ Enhanced to {len(training_data)} examples
"""

    return result


# Create interface
with gr.Blocks(title="Hebrew Intent Classification - Debug") as demo:

    gr.Markdown("# ๐Ÿ‡ฎ๐Ÿ‡ฑ Hebrew Intent Classification - Debug Version 2.1 ๐Ÿ“Š")
    gr.Markdown("### ๐Ÿ”ข Version 2.1 - Training Data Display Added (Aug 12, 2025) โœ…")

    with gr.Tab("Classification"):
        with gr.Row():
            with gr.Column():
                text_input = gr.Textbox(
                    label="Hebrew Text:",
                    placeholder="ืฉื›ื—ืชื™ ืืช ื”ืกื™ืกืžื” ืฉืœื™",
                    lines=3
                )
                classify_btn = gr.Button("Classify", variant="primary")

                # Quick examples
                gr.Markdown("### Examples:")
                examples = [
                    "ืฉื›ื—ืชื™ ืืช ื”ืกื™ืกืžื” ืฉืœื™",
                    "ืจื•ืฆื” ืœื‘ื˜ืœ ืืช ื”ืžื ื•ื™",
                    "ื›ืžื” ืขื•ืœื” ื”ื—ื‘ื™ืœื”",
                    "ื”ืืชืจ ืœื ืขื•ื‘ื“"
                ]

                for example in examples:
                    gr.Button(example, size="sm").click(
                        lambda x=example: x, outputs=text_input
                    )

            with gr.Column():
                result_output = gr.Textbox(
                    label="Result:",
                    lines=12,
                    interactive=False
                )

                confidence_output = gr.Label(
                    label="Confidence Scores",
                    num_top_classes=4
                )

    with gr.Tab("Debug"):
        gr.Markdown("### Debug Information")

        with gr.Row():
            with gr.Column():
                test_btn = gr.Button("Test Model Loading")
                debug_output = gr.Textbox(
                    label="Debug Output:",
                    lines=15,
                    interactive=False
                )

                test_btn.click(
                    lambda: test_model_loading()[1],
                    outputs=debug_output
                )

                conn_btn = gr.Button("Test Repository Connection")
                conn_output = gr.Textbox(
                    label="Connection Test:",
                    lines=5,
                    interactive=False
                )

                conn_btn.click(
                    test_connection,
                    outputs=conn_output
                )

            with gr.Column():
                data_btn = gr.Button("Show Training Data")
                training_output = gr.Textbox(
                    label="Training Data:",
                    lines=20,
                    interactive=False
                )

                data_btn.click(
                    get_training_data,
                    outputs=training_output
                )

    # Connect classification
    classify_btn.click(
        classify_text,
        inputs=[text_input],
        outputs=[result_output, confidence_output]
    )

    text_input.submit(
        classify_text,
        inputs=[text_input],
        outputs=[result_output, confidence_output]
    )

if __name__ == "__main__":
    demo.launch(
        share=True,
        server_name="0.0.0.0",
        server_port=7860
    )

    # Connect classification
    classify_btn.click(
        classify_text,
        inputs=[text_input],
        outputs=[result_output, confidence_output]
    )

    text_input.submit(
        classify_text,
        inputs=[text_input],
        outputs=[result_output, confidence_output]
    )

if __name__ == "__main__":
    demo.launch(
        share=True,
        server_name="0.0.0.0",
        server_port=7860
    )