Deploy: Working AEGIS BIO LAB 10 CONDUCTOR 20260111_185821

app.py

@@ -1,16 +1,13 @@
 from flask import Flask, render_template, request, jsonify
 import torch
-from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
+from transformers import AutoTokenizer, AutoModelForCausalLM
 import re
 import gc
 import json
 import threading
 import time
 from datetime import datetime
-from typing import Dict, List, Optional
-from pydantic import BaseModel
 import logging
-import os
 
 # Configure logging
 logging.basicConfig(level=logging.INFO)
@@ -27,91 +24,47 @@ loading_status = "Initializing AEGIS BIO LAB 10 CONDUCTOR Multi-Domain Expert Sy
 # AEGIS BIO LAB 10 CONDUCTOR Configuration
 MODEL_REPO = "Gaston895/aegisconduct"
 AEGIS_VERSION = "10.0"
-GLOBAL_REGIONS = [
-    "North America", "Europe", "Asia", "Africa", 
-    "South America", "Middle East", "Oceania", "Arctic Region"
-]
-
-def clear_memory():
-    """Clear memory aggressively"""
-    gc.collect()
-    if torch.cuda.is_available():
-        torch.cuda.empty_cache()
 
 def load_model():
-    """Load the AEGIS Conduct model with 8-bit quantization for CPU"""
+    """Load the AEGIS Conduct model optimized for CPU"""
     global model, tokenizer, model_loaded, loading_status
     
     try:
-        # Initial memory cleanup
-        clear_memory()
-        
         loading_status = "Loading AEGIS BIO LAB 10 CONDUCTOR tokenizer..."
-        print("🔄 Loading AEGIS BIO LAB 10 CONDUCTOR tokenizer...")
+        print("Loading AEGIS BIO LAB 10 CONDUCTOR Multi-Domain Expert for CPU...")
         
-        # 1. Load tokenizer first (fast)
+        # Load tokenizer first
         tokenizer = AutoTokenizer.from_pretrained(
             MODEL_REPO,
-            trust_remote_code=True,
-            use_fast=True
+            trust_remote_code=True
         )
         
-        # Set padding token if not set
+        # Add pad token if missing
         if tokenizer.pad_token is None:
             tokenizer.pad_token = tokenizer.eos_token
         
-        loading_status = "Configuring 8-bit quantization for CPU optimization..."
-        print("⚙️ Configuring 8-bit quantization (reduces 16GB model to ~8GB)...")
-        
-        # 2. Configure 8-bit quantization for CPU
-        quantization_config = BitsAndBytesConfig(
-            load_in_8bit=True,  # Essential for 16GB model
-            llm_int8_threshold=6.0,
-            llm_int8_has_fp16_weight=False,  # Important for CPU
-            bnb_8bit_compute_dtype=torch.float32,  # Use float32 for CPU
-            bnb_8bit_use_double_quant=False,  # Disable for CPU compatibility
-        )
+        loading_status = "Loading AEGIS BIO LAB 10 CONDUCTOR model (this may take a few minutes)..."
         
-        loading_status = "Loading 16GB model with 8-bit quantization (2-3 minutes)..."
-        print("📥 Loading 16GB model with 8-bit quantization...")
-        
-        # 3. Load model with 8-bit quantization
+        # Load model optimized for CPU
         model = AutoModelForCausalLM.from_pretrained(
             MODEL_REPO,
-            quantization_config=quantization_config,
-            device_map="cpu",  # Let it handle device mapping
+            torch_dtype=torch.float16,
+            device_map="cpu",
             trust_remote_code=True,
-            low_cpu_mem_usage=True,  # Critical for large models
-            torch_dtype=torch.float32,  # CPU compatible
-            max_memory={"cpu": "12GB"},  # Limit CPU memory usage
+            low_cpu_mem_usage=True
         )
         
-        # 4. Set model to evaluation mode
-        model.eval()
-        
-        # 5. Final memory cleanup
-        clear_memory()
-        
-        # Check actual memory usage
-        if hasattr(model, 'get_memory_footprint'):
-            try:
-                memory_footprint = model.get_memory_footprint()
-                print(f"✅ Model memory footprint: {memory_footprint / 1024**3:.2f} GB")
-            except:
-                print("✅ Model loaded with 8-bit quantization")
-        
-        print(f"✅ AEGIS BIO LAB 10 CONDUCTOR loaded successfully!")
-        print(f"⚡ 8-bit quantization: 16GB → ~8GB memory usage")
-        print(f"🚀 Ready for fast CPU inference")
+        # Force garbage collection
+        gc.collect()
         
         loading_status = "AEGIS BIO LAB 10 CONDUCTOR Multi-Domain Expert loaded successfully!"
+        print("AEGIS BIO LAB 10 CONDUCTOR Multi-Domain Expert loaded successfully on CPU!")
         model_loaded = True
         return True
         
     except Exception as e:
         loading_status = f"Error loading AEGIS BIO LAB 10 CONDUCTOR model: {str(e)}"
-        print(f"❌ Error loading model: {e}")
-        print(f"💡 Tip: Ensure bitsandbytes>=0.41.0 is installed")
+        print(f"Error loading model: {e}")
         model_loaded = False
         return False
 
@@ -126,64 +79,37 @@ def format_response(text):
     
     return text
 
-def analyze_with_aegis_conductor(prompt: str, analysis_type: str = "general") -> str:
-    """Analyze using AEGIS BIO LAB 10 CONDUCTOR - Multi-Domain Expert System"""
+def generate_response(message, history=None, temperature=0.7, max_tokens=128):
+    """Generate response from the model optimized for CPU"""
     global model, tokenizer, model_loaded
     
     if not model_loaded or model is None or tokenizer is None:
         return "AEGIS BIO LAB 10 CONDUCTOR is still loading... Please wait a moment and try again."
     
-    # Enhanced prompts for AEGIS BIO LAB 10 CONDUCTOR multi-domain analysis
-    system_prompts = {
-        "general": f"You are the AEGIS BIO LAB {AEGIS_VERSION} CONDUCTOR - an advanced multi-domain analysis system. You can provide expert analysis on ANY topic including economics, technology, science, politics, health, environment, security, and more. Provide comprehensive, well-reasoned responses with global perspective across all 8 regions: {', '.join(GLOBAL_REGIONS)}.",
-        "economic": f"You are the AEGIS BIO LAB {AEGIS_VERSION} CONDUCTOR Economics Expert. Provide comprehensive economic analysis covering market dynamics, financial implications, GDP impacts, inflation effects, trade relationships, and policy recommendations across all 8 global regions: {', '.join(GLOBAL_REGIONS)}.",
-        "technology": f"You are the AEGIS BIO LAB {AEGIS_VERSION} CONDUCTOR Technology Expert. Analyze technological developments, AI impacts, cybersecurity, innovation trends, and digital transformation across global regions.",
-        "security": f"You are the AEGIS BIO LAB {AEGIS_VERSION} CONDUCTOR Security Expert. Focus on threat analysis, risk assessment, geopolitical stability, and security implications across {len(GLOBAL_REGIONS)} global regions.",
-        "health": f"You are the AEGIS BIO LAB {AEGIS_VERSION} CONDUCTOR Health & Bio Expert. Analyze health systems, pandemic preparedness, biotechnology, medical innovations, and public health policies globally.",
-        "environment": f"You are the AEGIS BIO LAB {AEGIS_VERSION} CONDUCTOR Environmental Expert. Focus on climate change, sustainability, environmental policy, and ecological impacts across all global regions.",
-        "strategic": f"You are the AEGIS BIO LAB {AEGIS_VERSION} CONDUCTOR Strategic Planning Expert. Provide long-term strategic analysis, policy frameworks, and comprehensive planning across multiple domains and regions.",
-        "threat": f"You are the AEGIS BIO LAB {AEGIS_VERSION} CONDUCTOR Threat Analysis Expert. Assess multi-domain threats including economic, technological, environmental, security, and health risks across {len(GLOBAL_REGIONS)} global regions.",
-        "aegis_conductor": f"You are the AEGIS BIO LAB {AEGIS_VERSION} CONDUCTOR - the ultimate multi-domain analysis system. Provide comprehensive cross-domain analysis covering all aspects: economic, technological, security, health, environmental, and strategic implications across all 8 global regions: {', '.join(GLOBAL_REGIONS)}."
-    }
-    
-    system_prompt = system_prompts.get(analysis_type, system_prompts["general"])
-    
-    # Use Llama chat format (AEGIS model is based on Llama)
-    enhanced_prompt = f"""<s>[INST] <<SYS>>
-{system_prompt}
-
-AEGIS BIO LAB {AEGIS_VERSION} CONDUCTOR MULTI-DOMAIN CAPABILITIES:
-- Cross-Continental Analysis ({len(GLOBAL_REGIONS)} regions)
-- Multi-Domain Expertise (Economics, Technology, Security, Health, Environment, Strategy)
-- Threat Assessment & Risk Analysis
-- Policy Recommendations & Strategic Planning
-- Real-time Analysis & Insights
-- Global Perspective & Regional Adaptation
-<</SYS>>
-
-{prompt}
-
-As the AEGIS BIO LAB {AEGIS_VERSION} CONDUCTOR, provide a comprehensive analysis that includes:
-
-1. **Core Analysis** - Direct response to the query with expert insights
-2. **Multi-Domain Perspective** - Consider interconnections across different fields
-3. **Global Context** - Assess implications across relevant regions
-4. **Strategic Insights** - Long-term implications and recommendations
-5. **Risk Assessment** - Identify potential challenges and opportunities
-6. **Actionable Guidance** - Practical recommendations and next steps
-
-Provide thorough, well-reasoned analysis that demonstrates deep expertise while remaining accessible and actionable. [/INST]"""
-    
     try:
-        # Tokenize input with optimized length for 8-bit model
-        inputs = tokenizer(enhanced_prompt, return_tensors="pt", truncation=True, max_length=2048)
+        # Build conversation context
+        conversation = ""
+        if history:
+            # Only use last 2 exchanges to save memory
+            recent_history = history[-2:] if len(history) > 2 else history
+            for exchange in recent_history:
+                conversation += f"User: {exchange['user']}\nAssistant: {exchange['assistant']}\n\n"
+        
+        # Add current message with AEGIS prompt
+        aegis_prompt = f"""You are the AEGIS BIO LAB {AEGIS_VERSION} CONDUCTOR - an advanced multi-domain analysis system. You can provide expert analysis on ANY topic including economics, technology, science, politics, health, environment, security, and more. Provide comprehensive, well-reasoned responses.
+
+User: {message}
+Assistant:"""
+        
+        # Tokenize input with strict length limit for CPU
+        inputs = tokenizer(aegis_prompt, return_tensors="pt", truncation=True, max_length=512)
         
-        # Generate response with 8-bit quantized model (faster inference)
+        # Generate response with CPU-optimized settings
         with torch.no_grad():
             outputs = model.generate(
                 **inputs,
-                max_new_tokens=512,  # Good balance for 8-bit model
-                temperature=0.7,
+                max_new_tokens=max_tokens,
+                temperature=temperature,
                 do_sample=True,
                 top_p=0.9,
                 top_k=50,
@@ -191,121 +117,25 @@ Provide thorough, well-reasoned analysis that demonstrates deep expertise while
                 pad_token_id=tokenizer.eos_token_id,
                 eos_token_id=tokenizer.eos_token_id,
                 use_cache=True,
-                num_beams=1  # Faster for CPU
+                num_beams=1
             )
         
         # Decode response
         response = tokenizer.decode(outputs[0], skip_special_tokens=True)
         
         # Extract only the new response
-        response = response[len(enhanced_prompt):].strip()
+        response = response[len(aegis_prompt):].strip()
         
         # Format and clean response
         response = format_response(response)
         
         # Clean up memory after generation
-        clear_memory()
+        gc.collect()
         
-        return response if response else "I apologize, but I couldn't generate a proper AEGIS BIO LAB 10 CONDUCTOR analysis. Please try rephrasing your question."
+        return response if response else "I apologize, but I couldn't generate a proper response. Please try rephrasing your question."
         
     except Exception as e:
-        return f"AEGIS BIO LAB 10 CONDUCTOR analysis error: {str(e)}. Please try a shorter question."
-
-def conduct_aegis_threat_analysis(tech_scores: Dict[str, float], year: str = None) -> Dict:
-    """Conduct comprehensive AEGIS BIO LAB 10 CONDUCTOR threat analysis"""
-    if year is None:
-        year = str(datetime.now().year)
-    
-    # Filter critical economic threats (scores > 6.0 in our 0-10 scale)
-    critical_threats = {k: v for k, v in tech_scores.items() if v > 6.0}
-    
-    # Enhanced AEGIS BIO LAB 10 CONDUCTOR threat analysis prompt
-    analysis_prompt = f"""AEGIS BIO LAB {AEGIS_VERSION} CONDUCTOR - COMPREHENSIVE THREAT ANALYSIS - Year {year}
-
-TECHNOLOGY THREAT ASSESSMENT:
-Critical Threats: {len(critical_threats)} detected from {len(tech_scores)} total threat categories
-High-Impact Threat Categories: {list(critical_threats.keys())}
-Technology Threat Scores: {dict(list(tech_scores.items()))}
-
-REQUIRED CALCULATIONS AND ANALYSIS:
-
-CALCULATE METRICS:
-1. Market Shock Index (0-1 scale): Calculate based on threat interaction effects
-2. Impact Classification: Determine impact level (Limited/Moderate/Major/Crisis)
-3. Threat Level: Assess overall threat (Low/Medium/High/Extreme Risk)
-
-REGIONAL VULNERABILITIES (0-10 scale for each region):
-4. North America: Technology and financial sector resilience
-5. Europe: Manufacturing and energy security
-6. Asia: Trade diversification and supply chain adaptation
-7. Africa: Agricultural and resource sector protection
-8. South America: Climate adaptation and economic diversification
-9. Middle East: Energy transition and modernization
-10. Oceania: Resource security and climate resilience
-11. Arctic Region: Sustainable development
-
-CONTAGION METRICS (0-1 scale):
-12. Cascade Probability: Risk of cascading failures
-13. Propagation Speed: Rate of impact spread
-14. Containment Difficulty: Challenge of limiting damage
-15. Systemic Risk: Overall system stability threat
-
-Provide comprehensive analysis with specific numerical values for all calculated metrics."""
-    
-    # Get comprehensive analysis from the AEGIS model
-    full_analysis = analyze_with_aegis_conductor(analysis_prompt, "aegis_conductor")
-    
-    # Parse metrics from the response
-    result = {
-        "reasoning_analysis": full_analysis,
-        "market_shock_index": 0.0,
-        "impact_classification": "Analysis in Progress",
-        "threat_level": "Assessment Pending",
-        "regional_vulnerabilities": {},
-        "contagion_metrics": {},
-        "tech_scores": tech_scores,
-        "year": year,
-        "analysis_timestamp": datetime.now().isoformat()
-    }
-    
-    # Extract metrics from model response
-    lines = full_analysis.split('\n')
-    for line in lines:
-        line = line.strip()
-        if 'Market Shock Index:' in line or 'market shock index' in line.lower():
-            try:
-                import re
-                numbers = re.findall(r'(\d+\.?\d*)', line)
-                if numbers:
-                    value = float(numbers[0])
-                    if value <= 1.0:
-                        result["market_shock_index"] = value
-            except:
-                pass
-        elif 'Impact Classification:' in line or 'impact classification' in line.lower():
-            parts = line.split(':')
-            if len(parts) > 1:
-                result["impact_classification"] = parts[1].strip()
-        elif 'Threat Level:' in line or 'threat level' in line.lower():
-            parts = line.split(':')
-            if len(parts) > 1:
-                result["threat_level"] = parts[1].strip()
-    
-    # Extract regional vulnerabilities
-    for region in GLOBAL_REGIONS:
-        for line in lines:
-            if region.lower() in line.lower() and ':' in line:
-                try:
-                    import re
-                    numbers = re.findall(r'(\d+\.?\d*)', line)
-                    if numbers:
-                        score = float(numbers[0])
-                        if score <= 10.0:
-                            result["regional_vulnerabilities"][region] = score
-                except:
-                    pass
-    
-    return result
+        return f"Error generating response: {str(e)}. Please try a shorter question."
 
 @app.route('/')
 def index():
@@ -314,66 +144,40 @@ def index():
 
 @app.route('/status')
 def status():
-    """Get AEGIS model loading status"""
+    """Get model loading status"""
     return jsonify({
         'loaded': model_loaded,
         'status': loading_status,
         'model': MODEL_REPO,
-        'version': AEGIS_VERSION,
-        'regions': len(GLOBAL_REGIONS),
-        'quantization': '8-bit' if model_loaded else 'Loading'
+        'version': AEGIS_VERSION
     })
 
 @app.route('/chat', methods=['POST'])
 def chat():
-    """Handle AEGIS multi-domain chat messages"""
+    """Handle chat messages"""
     data = request.json
     message = data.get('message', '').strip()
     history = data.get('history', [])
     temperature = float(data.get('temperature', 0.7))
-    max_tokens = int(data.get('max_tokens', 256))
-    analysis_type = data.get('analysis_type', 'general')
+    max_tokens = int(data.get('max_tokens', 128))
     
     if not message:
         return jsonify({'error': 'No message provided'}), 400
     
-    # Generate response using AEGIS Multi-Domain System
-    response = analyze_with_aegis_conductor(message, analysis_type)
+    # Generate response
+    response = generate_response(message, history, temperature, max_tokens)
     
     return jsonify({
         'response': response,
         'timestamp': time.time(),
-        'model': f"AEGIS BIO LAB {AEGIS_VERSION} CONDUCTOR (8-bit)",
-        'analysis_type': analysis_type
+        'model': f"AEGIS BIO LAB {AEGIS_VERSION} CONDUCTOR"
     })
 
-@app.route('/aegis_analysis', methods=['POST'])
-def aegis_analysis():
-    """Handle comprehensive AEGIS BIO LAB 10 CONDUCTOR threat analysis"""
-    data = request.json
-    
-    # Get technology threat scores
-    tech_scores = {
-        'AI': float(data.get('ai_score', 7.0)),
-        'Cyber': float(data.get('cyber_score', 6.5)),
-        'Bio': float(data.get('bio_score', 8.0)),
-        'Nuclear': float(data.get('nuclear_score', 4.0)),
-        'Climate': float(data.get('climate_score', 7.5)),
-        'Space': float(data.get('space_score', 5.0))
-    }
-    
-    year = data.get('year', str(datetime.now().year))
-    
-    # Conduct comprehensive AEGIS analysis
-    analysis_result = conduct_aegis_threat_analysis(tech_scores, year)
-    
-    return jsonify(analysis_result)
-
 @app.route('/clear', methods=['POST'])
 def clear_chat():
     """Clear chat history and free memory"""
-    clear_memory()
-    return jsonify({'status': 'AEGIS BIO LAB 10 CONDUCTOR memory cleared'})
+    gc.collect()
+    return jsonify({'status': 'cleared'})
 
 # Start model loading in background thread
 def start_model_loading():
@@ -385,4 +189,4 @@ if __name__ == '__main__':
     loading_thread.daemon = True
     loading_thread.start()
     
-    app.run(host='0.0.0.0', port=7860, debug=False)
+    app.run(host='0.0.0.0', port=7860, debug=False)