"""Status checking utilities."""
import os
import json
import pandas as pd
from data.manager import (
    ENTITIES_PATH, MODEL_OUTPUT_DIR, AUDIO_DIR
)
from data.loader import get_hf_dataset, HF_DATASET_NAME, is_dataset_loaded, get_train_dataframe, get_test_dataframe


def check_setup_status():
    """Check the status of various setup components"""
    _hf_dataset = get_hf_dataset()
    
    # Check if dataset is loaded and has train/test splits
    train_data_available = False
    test_data_available = False
    train_count = 0
    test_count = 0
    
    if _hf_dataset is not None:
        if 'train' in _hf_dataset:
            train_data_available = True
            train_count = len(_hf_dataset['train'])
        if 'test' in _hf_dataset:
            test_data_available = True
            test_count = len(_hf_dataset['test'])
    
    status = {
        "entities_extracted": os.path.exists(ENTITIES_PATH),
        "model_trained": os.path.exists(MODEL_OUTPUT_DIR),
        "train_data": train_data_available,
        "test_data": test_data_available,
        "train_count": train_count,
        "test_count": test_count,
        "audio_files": os.path.exists(AUDIO_DIR) and len([f for f in os.listdir(AUDIO_DIR) if f.endswith('.wav')]) > 0,
        "audio_from_hf": False,
        "audio_count_hf": 0,
    }
    
    # Check if audio is available from HF dataset
    if _hf_dataset is None:
        try:
            from datasets import load_dataset
            hf_token = os.getenv("HF_TOKEN")
            _hf_dataset = load_dataset(HF_DATASET_NAME, token=hf_token)
        except:
            pass
    
    if _hf_dataset is not None:
        # Check if audio column exists without iterating (which would trigger decoding)
        # Even accessing .features might trigger torchcodec requirement, so wrap carefully
        audio_count = 0
        try:
            # Try to check features - this might trigger torchcodec ImportError
            has_train_audio = False
            has_test_audio = False
            
            try:
                if 'train' in _hf_dataset:
                    features = _hf_dataset['train'].features
                    has_train_audio = 'audio' in features if features else False
            except ImportError as e:
                if 'torchcodec' in str(e).lower():
                    # Can't check - torchcodec not available
                    has_train_audio = False
                else:
                    raise
            
            try:
                if 'test' in _hf_dataset:
                    features = _hf_dataset['test'].features
                    has_test_audio = 'audio' in features if features else False
            except ImportError as e:
                if 'torchcodec' in str(e).lower():
                    # Can't check - torchcodec not available
                    has_test_audio = False
                else:
                    raise
            
            if has_train_audio:
                status["audio_from_hf"] = True
                try:
                    audio_count = len(_hf_dataset['train'])
                except:
                    pass
            if has_test_audio:
                status["audio_from_hf"] = True
                try:
                    audio_count += len(_hf_dataset['test'])
                except:
                    pass
            
            if audio_count > 0:
                status["audio_count_hf"] = audio_count
                
        except ImportError as e:
            if 'torchcodec' in str(e).lower():
                # torchcodec not installed - can't check audio features
                # Don't set audio_from_hf to avoid false positives
                pass
            else:
                raise
        except Exception as e:
            # Other errors - don't fail, just skip audio check
            if 'torchcodec' not in str(e).lower():
                print(f"⚠ Warning checking audio features: {e}")
    
    # Check ESPnet
    try:
        from espnet2.bin.s2t_inference import Speech2Text
        status["espnet_installed"] = True
    except ImportError:
        status["espnet_installed"] = False
    
    # Check Flash Attention (optional optimization)
    try:
        import flash_attn
        status["flash_attention_available"] = True
    except ImportError:
        status["flash_attention_available"] = False
    
    # Get entity count if exists
    if status["entities_extracted"]:
        try:
            with open(ENTITIES_PATH, 'r') as f:
                data = json.load(f)
                status["entity_count"] = len(data.get('entities', []))
        except:
            status["entity_count"] = 0
    else:
        status["entity_count"] = 0
    
    return status


def get_data_loading_status():
    """Get formatted data loading status for Load Data tab"""
    status = check_setup_status()
    
    status_text = """
### 📊 Current Data Status

"""
    
    # Train data status
    if status['train_data']:
        status_text += f"✅ **Training Data**: Available from dataset ({status['train_count']:,} samples)\n"
    else:
        status_text += "❌ **Training Data**: Not loaded from dataset\n"
    
    # Test data status
    if status['test_data']:
        status_text += f"✅ **Test Data**: Available from dataset ({status['test_count']:,} samples)\n"
    else:
        status_text += "❌ **Test Data**: Not loaded from dataset\n"
    
    # Audio files status - show HF dataset status if available
    if status['audio_from_hf']:
        status_text += f"✅ **Audio Files**: Available from HF Dataset ({status['audio_count_hf']:,} files)\n"
        status_text += "   Audio files are loaded directly from the dataset on-demand.\n"
    elif status['audio_files']:
        try:
            audio_count = len([f for f in os.listdir(AUDIO_DIR) if f.endswith('.wav')])
            status_text += f"✅ **Audio Files**: Available locally ({audio_count:,} files)\n"
        except:
            status_text += "✅ **Audio Files**: Available locally\n"
    else:
        status_text += "⚠️ **Audio Files**: Not loaded (will be loaded on-demand from HF Dataset if available)\n"
    
    # Check if data was auto-loaded from HF
    if status['train_data'] or status['test_data']:
        status_text += f"\n💡 **Note**: Data is automatically loaded from `{HF_DATASET_NAME}` on startup.\n"
        if status['audio_from_hf']:
            status_text += "Audio files are accessed directly from the HF dataset (no local storage needed).\n"
    else:
        status_text += f"\n💡 **Note**: Data will be automatically loaded from `{HF_DATASET_NAME}` on startup.\n"
        status_text += "If it hasn't loaded yet, you can manually load it below.\n"
    
    return status_text


def get_status_display():
    """Get formatted status display with project insights"""
    status = check_setup_status()
    
    # Calculate project insights
    train_samples = status.get('train_count', 0)
    test_samples = status.get('test_count', 0)
    audio_count = 0
    avg_transcription_length = 0
    vocab_size = 0
    
    # Try to get vocabulary stats from training data if available
    if status['train_data']:
        try:
            train_df = get_train_dataframe()
            if 'Transcription' in train_df.columns:
                avg_length = train_df['Transcription'].str.len().mean()
                avg_transcription_length = avg_length
                # Estimate vocabulary size
                all_words = set()
                for text in train_df['Transcription'].dropna():
                    all_words.update(text.lower().split())
                vocab_size = len(all_words)
        except:
            pass
    
    # Check audio files - prefer HF dataset count
    if status['audio_from_hf']:
        audio_count = status['audio_count_hf']
    elif status['audio_files']:
        try:
            audio_count = len([f for f in os.listdir(AUDIO_DIR) if f.endswith('.wav')])
        except:
            audio_count = 0
    else:
        audio_count = 0
    
    status_text = """
<div class="homepage-header">
<h1>🏝️ Caribbean Voices Hackathon</h1>
<p class="subtitle">OWSM v3.1 Training & Inference Platform</p>
</div>

<div class="insights-section">
<h2>📈 Project Insights</h2>
<div class="insights-grid">
"""
    
    # Add colorful insight cards
    if train_samples > 0:
        status_text += f"""
<div class="insight-card card-blue">
<div class="insight-number">{train_samples:,}</div>
<div class="insight-label">Training Samples</div>
</div>
"""
    
    if test_samples > 0:
        status_text += f"""
<div class="insight-card card-green">
<div class="insight-number">{test_samples:,}</div>
<div class="insight-label">Test Samples</div>
</div>
"""
    
    if audio_count > 0:
        status_text += f"""
<div class="insight-card card-purple">
<div class="insight-number">{audio_count:,}</div>
<div class="insight-label">Audio Files</div>
</div>
"""
    
    if vocab_size > 0:
        status_text += f"""
<div class="insight-card card-orange">
<div class="insight-number">{vocab_size:,}</div>
<div class="insight-label">Unique Words</div>
</div>
"""
    
    if status['entities_extracted']:
        status_text += f"""
<div class="insight-card card-teal">
<div class="insight-number">{status['entity_count']}</div>
<div class="insight-label">Caribbean Entities</div>
</div>
"""
    
    status_text += """
</div>
</div>

<div class="status-section">
<h2>🔧 System Status</h2>
<div class="status-grid">
"""
    
    # Data Files Status
    status_text += """
<div class="status-group">
<h3>📁 Data Files</h3>
"""
    status_text += f"<div class='status-item'>{'✅' if status['train_data'] else '❌'} <strong>Training Data:</strong> {'Available from dataset' if status['train_data'] else 'Not loaded'}"
    if train_samples > 0:
        status_text += f" ({train_samples:,} samples)"
    status_text += "</div>\n"
    
    status_text += f"<div class='status-item'>{'✅' if status['test_data'] else '❌'} <strong>Test Data:</strong> {'Available from dataset' if status['test_data'] else 'Not loaded'}"
    if test_samples > 0:
        status_text += f" ({test_samples:,} samples)"
    status_text += "</div>\n"
    
    # Audio files status - show HF dataset status if available
    if status['audio_from_hf']:
        status_text += f"<div class='status-item'>✅ <strong>Audio Files:</strong> Available from HF Dataset ({audio_count:,} files)</div>\n"
    elif status['audio_files']:
        status_text += f"<div class='status-item'>✅ <strong>Audio Files:</strong> Available locally ({audio_count:,} files)</div>\n"
    else:
        status_text += f"<div class='status-item'>⚠️ <strong>Audio Files:</strong> Not Loaded</div>\n"
    status_text += "</div>\n"
    
    # OWSM Setup Status
    status_text += """
<div class="status-group">
<h3>🤖 OWSM Setup</h3>
"""
    status_text += f"<div class='status-item'>{'✅' if status['espnet_installed'] else '❌'} <strong>ESPnet:</strong> {'Installed' if status['espnet_installed'] else 'Not Installed'}</div>\n"
    flash_attn_status = status.get('flash_attention_available', False)
    if flash_attn_status:
        status_text += f"<div class='status-item'>✅ <strong>Flash Attention:</strong> Available (A10G GPU optimized)</div>\n"
    else:
        status_text += f"<div class='status-item'>⚠️ <strong>Flash Attention:</strong> Not Available (should be installed for A10G GPU)</div>\n"
    status_text += f"<div class='status-item'>{'✅' if status['entities_extracted'] else '❌'} <strong>Entities:</strong> {'Extracted' if status['entities_extracted'] else 'Not Extracted'}"
    if status['entities_extracted']:
        status_text += f" ({status['entity_count']} entities)"
    status_text += "</div>\n"
    status_text += f"<div class='status-item'>{'✅' if status['model_trained'] else '❌'} <strong>Model:</strong> {'Trained' if status['model_trained'] else 'Not Trained'}</div>\n"
    status_text += "</div>\n"
    
    status_text += """
</div>
</div>

<div class="workflow-section">
<h2>🚀 Quick Navigation</h2>
<p class="workflow-description">Jump to the tools you need to work with the Caribbean Voices dataset:</p>
</div>
"""
    
    return status_text