Upload folder using huggingface_hub

.env.example

@@ -0,0 +1,90 @@
+# Hugging Face Configuration
+# Get your token from https://huggingface.co/settings/tokens
+# Make sure to select "WRITE" access when creating the token
+HUGGINGFACE_TOKEN=your_huggingface_token_here
+
+# System Configuration
+DEBUG_MODE=False
+LOG_LEVEL=INFO
+MAX_WORKERS=4
+ASYNC_TIMEOUT=30
+
+# Resource Limits
+MAX_MEMORY_MB=8192
+MAX_CPU_PERCENT=90
+MAX_GPU_MEMORY_MB=4096
+MAX_API_CALLS_PER_MINUTE=500
+
+# Team Configuration
+MIN_TEAM_SIZE=2
+MAX_TEAM_SIZE=10
+MAX_CONCURRENT_OBJECTIVES=5
+
+# Error Recovery
+MAX_RETRIES=3
+RETRY_DELAY_SECONDS=5
+ERROR_THRESHOLD=0.2
+
+# Monitoring
+METRICS_INTERVAL_SECONDS=60
+HEALTH_CHECK_INTERVAL=30
+PERFORMANCE_LOG_RETENTION_DAYS=7
+
+# API Keys
+# Get your Hugging Face token from https://huggingface.co/settings/tokens
+# Required for uploading to Spaces - must have WRITE access
+HUGGINGFACE_API_KEY=your_huggingface_api_key
+
+# Optional API keys for additional features
+OPENAI_API_KEY=your_openai_api_key
+GROQ_API_KEY=your_groq_api_key
+
+# Service Configuration
+PORT=7860
+HOST=0.0.0.0
+DEBUG=True
+ENVIRONMENT=development
+
+# Database Configuration
+DATABASE_URL=sqlite:///./ventures.db
+
+# Model Configuration
+MODEL_CACHE_DIR=./model_cache
+DEFAULT_MODEL=gpt-4-turbo-preview
+
+# Venture Configuration
+MIN_PROFIT_TARGET=1000000
+DEFAULT_CURRENCY=USD
+RISK_TOLERANCE=medium
+
+# API Configuration
+API_VERSION=v1
+API_PREFIX=/api/v1
+CORS_ORIGINS=["*"]
+MAX_REQUEST_SIZE=10MB
+
+# Monitoring Configuration
+ENABLE_METRICS=True
+METRICS_PORT=9090
+LOG_LEVEL=INFO
+
+# Cache Configuration
+REDIS_URL=redis://localhost:6379/0
+CACHE_TTL=3600
+
+# Security Configuration
+JWT_SECRET=your_jwt_secret
+JWT_ALGORITHM=HS256
+ACCESS_TOKEN_EXPIRE_MINUTES=30
+
+# Feature Flags
+ENABLE_PORTFOLIO_OPTIMIZATION=True
+ENABLE_MARKET_ANALYSIS=True
+ENABLE_MONETIZATION_STRATEGY=True
+ENABLE_VENTURE_ANALYSIS=True
+
+# Note: After copying this file to .env:
+# 1. Replace 'your_huggingface_token_here' with your actual token
+# 2. Make sure your token has WRITE access for Spaces
+# 3. Keep this .env.example file for reference
+# 4. Never commit your actual .env file with real tokens

meta_learning.py

@@ -42,22 +42,38 @@ class LearningMetrics:
 class MetaLearningSystem:
     """Meta-learning system for optimizing learning strategies"""
     
-    def __init__(self):
+    def __init__(self, config: Optional[Dict[str, Any]] = None):
         self.logger = logging.getLogger(__name__)
+        self.config = config or {}
         
-        # Initialize quantum system with consistent configuration
+        # 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
+        })
+        
+        # Initialize quantum system with shared config
         quantum_config = {
-            "min_confidence": 0.7,
-            "parallel_threshold": 3,
-            "learning_rate": 0.1,
-            "strategy_weights": {
-                "LOCAL_LLM": 0.8,
-                "CHAIN_OF_THOUGHT": 0.6,
-                "TREE_OF_THOUGHTS": 0.5,
-                "META_LEARNING": 0.4
-            }
+            'min_confidence': self.min_confidence,
+            'parallel_threshold': self.parallel_threshold,
+            'learning_rate': self.learning_rate,
+            'strategy_weights': self.strategy_weights,
+            'num_qubits': self.config.get('num_qubits', 8),
+            'entanglement_strength': self.config.get('entanglement_strength', 0.5),
+            'interference_threshold': self.config.get('interference_threshold', 0.3),
+            'tunneling_rate': self.config.get('tunneling_rate', 0.1),
+            'annealing_schedule': self.config.get('annealing_schedule', {
+                'initial_temp': 1.0,
+                'final_temp': 0.01,
+                'steps': 100,
+                'cooling_rate': 0.95
+            })
         }
-        
         self.quantum_system = QuantumLearningSystem(quantum_config)
         self.strategies = {}
         self.performance_history = []

multimodal_reasoning.py

@@ -5,7 +5,7 @@ Implements reasoning across different types of information.
 """
 
 import logging
-from typing import Dict, Any, List
+from typing import Dict, Any, List, Optional
 from datetime import datetime
 import json
 import numpy as np
@@ -14,6 +14,34 @@ from .reasoning import ReasoningStrategy
 class MultiModalReasoning(ReasoningStrategy):
     """Implements multi-modal reasoning across different types of information."""
     
+    def __init__(self, config: Optional[Dict[str, Any]] = None):
+        """Initialize multi-modal 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
+        })
+        
+        # Multi-modal specific parameters
+        self.modality_weights = self.config.get('modality_weights', {
+            'text': 0.8,
+            'image': 0.7,
+            'audio': 0.6,
+            'video': 0.5,
+            'structured': 0.7
+        })
+        self.cross_modal_threshold = self.config.get('cross_modal_threshold', 0.6)
+        self.integration_steps = self.config.get('integration_steps', 3)
+        self.alignment_method = self.config.get('alignment_method', 'attention')
+    
     async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
         try:
             # Process different modalities
@@ -86,7 +114,7 @@ class MultiModalReasoning(ReasoningStrategy):
                     for item1 in items1:
                         for item2 in items2:
                             similarity = self._calculate_similarity(item1, item2)
-                            if similarity > 0.5:  # Threshold for alignment
+                            if similarity > self.cross_modal_threshold:  # Threshold for alignment
                                 alignments.append({
                                     "type1": type1,
                                     "type2": type2,

quantum_learning.py

@@ -40,25 +40,35 @@ class QuantumLearningSystem:
     5. Uses quantum annealing for global optimization
     """
     
-    def __init__(self, 
-                 num_qubits: int = 8,
-                 entanglement_strength: float = 0.5,
-                 interference_threshold: float = 0.3,
-                 tunneling_rate: float = 0.1,
-                 annealing_schedule: Optional[Dict[str, Any]] = None):
+    def __init__(self, config: Optional[Dict[str, Any]] = None):
         """Initialize quantum learning system."""
-        self.num_qubits = num_qubits
-        self.entanglement_strength = entanglement_strength
-        self.interference_threshold = interference_threshold
-        self.tunneling_rate = tunneling_rate
-        self.annealing_schedule = annealing_schedule or {
-            "initial_temp": 10.0,
-            "final_temp": 0.1,
-            "cooling_rate": 0.95
-        }
+        self.config = config or {}
+        
+        # Quantum system parameters
+        self.num_qubits = self.config.get('num_qubits', 8)
+        self.entanglement_strength = self.config.get('entanglement_strength', 0.5)
+        self.interference_threshold = self.config.get('interference_threshold', 0.3)
+        self.tunneling_rate = self.config.get('tunneling_rate', 0.1)
+        self.annealing_schedule = self.config.get('annealing_schedule', {
+            'initial_temp': 1.0,
+            'final_temp': 0.01,
+            'steps': 100,
+            'cooling_rate': 0.95
+        })
+        
+        # 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
+        })
         
         # Initialize quantum state
-        self.state = np.zeros((2**num_qubits,), dtype=complex)
+        self.state = np.zeros((2**self.num_qubits,), dtype=complex)
         self.state[0] = 1.0  # Initialize to |0⟩ state
         
         # Pattern storage

team_management.py

@@ -14,7 +14,7 @@ Features:
 - Synchronized execution
 """
 
-from typing import Dict, List, Optional, Set, Union, TypeVar
+from typing import Dict, List, Optional, Set, Union, TypeVar, Any
 from dataclasses import dataclass, field
 from enum import Enum
 import asyncio
@@ -196,7 +196,8 @@ class TeamManager:
             agent = Agent(
                 profile=config["profile"],
                 reasoning_engine=self.orchestrator.reasoning_engine,
-                meta_learning=self.orchestrator.meta_learning
+                meta_learning=self.orchestrator.meta_learning,
+                config=config.get("config", {})
             )
             
             self.agents[team_id][agent_id] = agent
@@ -484,18 +485,21 @@ class TeamManager:
             )
 
 class Agent:
-    def __init__(self, profile: Dict, reasoning_engine: UnifiedReasoningEngine, meta_learning: bool):
+    def __init__(self, profile: Dict, reasoning_engine: UnifiedReasoningEngine, meta_learning: bool, config: Optional[Dict[str, Any]] = None):
         self.profile = profile
+        self.config = config or {}
+        
+        # Use provided reasoning engine or create one with config
         self.reasoning_engine = reasoning_engine if reasoning_engine else UnifiedReasoningEngine(
-            min_confidence=0.7,
-            parallel_threshold=3,
-            learning_rate=0.1,
-            strategy_weights={
+            min_confidence=self.config.get('min_confidence', 0.7),
+            parallel_threshold=self.config.get('parallel_threshold', 3),
+            learning_rate=self.config.get('learning_rate', 0.1),
+            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
-            }
+            })
         )
         self.meta_learning = meta_learning
         self.state = AgentState.IDLE