services/analysis_service.py

"""Service for analysis operations: processing arguments, extracting topics, and predicting stance"""

import logging
from typing import List, Dict, Optional
from datetime import datetime

from services.database_service import database_service
from services.topic_similarity_service import topic_similarity_service
from services.stance_model_manager import stance_model_manager

logger = logging.getLogger(__name__)


class AnalysisService:
    """Service for analyzing arguments with topic extraction and stance prediction"""
    
    def __init__(self):
        self.table_name = "analysis_results"
    
    def _get_client(self):
        """Get Supabase client"""
        return database_service.get_client()
    
    def analyze_arguments(
        self,
        user_id: str,
        arguments: List[str]
    ) -> List[Dict]:
        """
        Analyze arguments: extract topics, predict stance, and save to database
        
        Args:
            user_id: User UUID
            arguments: List of argument texts
            
        Returns:
            List of analysis results with argument, topic, and stance prediction
        """
        try:
            if not arguments or len(arguments) == 0:
                raise ValueError("Arguments list cannot be empty")
            
            logger.info(f"Starting analysis for {len(arguments)} arguments for user {user_id}")
            
            # Step 1: Find most similar topics for all arguments
            logger.info("Step 1: Finding most similar topics...")
            topics = topic_similarity_service.batch_find_similar_topics(arguments)
            
            if len(topics) != len(arguments):
                raise RuntimeError(f"Topic extraction returned {len(topics)} topics but expected {len(arguments)}")
            
            # Step 2: Predict stance for each argument-topic pair
            logger.info("Step 2: Predicting stance for argument-topic pairs...")
            stance_results = []
            for arg, topic in zip(arguments, topics):
                if topic is None:
                    logger.warning(f"Skipping argument with null topic: {arg[:50]}...")
                    continue
                
                stance_result = stance_model_manager.predict(topic, arg)
                stance_results.append({
                    "argument": arg,
                    "topic": topic,
                    "predicted_stance": stance_result["predicted_stance"],
                    "confidence": stance_result["confidence"],
                    "probability_con": stance_result["probability_con"],
                    "probability_pro": stance_result["probability_pro"],
                })
            
            # Step 3: Save all results to database
            logger.info(f"Step 3: Saving {len(stance_results)} results to database...")
            saved_results = self._save_analysis_results(user_id, stance_results)
            
            logger.info(f"Analysis completed: {len(saved_results)} results saved")
            return saved_results
            
        except Exception as e:
            logger.error(f"Error in analyze_arguments: {str(e)}")
            raise RuntimeError(f"Analysis failed: {str(e)}")
    
    def _save_analysis_results(
        self,
        user_id: str,
        results: List[Dict]
    ) -> List[Dict]:
        """
        Save analysis results to database
        
        Args:
            user_id: User UUID
            results: List of analysis result dictionaries
            
        Returns:
            List of saved results with database IDs
        """
        try:
            client = self._get_client()
            
            # Prepare data for batch insert
            insert_data = []
            for result in results:
                insert_data.append({
                    "user_id": user_id,
                    "argument": result["argument"],
                    "topic": result["topic"],
                    "predicted_stance": result["predicted_stance"],
                    "confidence": result["confidence"],
                    "probability_con": result["probability_con"],
                    "probability_pro": result["probability_pro"],
                    "created_at": datetime.utcnow().isoformat(),
                    "updated_at": datetime.utcnow().isoformat()
                })
            
            # Batch insert
            response = client.table(self.table_name).insert(insert_data).execute()
            
            if response.data:
                logger.info(f"Successfully saved {len(response.data)} analysis results")
                return response.data
            else:
                raise RuntimeError("Failed to save analysis results: no data returned")
                
        except Exception as e:
            logger.error(f"Error saving analysis results: {str(e)}")
            raise RuntimeError(f"Failed to save analysis results: {str(e)}")
    
    def get_user_analysis_results(
        self,
        user_id: str,
        limit: Optional[int] = 100,
        offset: Optional[int] = 0
    ) -> List[Dict]:
        """
        Get analysis results for a user
        
        Args:
            user_id: User UUID
            limit: Maximum number of results to return
            offset: Number of results to skip
            
        Returns:
            List of analysis results
        """
        try:
            client = self._get_client()
            
            query = client.table(self.table_name)\
                .select("*")\
                .eq("user_id", user_id)\
                .order("created_at", desc=True)\
                .limit(limit)\
                .offset(offset)
            
            result = query.execute()
            
            return result.data if result.data else []
                
        except Exception as e:
            logger.error(f"Error getting user analysis results: {str(e)}")
            raise RuntimeError(f"Failed to get analysis results: {str(e)}")


# Initialize singleton instance
analysis_service = AnalysisService()