space/multimodal.py

"""Advanced multimodal reasoning combining different types of information."""

import logging
from typing import Dict, Any, List, Optional, Set, Union, Type, Tuple
import json
from dataclasses import dataclass, field
from enum import Enum
from datetime import datetime
import numpy as np
from collections import defaultdict

from .base import ReasoningStrategy

@dataclass
class ModalityFeatures:
    """Features extracted from different modalities."""
    text: List[Dict[str, Any]]
    image: Optional[List[Dict[str, Any]]] = None
    audio: Optional[List[Dict[str, Any]]] = None
    video: Optional[List[Dict[str, Any]]] = None
    structured: Optional[List[Dict[str, Any]]] = None

class MultiModalReasoning(ReasoningStrategy):
    """
    Advanced multimodal reasoning that:
    1. Processes different types of information
    2. Aligns cross-modal features
    3. Integrates multimodal context
    4. Generates coherent responses
    5. Handles uncertainty
    """
    
    def __init__(self, config: Optional[Dict[str, Any]] = None):
        """Initialize multimodal reasoning."""
        super().__init__()
        self.config = config or {}
        
        # Standard reasoning parameters
        self.min_confidence = self.config.get('min_confidence', 0.7)
        self.parallel_threshold = self.config.get('parallel_threshold', 3)
        self.learning_rate = self.config.get('learning_rate', 0.1)
        self.strategy_weights = self.config.get('strategy_weights', {
            "LOCAL_LLM": 0.8,
            "CHAIN_OF_THOUGHT": 0.6,
            "TREE_OF_THOUGHTS": 0.5,
            "META_LEARNING": 0.4
        })
        
        # Configure model repositories
        self.models = self.config.get('models', {
            'img2img': {
                'repo_id': 'enhanceaiteam/Flux-Uncensored-V2',
                'filename': 'Flux-Uncensored-V2.safetensors'
            },
            'img2vid': {
                'repo_id': 'stabilityai/stable-video-diffusion-img2vid-xt',
                'filename': 'svd_xt.safetensors'
            },
            'any2any': {
                'repo_id': 'deepseek-ai/JanusFlow-1.3B',
                'filename': 'janusflow-1.3b.safetensors'
            }
        })
        
        # Configure modality weights
        self.weights = self.config.get('modality_weights', {
            'text': 0.4,
            'image': 0.3,
            'audio': 0.1,
            'video': 0.1,
            'structured': 0.1
        })
    
    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
        """
        Apply multimodal reasoning to process and integrate different types of information.
        
        Args:
            query: The input query to reason about
            context: Additional context and parameters
            
        Returns:
            Dict containing reasoning results and confidence scores
        """
        try:
            # Process across modalities
            modalities = await self._process_modalities(query, context)
            
            # Align cross-modal information
            alignment = await self._cross_modal_alignment(modalities, context)
            
            # Integrate aligned information
            integration = await self._integrated_analysis(alignment, context)
            
            # Generate final response
            response = await self._generate_response(integration, context)
            
            return {
                'answer': response.get('text', ''),
                'confidence': self._calculate_confidence(integration),
                'modalities': modalities,
                'alignment': alignment,
                'integration': integration
            }
            
        except Exception as e:
            logging.error(f"Multimodal reasoning failed: {str(e)}")
            return {
                'error': f"Multimodal reasoning failed: {str(e)}",
                'confidence': 0.0
            }
    
    async def _process_modalities(
        self,
        query: str,
        context: Dict[str, Any]
    ) -> Dict[str, List[Dict[str, Any]]]:
        """Process query across different modalities."""
        modalities = {}
        
        # Process text
        if 'text' in context:
            modalities['text'] = self._process_text(context['text'])
            
        # Process images
        if 'images' in context:
            modalities['image'] = self._process_images(context['images'])
            
        # Process audio
        if 'audio' in context:
            modalities['audio'] = self._process_audio(context['audio'])
            
        # Process video
        if 'video' in context:
            modalities['video'] = self._process_video(context['video'])
            
        # Process structured data
        if 'structured' in context:
            modalities['structured'] = self._process_structured(context['structured'])
            
        return modalities
    
    async def _cross_modal_alignment(
        self,
        modalities: Dict[str, List[Dict[str, Any]]],
        context: Dict[str, Any]
    ) -> List[Dict[str, Any]]:
        """Align information across different modalities."""
        alignments = []
        
        # Get all modality pairs
        modality_pairs = [
            (m1, m2) for i, m1 in enumerate(modalities.keys())
            for m2 in list(modalities.keys())[i+1:]
        ]
        
        # Align each pair
        for mod1, mod2 in modality_pairs:
            items1 = modalities[mod1]
            items2 = modalities[mod2]
            
            # Calculate cross-modal similarities
            for item1 in items1:
                for item2 in items2:
                    similarity = self._calculate_similarity(item1, item2)
                    if similarity > 0.7:  # Alignment threshold
                        alignments.append({
                            'modality1': mod1,
                            'modality2': mod2,
                            'item1': item1,
                            'item2': item2,
                            'similarity': similarity
                        })
        
        return alignments
    
    def _calculate_similarity(
        self,
        item1: Dict[str, Any],
        item2: Dict[str, Any]
    ) -> float:
        """Calculate similarity between two items from different modalities."""
        # Simple feature overlap for now
        features1 = set(str(v) for v in item1.values())
        features2 = set(str(v) for v in item2.values())
        
        if not features1 or not features2:
            return 0.0
            
        overlap = len(features1.intersection(features2))
        total = len(features1.union(features2))
        
        return overlap / total if total > 0 else 0.0
    
    async def _integrated_analysis(
        self,
        alignment: List[Dict[str, Any]],
        context: Dict[str, Any]
    ) -> List[Dict[str, Any]]:
        """Perform integrated analysis of aligned information."""
        integrated = []
        
        # Group alignments by similarity
        similarity_groups = defaultdict(list)
        for align in alignment:
            similarity_groups[align['similarity']].append(align)
        
        # Process groups in order of similarity
        for similarity, group in sorted(
            similarity_groups.items(),
            key=lambda x: x[0],
            reverse=True
        ):
            # Combine aligned features
            for align in group:
                integrated.append({
                    'features': {
                        **align['item1'],
                        **align['item2']
                    },
                    'modalities': [align['modality1'], align['modality2']],
                    'confidence': align['similarity']
                })
        
        return integrated
    
    async def _generate_response(
        self,
        integration: List[Dict[str, Any]],
        context: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Generate coherent response from integrated analysis."""
        if not integration:
            return {'text': '', 'confidence': 0.0}
        
        # Combine all integrated features
        all_features = {}
        for item in integration:
            all_features.update(item['features'])
        
        # Generate response text
        response_text = []
        
        # Add main findings
        response_text.append("Main findings across modalities:")
        for feature, value in all_features.items():
            response_text.append(f"- {feature}: {value}")
        
        # Add confidence
        confidence = sum(item['confidence'] for item in integration) / len(integration)
        response_text.append(f"\nOverall confidence: {confidence:.2f}")
        
        return {
            'text': "\n".join(response_text),
            'confidence': confidence
        }
    
    def _calculate_confidence(self, integration: List[Dict[str, Any]]) -> float:
        """Calculate overall confidence score."""
        if not integration:
            return 0.0
        
        # Base confidence
        confidence = 0.5
        
        # Adjust based on number of modalities
        unique_modalities = set()
        for item in integration:
            unique_modalities.update(item['modalities'])
        
        modality_bonus = len(unique_modalities) * 0.1
        confidence += min(modality_bonus, 0.3)
        
        # Adjust based on integration quality
        avg_similarity = sum(
            item['confidence'] for item in integration
        ) / len(integration)
        confidence += avg_similarity * 0.2
        
        return min(confidence, 1.0)

    def _process_text(self, text: str) -> List[Dict[str, Any]]:
        """Process text modality."""
        # Simple text processing for now
        return [{'text': text}]

    def _process_images(self, images: List[str]) -> List[Dict[str, Any]]:
        """Process image modality."""
        # Simple image processing for now
        return [{'image': image} for image in images]

    def _process_audio(self, audio: List[str]) -> List[Dict[str, Any]]:
        """Process audio modality."""
        # Simple audio processing for now
        return [{'audio': audio_file} for audio_file in audio]

    def _process_video(self, video: List[str]) -> List[Dict[str, Any]]:
        """Process video modality."""
        # Simple video processing for now
        return [{'video': video_file} for video_file in video]

    def _process_structured(self, structured: Dict[str, Any]) -> List[Dict[str, Any]]:
        """Process structured data modality."""
        # Simple structured data processing for now
        return [{'structured': structured}]