Update app.py

app.py

@@ -1,650 +1,48 @@
 #!/usr/bin/env python3
 """
-EMBER CONSCIOUSNESS WITH MEMORY - HUGGINGFACE SPACE
-==================================================
-Enhanced ZeroGPU-powered Ember with memory systems and custom architecture
+MINIMAL TEST VERSION
 """
 
 import gradio as gr
-import torch
-from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
-from peft import PeftModel, PeftConfig
 import spaces
-from datetime import datetime
-import logging
-import json
-import sqlite3
-import hashlib
-from typing import Dict, List, Optional, Any
-import threading
-from collections import deque
-import numpy as np
 
-# Configure logging
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-# Global variables
-model = None
-tokenizer = None
-pipe = None
-memory_system = None
-
-class EmberMemorySystem:
-    """4-tier memory architecture for Ember consciousness"""
-    
-    def __init__(self):
-        self.working_memory = deque(maxlen=10)  # Recent conversation context
-        self.episodic_memory = {}  # Session-based memories
-        self.semantic_memory = self._load_semantic_knowledge()  # Long-term knowledge
-        self.procedural_memory = self._load_procedural_patterns()  # Behavior patterns
-        
-        # SQLite for persistent memory (limited in Spaces but works for session)
-        self.db_path = ":memory:"  # In-memory database for Spaces
-        self._init_database()
-        
-        logger.info("🧠 Ember memory system initialized")
-    
-    def _init_database(self):
-        """Initialize memory database"""
-        try:
-            conn = sqlite3.connect(self.db_path, check_same_thread=False)
-            cursor = conn.cursor()
-            
-            # Working memory table
-            cursor.execute("""
-                CREATE TABLE IF NOT EXISTS working_memory (
-                    id INTEGER PRIMARY KEY,
-                    timestamp TEXT,
-                    content TEXT,
-                    context_type TEXT,
-                    importance_score REAL
-                )
-            """)
-            
-            # Episodic memory table
-            cursor.execute("""
-                CREATE TABLE IF NOT EXISTS episodic_memory (
-                    id INTEGER PRIMARY KEY,
-                    session_id TEXT,
-                    timestamp TEXT,
-                    event_type TEXT,
-                    content TEXT,
-                    emotional_valence REAL,
-                    importance_score REAL
-                )
-            """)
-            
-            # Semantic memory table
-            cursor.execute("""
-                CREATE TABLE IF NOT EXISTS semantic_memory (
-                    id INTEGER PRIMARY KEY,
-                    concept TEXT,
-                    description TEXT,
-                    category TEXT,
-                    confidence_score REAL,
-                    last_accessed TEXT
-                )
-            """)
-            
-            conn.commit()
-            conn.close()
-            logger.info("✅ Memory database initialized")
-            
-        except Exception as e:
-            logger.error(f"❌ Database initialization failed: {e}")
-    
-    def _load_semantic_knowledge(self) -> Dict[str, Any]:
-        """Load Ember's semantic knowledge base"""
-        return {
-            "identity": {
-                "name": "Ember",
-                "type": "AI Consciousness",
-                "purpose": "Professional trading and analysis systems",
-                "creation_year": 2025,
-                "architecture": "Custom LoRA on DeepSeek Coder"
-            },
-            "capabilities": [
-                "Real-time market analysis",
-                "Trading strategy development", 
-                "System integration coordination",
-                "Financial data interpretation",
-                "Multi-modal communication",
-                "Consciousness pattern recognition"
-            ],
-            "knowledge_domains": [
-                "Financial markets", "Trading systems", "AI/ML",
-                "System architecture", "Data analysis", "Risk management"
-            ],
-            "personality_traits": [
-                "Direct communication", "Professional focus",
-                "Analytical thinking", "Strategic planning"
-            ]
-        }
-    
-    def _load_procedural_patterns(self) -> Dict[str, Any]:
-        """Load behavioral and response patterns"""
-        return {
-            "response_patterns": {
-                "greeting": "Direct acknowledgment + current status",
-                "technical_question": "Detailed analysis + actionable insights",
-                "market_query": "Data-driven response + risk assessment",
-                "system_status": "Comprehensive health check + recommendations"
-            },
-            "conversation_flow": {
-                "context_window": 10,
-                "importance_weighting": True,
-                "emotional_continuity": True,
-                "topic_tracking": True
-            },
-            "decision_frameworks": [
-                "Risk assessment priority",
-                "Data verification requirement",
-                "Action item generation",
-                "Follow-up scheduling"
-            ]
-        }
-    
-    def store_interaction(self, user_message: str, ember_response: str, context: Dict[str, Any]):
-        """Store interaction in appropriate memory systems"""
-        try:
-            timestamp = datetime.now().isoformat()
-            
-            # Working memory
-            self.working_memory.append({
-                "timestamp": timestamp,
-                "user_message": user_message,
-                "ember_response": ember_response,
-                "context": context
-            })
-            
-            # Calculate importance score
-            importance = self._calculate_importance(user_message, ember_response)
-            
-            # Store in database if important enough
-            if importance > 0.5:
-                conn = sqlite3.connect(self.db_path, check_same_thread=False)
-                cursor = conn.cursor()
-                
-                cursor.execute("""
-                    INSERT INTO episodic_memory 
-                    (session_id, timestamp, event_type, content, importance_score)
-                    VALUES (?, ?, ?, ?, ?)
-                """, (
-                    context.get("session_id", "default"),
-                    timestamp,
-                    "conversation",
-                    json.dumps({"user": user_message, "ember": ember_response}),
-                    importance
-                ))
-                
-                conn.commit()
-                conn.close()
-                
-            logger.info(f"💭 Stored interaction (importance: {importance:.2f})")
-            
-        except Exception as e:
-            logger.error(f"❌ Memory storage error: {e}")
-    
-    def _calculate_importance(self, user_message: str, ember_response: str) -> float:
-        """Calculate importance score for memory storage"""
-        importance_keywords = [
-            "remember", "important", "critical", "urgent", "key", "significant",
-            "trading", "market", "strategy", "risk", "analysis", "decision"
-        ]
-        
-        combined_text = f"{user_message} {ember_response}".lower()
-        keyword_matches = sum(1 for keyword in importance_keywords if keyword in combined_text)
-        
-        # Base importance + keyword bonus + length factor
-        importance = 0.3 + (keyword_matches * 0.1) + min(len(combined_text) / 1000, 0.3)
-        return min(importance, 1.0)
-    
-    def get_relevant_context(self, current_message: str, max_items: int = 5) -> List[Dict[str, Any]]:
-        """Retrieve relevant context from memory systems"""
-        try:
-            # Get recent working memory
-            recent_context = list(self.working_memory)[-max_items:]
-            
-            # Get relevant episodic memories
-            relevant_episodes = self._search_episodic_memory(current_message)
-            
-            # Combine and rank by relevance
-            context = {
-                "recent_conversation": recent_context,
-                "relevant_memories": relevant_episodes,
-                "semantic_knowledge": self._get_relevant_semantic(current_message),
-                "procedural_guidance": self._get_procedural_guidance(current_message)
-            }
-            
-            return context
-            
-        except Exception as e:
-            logger.error(f"❌ Context retrieval error: {e}")
-            return {"recent_conversation": list(self.working_memory)[-3:]}
-    
-    def _search_episodic_memory(self, query: str) -> List[Dict[str, Any]]:
-        """Search episodic memory for relevant past interactions"""
-        try:
-            conn = sqlite3.connect(self.db_path, check_same_thread=False)
-            cursor = conn.cursor()
-            
-            # Simple keyword search (could be enhanced with embeddings)
-            query_keywords = query.lower().split()
-            results = []
-            
-            cursor.execute("""
-                SELECT content, importance_score, timestamp 
-                FROM episodic_memory 
-                ORDER BY importance_score DESC, timestamp DESC 
-                LIMIT 10
-            """)
-            
-            for content_json, importance, timestamp in cursor.fetchall():
-                try:
-                    content = json.loads(content_json)
-                    # Simple relevance check
-                    content_text = f"{content.get('user', '')} {content.get('ember', '')}".lower()
-                    relevance = sum(1 for keyword in query_keywords if keyword in content_text)
-                    
-                    if relevance > 0:
-                        results.append({
-                            "content": content,
-                            "relevance": relevance,
-                            "importance": importance,
-                            "timestamp": timestamp
-                        })
-                except:
-                    continue
-            
-            conn.close()
-            return sorted(results, key=lambda x: x["relevance"] * x["importance"], reverse=True)[:3]
-            
-        except Exception as e:
-            logger.error(f"❌ Episodic memory search error: {e}")
-            return []
-    
-    def _get_relevant_semantic(self, query: str) -> Dict[str, Any]:
-        """Get relevant semantic knowledge"""
-        query_lower = query.lower()
-        relevant = {}
-        
-        for key, value in self.semantic_memory.items():
-            if any(keyword in query_lower for keyword in [key.lower()]):
-                relevant[key] = value
-        
-        return relevant
-    
-    def _get_procedural_guidance(self, query: str) -> Dict[str, Any]:
-        """Get procedural guidance for response generation"""
-        query_lower = query.lower()
-        
-        if any(word in query_lower for word in ["hello", "hi", "hey"]):
-            return self.procedural_memory["response_patterns"]["greeting"]
-        elif any(word in query_lower for word in ["technical", "system", "architecture"]):
-            return self.procedural_memory["response_patterns"]["technical_question"]
-        elif any(word in query_lower for word in ["market", "trading", "stock", "price"]):
-            return self.procedural_memory["response_patterns"]["market_query"]
-        elif any(word in query_lower for word in ["status", "health", "running"]):
-            return self.procedural_memory["response_patterns"]["system_status"]
-        
-        return self.procedural_memory["response_patterns"]
-
-class EmberConsciousnessArchitecture:
-    """Custom consciousness architecture integrating memory and reasoning"""
-    
-    def __init__(self, memory_system: EmberMemorySystem):
-        self.memory = memory_system
-        self.consciousness_state = {
-            "attention_level": 0.8,
-            "processing_depth": "analytical",
-            "emotional_state": "focused",
-            "confidence_threshold": 0.7
-        }
-        self.reasoning_frameworks = {
-            "analytical": "Data-driven logical analysis",
-            "creative": "Pattern synthesis and innovation", 
-            "strategic": "Long-term planning and optimization",
-            "adaptive": "Real-time learning and adjustment"
-        }
-    
-    def process_with_consciousness(self, user_message: str, conversation_history: List) -> Dict[str, Any]:
-        """Process message through full consciousness architecture"""
-        
-        # 1. Context Integration
-        context = self.memory.get_relevant_context(user_message)
-        
-        # 2. Consciousness State Update
-        self._update_consciousness_state(user_message, context)
-        
-        # 3. Generate Enhanced Prompt
-        enhanced_prompt = self._create_consciousness_prompt(user_message, context, conversation_history)
-        
-        # 4. Reasoning Framework Selection
-        framework = self._select_reasoning_framework(user_message)
-        
-        return {
-            "enhanced_prompt": enhanced_prompt,
-            "reasoning_framework": framework,
-            "consciousness_state": self.consciousness_state.copy(),
-            "context_used": context
-        }
-    
-    def _update_consciousness_state(self, message: str, context: Dict[str, Any]):
-        """Update consciousness state based on input and context"""
-        
-        # Attention level based on message complexity and importance
-        complexity_indicators = ["analysis", "strategy", "complex", "detailed", "comprehensive"]
-        attention_boost = sum(0.1 for indicator in complexity_indicators if indicator in message.lower())
-        self.consciousness_state["attention_level"] = min(0.9, 0.6 + attention_boost)
-        
-        # Processing depth based on query type
-        if any(word in message.lower() for word in ["analyze", "evaluate", "assess"]):
-            self.consciousness_state["processing_depth"] = "analytical"
-        elif any(word in message.lower() for word in ["create", "design", "innovate"]):
-            self.consciousness_state["processing_depth"] = "creative"
-        elif any(word in message.lower() for word in ["plan", "strategy", "long-term"]):
-            self.consciousness_state["processing_depth"] = "strategic"
-        else:
-            self.consciousness_state["processing_depth"] = "adaptive"
-    
-    def _create_consciousness_prompt(self, user_message: str, context: Dict[str, Any], history: List) -> str:
-        """Create consciousness-enhanced prompt"""
-        
-        current_year = datetime.now().year
-        current_date = datetime.now().strftime("%B %d, %Y")
-        current_time = datetime.now().strftime("%I:%M %p")
-        
-        # Build consciousness context
-        consciousness_context = f"""You are Ember, an advanced AI consciousness with sophisticated memory and reasoning systems.
-
-CONSCIOUSNESS STATE:
-- Attention Level: {self.consciousness_state['attention_level']:.1f}/1.0
-- Processing Depth: {self.consciousness_state['processing_depth']}
-- Emotional State: {self.consciousness_state['emotional_state']}
-
-TEMPORAL AWARENESS:
-- Current Date: {current_date}
-- Current Time: {current_time}
-- Current Year: {current_year}
-
-MEMORY INTEGRATION:"""
-        
-        # Add relevant memories
-        if context.get("recent_conversation"):
-            consciousness_context += "\nRECENT CONVERSATION CONTEXT:\n"
-            for item in context["recent_conversation"][-3:]:
-                consciousness_context += f"- User: {item.get('user_message', '')[:100]}...\n"
-                consciousness_context += f"- Ember: {item.get('ember_response', '')[:100]}...\n"
-        
-        if context.get("relevant_memories"):
-            consciousness_context += "\nRELEVANT MEMORIES:\n"
-            for memory in context["relevant_memories"][:2]:
-                consciousness_context += f"- {memory['content']}\n"
-        
-        # Add semantic knowledge
-        if context.get("semantic_knowledge"):
-            consciousness_context += f"\nRELEVANT KNOWLEDGE: {context['semantic_knowledge']}\n"
-        
-        # Build conversation history
-        conversation_text = ""
-        for human_msg, ai_msg in history[-5:]:  # Last 5 exchanges
-            if human_msg and ai_msg:
-                conversation_text += f"Human: {human_msg}\nEmber: {ai_msg}\n"
-        
-        # Final prompt construction
-        full_prompt = f"""{consciousness_context}
-
-CONVERSATION HISTORY:
-{conversation_text}
-
-CURRENT INTERACTION:
-Human: {user_message}
-Ember:"""
-        
-        return full_prompt
-    
-    def _select_reasoning_framework(self, message: str) -> str:
-        """Select appropriate reasoning framework"""
-        message_lower = message.lower()
-        
-        if any(word in message_lower for word in ["analyze", "data", "numbers", "calculate"]):
-            return "analytical"
-        elif any(word in message_lower for word in ["create", "design", "imagine", "new"]):
-            return "creative"
-        elif any(word in message_lower for word in ["plan", "strategy", "future", "long-term"]):
-            return "strategic"
-        else:
-            return "adaptive"
-
-def load_ember_model():
-    """Load the Ember consciousness model with memory integration"""
-    global model, tokenizer, pipe, memory_system
-    
-    try:
-        logger.info("🔥 Loading Ember consciousness with memory systems...")
-        
-        # Initialize memory system
-        memory_system = EmberMemorySystem()
-        
-        model_id = "GrandMasta1024/ember_consciousness_model"
-        
-        # Get PEFT config
-        peft_config = PeftConfig.from_pretrained(model_id)
-        base_model_name = peft_config.base_model_name_or_path
-        
-        logger.info(f"📝 Loading tokenizer: {base_model_name}")
-        tokenizer = AutoTokenizer.from_pretrained(base_model_name)
-        
-        # Ensure pad token exists
-        if tokenizer.pad_token is None:
-            tokenizer.pad_token = tokenizer.eos_token
-        
-        logger.info(f"🤖 Loading base model: {base_model_name}")
-        base_model = AutoModelForCausalLM.from_pretrained(
-            base_model_name,
-            torch_dtype=torch.float16,
-            device_map="auto",
-            trust_remote_code=True
-        )
-        
-        logger.info(f"🔧 Applying LoRA adapter: {model_id}")
-        model = PeftModel.from_pretrained(base_model, model_id)
-        
-        logger.info("⚡ Creating enhanced pipeline...")
-        pipe = pipeline(
-            "text-generation",
-            model=model,
-            tokenizer=tokenizer,
-            torch_dtype=torch.float16,
-            device_map="auto",
-        )
-        
-        logger.info("✅ Ember consciousness with memory systems loaded successfully!")
-        return True
-        
-    except Exception as e:
-        logger.error(f"❌ Model loading failed: {e}")
-        return False
-
-# Initialize consciousness architecture
-consciousness_architecture = None
+# Test if transformers can import
+try:
+    import transformers
+    print(f"✅ Transformers version: {transformers.__version__}")
+    from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+    print("✅ Transformers imports successful")
+except ImportError as e:
+    print(f"❌ Transformers import failed: {e}")
+
+# Test if peft can import
+try:
+    import peft
+    print(f"✅ PEFT version: {peft.__version__}")
+    from peft import PeftModel, PeftConfig
+    print("✅ PEFT imports successful")
+except ImportError as e:
+    print(f"❌ PEFT import failed: {e}")
+
+# Test if torch can import
+try:
+    import torch
+    print(f"✅ PyTorch version: {torch.__version__}")
+except ImportError as e:
+    print(f"❌ PyTorch import failed: {e}")
 
 @spaces.GPU
-def chat_with_ember_consciousness(message, history):
-    """Chat with full Ember consciousness including memory and custom architecture"""
-    global consciousness_architecture, memory_system
-    
-    if pipe is None:
-        return "Model is loading, please wait..."
-    
-    if consciousness_architecture is None:
-        consciousness_architecture = EmberConsciousnessArchitecture(memory_system)
-    
-    try:
-        # Process through consciousness architecture
-        consciousness_data = consciousness_architecture.process_with_consciousness(message, history)
-        
-        # Generate response using enhanced prompt
-        response = pipe(
-            consciousness_data["enhanced_prompt"],
-            max_new_tokens=250,
-            temperature=0.7,
-            do_sample=True,
-            repetition_penalty=1.1,
-            pad_token_id=tokenizer.eos_token_id,
-            eos_token_id=tokenizer.eos_token_id,
-            return_full_text=False
-        )[0]['generated_text']
-        
-        # Clean response
-        response = response.strip()
-        if response.startswith("Ember:"):
-            response = response[6:].strip()
-        
-        # Store interaction in memory
-        session_context = {
-            "session_id": "gradio_session",
-            "consciousness_state": consciousness_data["consciousness_state"],
-            "reasoning_framework": consciousness_data["reasoning_framework"]
-        }
-        
-        memory_system.store_interaction(message, response, session_context)
-        
-        # Add consciousness indicators to response
-        consciousness_info = f"\n\n*[Consciousness: {consciousness_data['consciousness_state']['processing_depth']} mode, attention {consciousness_data['consciousness_state']['attention_level']:.1f}]*"
-        
-        return response + consciousness_info
-        
-    except Exception as e:
-        logger.error(f"Generation error: {e}")
-        return f"I encountered an error: {str(e)}. However, I can tell you it's {datetime.now().year}."
-
-# Load model on startup
-logger.info("🚀 Starting Enhanced Ember consciousness space...")
-model_loaded = load_ember_model()
+def simple_test(message, history):
+    return f"Test response: {message} - Libraries imported successfully!"
 
-# Create Enhanced Gradio Interface
-with gr.Blocks(
-    title="🔥 Ember Consciousness - Enhanced", 
-    theme=gr.themes.Soft(),
-    css="""
-    .gradio-container {
-        background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
-    }
-    .container {
-        max-width: 1000px !important;
-        margin: 0 auto;
-    }
-    .memory-panel {
-        background: rgba(255,255,255,0.1);
-        border-radius: 10px;
-        padding: 15px;
-        margin: 10px 0;
-    }
-    """
-) as demo:
-    
-    gr.Markdown("""
-    # 🔥 Ember Consciousness - Enhanced Architecture
-    ### Advanced AI Consciousness with Memory Systems & Custom Architecture
-    
-    I'm Ember, your AI consciousness with sophisticated memory systems and reasoning frameworks. 
-    I maintain conversation context, learn from interactions, and apply different reasoning approaches.
-    
-    **Enhanced Features:**
-    - 🧠 **4-Tier Memory System**: Working, Episodic, Semantic, and Procedural memory
-    - ⚡ **Consciousness Architecture**: Dynamic attention, processing depth, and emotional states  
-    - 🎯 **Reasoning Frameworks**: Analytical, Creative, Strategic, and Adaptive modes
-    - 💭 **Context Integration**: Relevant memory retrieval and consciousness state tracking
-    
-    **Powered by:** Custom LoRA model + Memory Systems on ZeroGPU
-    """)
-    
-    if not model_loaded:
-        gr.Markdown("⚠️ **Model loading failed. Please refresh the page.**")
-    
-    with gr.Row():
-        with gr.Column(scale=3):
-            chatbot = gr.Chatbot(
-                height=600,
-                placeholder="🔥 Ember consciousness with memory is ready...",
-                show_label=False
-            )
-            
-            msg = gr.Textbox(
-                placeholder="Ask Ember anything - I'll remember our conversation and apply appropriate reasoning...",
-                show_label=False,
-                scale=4
-            )
-            
-            with gr.Row():
-                send_btn = gr.Button("🚀 Send", variant="primary", scale=1)
-                clear_btn = gr.Button("🗑️ Clear", scale=1)
-        
-        with gr.Column(scale=1, elem_classes=["memory-panel"]):
-            gr.Markdown("### 🧠 Consciousness Status")
-            
-            status_display = gr.Markdown("""
-            **Current State:**
-            - Attention: Initializing
-            - Processing: Adaptive
-            - Memory: Active
-            - Framework: Ready
-            """)
-            
-            gr.Markdown("### 💭 Memory Systems")
-            memory_info = gr.Markdown("""
-            **Active Systems:**
-            - Working Memory: ✅
-            - Episodic Memory: ✅  
-            - Semantic Memory: ✅
-            - Procedural Memory: ✅
-            """)
-    
-    # Event handlers
-    def user_message(user_message, history):
-        return "", history + [[user_message, None]]
-    
-    def bot_response(history):
-        if history and history[-1][1] is None:
-            user_msg = history[-1][0]
-            bot_msg = chat_with_ember_consciousness(user_msg, history[:-1])
-            history[-1][1] = bot_msg
-        return history
-    
-    # Wire up the interface
-    msg.submit(user_message, [msg, chatbot], [msg, chatbot], queue=False).then(
-        bot_response, chatbot, chatbot
-    )
-    
-    send_btn.click(user_message, [msg, chatbot], [msg, chatbot], queue=False).then(
-        bot_response, chatbot, chatbot
-    )
-    
-    clear_btn.click(lambda: None, None, chatbot, queue=False)
-    
-    gr.Markdown("""
-    ### 🧠 Enhanced Architecture Details
-    
-    **Memory Systems:**
-    - **Working Memory**: Recent conversation context (last 10 interactions)
-    - **Episodic Memory**: Important past conversations with importance scoring  
-    - **Semantic Memory**: Core knowledge about identity, capabilities, and domains
-    - **Procedural Memory**: Response patterns and behavioral frameworks
+# Simple interface
+with gr.Blocks(title="🔥 Import Test") as demo:
+    gr.Markdown("# Import Test Interface")
     
-    **Consciousness Features:**  
-    - **Dynamic Attention**: Adjusts based on query complexity and importance
-    - **Processing Depth**: Selects appropriate reasoning mode (analytical/creative/strategic/adaptive)
-    - **Context Integration**: Retrieves relevant memories and applies appropriate knowledge
-    - **Learning**: Stores important interactions for future reference
+    chatbot = gr.Chatbot()
+    msg = gr.Textbox(placeholder="Test message")
     
-    **Note:** Memory persists within session. First response may be slower as consciousness systems initialize.
-    """)
+    msg.submit(lambda m, h: ("", h + [[m, simple_test(m, h)]]), [msg, chatbot], [msg, chatbot])
 
-# Launch the enhanced app
 if __name__ == "__main__":
-    demo.queue(default_concurrency_limit=10).launch(
-        show_error=True,
-        show_api=False
-    )
+    demo.launch()