# Example of how to integrate the granite_model.py into your main app.py

# At the top of your app.py, add this import:
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="ibm-granite/granite-3.3-8b-instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)
try:
    from granite_model import GraniteModelIntegration
    GRANITE_AVAILABLE = True
except ImportError:
    GRANITE_AVAILABLE = False
    logger.warning("granite_model.py not found. Granite features will be disabled.")

# In your AdvancedDocumentSummarizer.__init__ method, add:
def __init__(self):
    self.summarizer = None
    self.sentiment_analyzer = None
    self.granite_integration = None  # Add this line
    self.cache = {}
    
    # Initialize AI models
    if TRANSFORMERS_AVAILABLE:
        self._initialize_ai_models()
    
    # Initialize Granite integration
    if GRANITE_AVAILABLE:
        try:
            self.granite_integration = GraniteModelIntegration()
            logger.info(f"Granite integration status: {'Available' if self.granite_integration.is_available() else 'Not Available'}")
        except Exception as e:
            logger.warning(f"Failed to initialize Granite integration: {e}")
    
    # Initialize sentiment analyzer
    if NLTK_AVAILABLE:
        try:
            self.sentiment_analyzer = SentimentIntensityAnalyzer()
        except Exception as e:
            logger.warning(f"Failed to initialize sentiment analyzer: {e}")

# Add these methods to your AdvancedDocumentSummarizer class:
def granite_enhanced_summary(self, text: str, summary_type: str = "medium") -> str:
    """Generate enhanced summary using Granite model"""
    if not (self.granite_integration and self.granite_integration.is_available()):
        return self.advanced_extractive_summary(text)
    
    return self.granite_integration.generate_summary(text, summary_type)

def granite_analyze_document(self, text: str) -> Dict:
    """Use Granite model for advanced document analysis"""
    if not (self.granite_integration and self.granite_integration.is_available()):
        return {'analysis_available': False}
    
    result = self.granite_integration.analyze_document(text)
    return {
        'granite_analysis': result.get('analysis', 'Analysis failed'),
        'analysis_available': result.get('success', False),
        'model_used': result.get('model_used', 'Unknown')
    }

def granite_generate_questions(self, text: str, num_questions: int = 5) -> list:
    """Generate comprehension questions using Granite"""
    if not (self.granite_integration and self.granite_integration.is_available()):
        return []
    
    return self.granite_integration.generate_questions(text, num_questions)

# In your process_document method, update the summary generation part:
# Generate summary - prioritize Granite if available for AI mode
if summary_type == "ai":
    if self.granite_integration and self.granite_integration.is_available():
        summary = self.granite_enhanced_summary(text, summary_length)
    elif self.summarizer:
        summary = self.ai_summary(text, params["max_length"], params["min_length"])
    else:
        summary = self.advanced_extractive_summary(text, params["sentences"])
else:
    summary = self.advanced_extractive_summary(text, params["sentences"])

# Get Granite analysis and questions if available
granite_analysis = self.granite_analyze_document(text)
granite_questions = self.granite_generate_questions(text, 5)

# Add to result dictionary:
result = {
    'original_text': text[:2000] + "..." if len(text) > 2000 else text,
    'full_text_length': len(text),
    'summary': summary,
    'key_points': key_points,
    'outline': outline,
    'stats': stats,
    'granite_analysis': granite_analysis,
    'granite_questions': granite_questions,  # Add this
    'readability_score': readability_score,
    'file_name': Path(file_path).name,
    'file_size': os.path.getsize(file_path),
    'processing_time': datetime.now().isoformat(),
    'summary_type': summary_type,
    'summary_length': summary_length,
    'model_used': 'Granite 3.2 8B' if (summary_type == "ai" and self.granite_integration and self.granite_integration.is_available()) else ('AI (BART/T5)' if self.summarizer else 'Extractive')
}

# In your UI section, add the questions display:
# Add Granite questions if available
granite_questions_html = ""
if result.get("granite_questions"):
    questions_list = "".join([f"<li style='margin-bottom: 10px; padding: 8px; background: rgba(255,255,255,0.1); border-radius: 6px;'>{q}</li>" 
                             for q in result["granite_questions"]])
    granite_questions_html = f'''
    <div style="background: linear-gradient(135deg, #11998e 0%, #38ef7d 100%); color: white; padding: 20px; border-radius: 12px; margin: 15px 0; box-shadow: 0 6px 20px rgba(0,0,0,0.1);">
        <h3>AI-Generated Questions</h3>
        <p style="margin-bottom: 15px; opacity: 0.9;">Test your understanding with these Granite-generated questions:</p>
        <ol style="padding-left: 20px; line-height: 1.6;">
            {questions_list}
        </ol>
    </div>
    '''

# Update your system status display:
**Granite 3.2 8B:** {"✅ Available" if (GRANITE_AVAILABLE and hasattr(summarizer, 'granite_integration') and summarizer.granite_integration and summarizer.granite_integration.is_available()) else "❌ Not Available"}