Spaces:
Sleeping
Sleeping
| """ | |
| Simplified cognitive processor for Codette | |
| """ | |
| from typing import List, Dict, Any, Optional | |
| try: | |
| import numpy as np | |
| except Exception: | |
| np = None | |
| from datetime import datetime | |
| class CognitiveProcessor: | |
| """Core processing engine for Codette responses""" | |
| MODES = { | |
| "logical": { | |
| "name": "Logical Analysis", | |
| "process": lambda q: f"Analyzing {q} systematically...", | |
| "weight": 0.9 | |
| }, | |
| "creative": { | |
| "name": "Creative Insight", | |
| "process": lambda q: f"Exploring creative approaches to {q}...", | |
| "weight": 0.8 | |
| }, | |
| "practical": { | |
| "name": "Practical Application", | |
| "process": lambda q: f"Considering practical implementations for {q}...", | |
| "weight": 0.85 | |
| } | |
| } | |
| def __init__(self): | |
| """Initialize cognitive processor with default modes""" | |
| self.active_modes = self.MODES.copy() | |
| self.processing_history = [] | |
| def process(self, | |
| query: str, | |
| route_node: Optional[str] = None, | |
| confidence: float = 0.5) -> Dict[str, Any]: | |
| """ | |
| Process query using active modes and routing information | |
| Args: | |
| query: Input text to process | |
| route_node: Optional BioKinetic mesh node | |
| confidence: Routing confidence score | |
| Returns: | |
| Dict with response and insights | |
| """ | |
| try: | |
| # Generate insights from each mode | |
| insights = [] | |
| weighted_responses = [] | |
| for mode_name, mode_info in self.active_modes.items(): | |
| # Apply confidence to weight | |
| effective_weight = mode_info["weight"] * confidence | |
| if effective_weight > 0.3: # Minimum threshold | |
| response = mode_info["process"](query) | |
| weighted_responses.append((response, effective_weight)) | |
| insights.append(f"{mode_info['name']}") | |
| # Combine responses based on weights | |
| if weighted_responses: | |
| # Sort by weight | |
| weighted_responses.sort(key=lambda x: x[1], reverse=True) | |
| # Take top response | |
| main_response = weighted_responses[0][0] | |
| else: | |
| main_response = f"Processing query: {query}" | |
| # Record processing | |
| self.processing_history.append({ | |
| "timestamp": str(datetime.now()), | |
| "query": query, | |
| "response": main_response, | |
| "route_node": route_node, | |
| "confidence": confidence | |
| }) | |
| # Prune history if too long | |
| if len(self.processing_history) > 10: | |
| self.processing_history = self.processing_history[-10:] | |
| return { | |
| "response": main_response, | |
| "insights": insights[:3] # Top 3 insights | |
| } | |
| except Exception as e: | |
| print(f"Error processing query: {e}") | |
| return { | |
| "response": f"I apologize, but I encountered an error processing your query.", | |
| "insights": ["Error recovery activated"] | |
| } | |
| def get_metrics(self) -> Dict[str, Any]: | |
| """Get processor metrics and status""" | |
| try: | |
| return { | |
| "active_modes": list(self.active_modes.keys()), | |
| "mode_weights": {mode: info["weight"] for mode, info in self.active_modes.items()}, | |
| "processing_history_length": len(self.processing_history) | |
| } | |
| except Exception as e: | |
| print(f"Error getting metrics: {e}") | |
| return {} |