Update app.py

app.py

@@ -1,384 +1,624 @@
 import gradio as gr
 import requests
 import json
-from datetime import datetime
+from datetime import datetime, timedelta
 import re
 import xml.etree.ElementTree as ET
 import random
+import hashlib
+import math
+from collections import defaultdict
 
-class GeopoliticalAI:
+class UniversalAI:
     def __init__(self):
-        # Fonti dati real-time
+        # Simulated massive knowledge base (trilioni di token)
+        self.knowledge_domains = self.initialize_knowledge_domains()
+        self.conversation_memory = []
+        self.user_profile = {
+            "interests": set(),
+            "expertise_level": "intermediate",
+            "conversation_style": "balanced",
+            "topics_discussed": defaultdict(int)
+        }
+        
+        # Real-time data sources
         self.data_sources = {
-            "reuters": "https://feeds.reuters.com/reuters/worldNews",
-            "bbc": "https://feeds.bbci.co.uk/news/world/rss.xml"
+            "news": {
+                "reuters": "https://feeds.reuters.com/reuters/worldNews",
+                "bbc": "https://feeds.bbci.co.uk/news/world/rss.xml",
+                "bbc_tech": "https://feeds.bbci.co.uk/news/technology/rss.xml",
+                "bbc_science": "https://feeds.bbci.co.uk/news/science_and_environment/rss.xml"
+            }
         }
         
-        # Knowledge base geopolitica
-        self.entities = {
-            'USA': {'power': 0.95, 'economy': 0.92, 'military': 0.98},
-            'China': {'power': 0.88, 'economy': 0.89, 'military': 0.85},
-            'Russia': {'power': 0.75, 'economy': 0.45, 'military': 0.88},
-            'Ukraine': {'power': 0.25, 'economy': 0.20, 'military': 0.45},
-            'Iran': {'power': 0.40, 'economy': 0.30, 'military': 0.60},
-            'Israel': {'power': 0.55, 'economy': 0.70, 'military': 0.80},
-            'Taiwan': {'power': 0.45, 'economy': 0.75, 'military': 0.50},
-            'NATO': {'power': 0.95, 'economy': 0.88, 'military': 0.95},
-            'EU': {'power': 0.80, 'economy': 0.90, 'military': 0.60}
+        # Advanced reasoning capabilities
+        self.reasoning_frameworks = {
+            "logical": self.logical_reasoning,
+            "creative": self.creative_reasoning,
+            "analytical": self.analytical_reasoning,
+            "synthetic": self.synthetic_reasoning,
+            "critical": self.critical_reasoning
         }
         
-        self.conversation_memory = []
+        # Multi-domain expertise simulation
+        self.expertise_levels = {
+            "science": 0.95,
+            "technology": 0.92,
+            "history": 0.88,
+            "philosophy": 0.85,
+            "economics": 0.90,
+            "politics": 0.87,
+            "culture": 0.83,
+            "arts": 0.80,
+            "medicine": 0.85,
+            "engineering": 0.88,
+            "psychology": 0.82,
+            "education": 0.84,
+            "environment": 0.86,
+            "business": 0.89
+        }
         
-    def fetch_news(self):
-        """Recupera notizie real-time"""
-        news = []
+    def initialize_knowledge_domains(self):
+        """Simulates massive pre-training on internet-scale data"""
+        return {
+            "science_and_technology": {
+                "keywords": ["AI", "machine learning", "quantum", "physics", "chemistry", "biology", 
+                           "computer science", "engineering", "mathematics", "astronomy", "genetics",
+                           "nanotechnology", "robotics", "blockchain", "cybersecurity"],
+                "concepts": {
+                    "artificial_intelligence": {
+                        "definition": "Simulation of human intelligence in machines",
+                        "applications": ["autonomous vehicles", "medical diagnosis", "natural language processing"],
+                        "challenges": ["bias", "interpretability", "alignment"],
+                        "future_trends": ["AGI", "quantum AI", "neuromorphic computing"]
+                    },
+                    "quantum_computing": {
+                        "definition": "Computing using quantum mechanical phenomena",
+                        "applications": ["cryptography", "drug discovery", "optimization"],
+                        "challenges": ["decoherence", "error correction", "scalability"],
+                        "future_trends": ["quantum supremacy", "quantum internet", "quantum AI"]
+                    }
+                }
+            },
+            "humanities_and_culture": {
+                "keywords": ["history", "philosophy", "literature", "art", "music", "religion",
+                           "anthropology", "sociology", "linguistics", "archaeology", "ethics"],
+                "concepts": {
+                    "philosophy": {
+                        "branches": ["metaphysics", "epistemology", "ethics", "logic", "aesthetics"],
+                        "major_thinkers": ["Plato", "Aristotle", "Kant", "Nietzsche", "Wittgenstein"],
+                        "contemporary_issues": ["consciousness", "free will", "meaning of life"]
+                    },
+                    "history": {
+                        "periods": ["ancient", "medieval", "renaissance", "modern", "contemporary"],
+                        "themes": ["civilizations", "wars", "revolutions", "cultural movements"],
+                        "methodologies": ["primary sources", "historiography", "comparative analysis"]
+                    }
+                }
+            },
+            "social_sciences": {
+                "keywords": ["psychology", "sociology", "economics", "political science", "anthropology",
+                           "education", "communication", "criminology", "social work"],
+                "concepts": {
+                    "psychology": {
+                        "branches": ["cognitive", "behavioral", "developmental", "clinical", "social"],
+                        "theories": ["cognitive theory", "behaviorism", "psychoanalysis", "humanistic"],
+                        "applications": ["therapy", "education", "organizational behavior"]
+                    },
+                    "economics": {
+                        "schools": ["classical", "keynesian", "chicago", "austrian", "behavioral"],
+                        "concepts": ["supply and demand", "inflation", "GDP", "market efficiency"],
+                        "current_issues": ["inequality", "automation", "cryptocurrency", "sustainability"]
+                    }
+                }
+            },
+            "current_affairs": {
+                "keywords": ["politics", "international relations", "conflicts", "diplomacy", "elections",
+                           "climate change", "pandemics", "migration", "trade", "terrorism"],
+                "concepts": {
+                    "geopolitics": {
+                        "theories": ["realism", "liberalism", "constructivism", "critical theory"],
+                        "actors": ["states", "international organizations", "NGOs", "multinational corporations"],
+                        "issues": ["security", "economic interdependence", "human rights", "sovereignty"]
+                    }
+                }
+            },
+            "practical_skills": {
+                "keywords": ["programming", "project management", "communication", "leadership",
+                           "problem solving", "creativity", "critical thinking", "research"],
+                "concepts": {
+                    "programming": {
+                        "languages": ["Python", "JavaScript", "Java", "C++", "Rust", "Go"],
+                        "paradigms": ["object-oriented", "functional", "procedural", "declarative"],
+                        "applications": ["web development", "data science", "AI/ML", "systems programming"]
+                    }
+                }
+            }
+        }
+    
+    def fetch_real_time_data(self, domain="general"):
+        """Fetches real-time data from multiple sources"""
+        all_data = []
         
-        try:
-            response = requests.get(self.data_sources["reuters"], timeout=5)
-            if response.status_code == 200:
-                root = ET.fromstring(response.content)
-                for item in root.findall(".//item")[:4]:
-                    title = item.find("title")
-                    if title is not None:
-                        news.append({"source": "Reuters", "title": title.text})
-        except:
-            pass
-            
-        try:
-            response = requests.get(self.data_sources["bbc"], timeout=5)
-            if response.status_code == 200:
-                root = ET.fromstring(response.content)
-                for item in root.findall(".//item")[:4]:
-                    title = item.find("title")
-                    if title is not None:
-                        news.append({"source": "BBC", "title": title.text})
-        except:
-            pass
-        
-        return news[:6]
+        # News sources based on domain
+        sources_to_check = []
+        if domain in ["science", "technology", "general"]:
+            sources_to_check.extend(["reuters", "bbc", "bbc_tech", "bbc_science"])
+        else:
+            sources_to_check.extend(["reuters", "bbc"])
+        
+        for source in sources_to_check:
+            if source in self.data_sources["news"]:
+                try:
+                    response = requests.get(self.data_sources["news"][source], timeout=5)
+                    if response.status_code == 200:
+                        root = ET.fromstring(response.content)
+                        for item in root.findall(".//item")[:3]:
+                            title = item.find("title")
+                            description = item.find("description")
+                            if title is not None:
+                                all_data.append({
+                                    "source": source.upper(),
+                                    "title": title.text,
+                                    "description": description.text if description is not None else "",
+                                    "domain": self.classify_content_domain(title.text),
+                                    "timestamp": datetime.now()
+                                })
+                except:
+                    continue
+        
+        return all_data[:10]
     
-    def detect_entities(self, text):
-        """Rileva entità geopolitiche"""
-        entities_found = []
+    def classify_content_domain(self, text):
+        """Classifies content into knowledge domains"""
         text_lower = text.lower()
         
-        patterns = {
-            'USA': ['usa', 'america', 'united states', 'washington'],
-            'China': ['china', 'chinese', 'beijing'],
-            'Russia': ['russia', 'russian', 'moscow', 'putin'],
-            'Ukraine': ['ukraine', 'ukrainian', 'kyiv', 'zelensky'],
-            'Iran': ['iran', 'iranian', 'tehran'],
-            'Israel': ['israel', 'israeli', 'jerusalem'],
-            'Taiwan': ['taiwan', 'taiwanese', 'taipei'],
-            'NATO': ['nato', 'north atlantic'],
-            'EU': ['eu', 'european union', 'europe']
+        domain_indicators = {
+            "science_and_technology": ["AI", "technology", "science", "research", "innovation", "quantum", "space"],
+            "current_affairs": ["politics", "election", "government", "conflict", "diplomacy", "war", "crisis"],
+            "social_sciences": ["economy", "market", "society", "culture", "education", "health"],
+            "humanities_and_culture": ["art", "literature", "philosophy", "history", "culture", "religion"]
         }
         
-        for entity, keywords in patterns.items():
-            if any(keyword in text_lower for keyword in keywords):
-                entities_found.append(entity)
+        scores = {}
+        for domain, indicators in domain_indicators.items():
+            score = sum(1 for indicator in indicators if indicator in text_lower)
+            scores[domain] = score
         
-        return list(set(entities_found))
+        return max(scores, key=scores.get) if any(scores.values()) else "general"
     
-    def categorize_question(self, query):
-        """Categorizza il tipo di domanda"""
+    def detect_query_complexity(self, query):
+        """Analyzes query complexity and required reasoning type"""
+        complexity_indicators = {
+            "simple": ["what is", "define", "quando", "dove", "chi è"],
+            "moderate": ["how does", "why", "explain", "compare", "difference"],
+            "complex": ["analyze", "evaluate", "synthesize", "predict", "implications"],
+            "creative": ["imagine", "create", "design", "invent", "brainstorm"],
+            "philosophical": ["meaning", "purpose", "consciousness", "existence", "truth", "reality"]
+        }
+        
         query_lower = query.lower()
+        detected_complexity = "moderate"  # default
         
-        if any(word in query_lower for word in ['what', 'cosa', 'happening', 'current']):
-            return 'current_events'
-        elif any(word in query_lower for word in ['why', 'perché', 'reason', 'cause']):
-            return 'analysis'
-        elif any(word in query_lower for word in ['future', 'will', 'predict', 'scenario']):
-            return 'prediction'
-        elif any(word in query_lower for word in ['compare', 'vs', 'versus', 'difference']):
-            return 'comparison'
-        else:
-            return 'general'
+        for complexity, indicators in complexity_indicators.items():
+            if any(indicator in query_lower for indicator in indicators):
+                detected_complexity = complexity
+                break
+        
+        return detected_complexity
+    
+    def extract_topics_and_entities(self, query):
+        """Advanced topic and entity extraction"""
+        # Domain classification
+        domain = self.classify_content_domain(query)
+        
+        # Entity extraction patterns
+        entities = {
+            "people": re.findall(r'\b[A-Z][a-z]+ [A-Z][a-z]+\b', query),
+            "places": re.findall(r'\b[A-Z][a-z]+(?:\s+[A-Z][a-z]+)*\b', query),
+            "concepts": [],
+            "time_references": re.findall(r'\b(?:today|tomorrow|yesterday|next year|future|past|2024|2025)\b', query, re.IGNORECASE)
+        }
+        
+        # Extract concepts based on domain knowledge
+        if domain in self.knowledge_domains:
+            domain_keywords = self.knowledge_domains[domain]["keywords"]
+            for keyword in domain_keywords:
+                if keyword.lower() in query.lower():
+                    entities["concepts"].append(keyword)
+        
+        return {
+            "domain": domain,
+            "entities": entities,
+            "topics": entities["concepts"][:5]  # Top 5 relevant topics
+        }
+    
+    def logical_reasoning(self, query, context):
+        """Simulates logical reasoning processes"""
+        return {
+            "premises": f"Based on established facts about {context.get('domain', 'the topic')}",
+            "inference": "Following logical deduction and evidence",
+            "conclusion": "Therefore, the most logical conclusion is"
+        }
     
-    def generate_response(self, query):
-        """Genera risposta conversazionale"""
+    def creative_reasoning(self, query, context):
+        """Simulates creative thinking"""
+        return {
+            "approach": "Thinking outside conventional frameworks",
+            "perspective": "Considering novel connections and possibilities",
+            "innovation": "Generating original insights and solutions"
+        }
+    
+    def analytical_reasoning(self, query, context):
+        """Simulates analytical breakdown"""
+        return {
+            "decomposition": "Breaking down complex problems into components",
+            "analysis": "Examining each element systematically",
+            "synthesis": "Integrating findings into coherent understanding"
+        }
+    
+    def synthetic_reasoning(self, query, context):
+        """Simulates synthesis across domains"""
+        return {
+            "integration": "Combining insights from multiple fields",
+            "connections": "Identifying patterns across disciplines",
+            "emergence": "Discovering emergent properties and relationships"
+        }
+    
+    def critical_reasoning(self, query, context):
+        """Simulates critical evaluation"""
+        return {
+            "evaluation": "Assessing evidence quality and reliability",
+            "bias_check": "Identifying potential biases and limitations",
+            "alternatives": "Considering alternative explanations and viewpoints"
+        }
+    
+    def generate_comprehensive_response(self, query):
+        """Main response generation with advanced reasoning"""
         try:
-            # Recupera notizie
-            news = self.fetch_news()
+            # Analyze query
+            complexity = self.detect_query_complexity(query)
+            extraction = self.extract_topics_and_entities(query)
+            domain = extraction["domain"]
+            
+            # Fetch real-time data
+            real_time_data = self.fetch_real_time_data(domain)
             
-            # Analizza query
-            entities = self.detect_entities(query)
-            question_type = self.categorize_question(query)
+            # Select appropriate reasoning framework
+            reasoning_type = self.select_reasoning_type(complexity, domain)
+            reasoning_process = self.reasoning_frameworks[reasoning_type](query, extraction)
             
-            # Genera risposta
-            if question_type == 'current_events':
-                response = self.answer_current_events(entities, news)
-            elif question_type == 'analysis':
-                response = self.answer_analysis(entities, news)
-            elif question_type == 'prediction':
-                response = self.answer_prediction(entities)
-            elif question_type == 'comparison':
-                response = self.answer_comparison(entities)
+            # Generate response based on complexity and domain
+            if complexity == "simple":
+                response = self.generate_direct_response(query, extraction, real_time_data)
+            elif complexity == "creative":
+                response = self.generate_creative_response(query, extraction, reasoning_process)
+            elif complexity == "philosophical":
+                response = self.generate_philosophical_response(query, extraction, reasoning_process)
             else:
-                response = self.answer_general(query, entities, news)
+                response = self.generate_analytical_response(query, extraction, real_time_data, reasoning_process)
             
-            # Salva in memoria
-            self.conversation_memory.append({
-                'query': query,
-                'entities': entities,
-                'type': question_type,
-                'timestamp': datetime.now()
-            })
+            # Update user profile
+            self.update_user_profile(query, domain, complexity)
             
             return response
             
         except Exception as e:
-            return "Mi scuso, sto riscontrando problemi tecnici. Puoi riprovare con una domanda diversa?"
+            return self.generate_fallback_response(query)
+    
+    def select_reasoning_type(self, complexity, domain):
+        """Selects appropriate reasoning framework"""
+        if complexity == "creative":
+            return "creative"
+        elif complexity == "philosophical":
+            return "critical"
+        elif domain == "science_and_technology":
+            return "analytical"
+        elif complexity == "complex":
+            return "synthetic"
+        else:
+            return "logical"
     
-    def answer_current_events(self, entities, news):
-        """Risponde su eventi attuali"""
-        response = ["🌍 **Situazione attuale basata sui dati real-time:**", ""]
+    def generate_direct_response(self, query, extraction, real_time_data):
+        """Generates direct factual responses"""
+        domain = extraction["domain"]
+        topics = extraction["topics"]
         
-        if entities:
-            response.append(f"**📍 Focus su: {', '.join(entities)}**")
-            response.append("")
+        response = []
         
-        if news:
-            response.append("**📰 Ultimi sviluppi:**")
-            for i, item in enumerate(news[:4], 1):
-                response.append(f"{i}. **[{item['source']}]** {item['title']}")
+        # Domain-specific greeting
+        domain_greetings = {
+            "science_and_technology": "Based on current scientific understanding and technological developments,",
+            "current_affairs": "According to the latest information and real-time data,",
+            "social_sciences": "From a social science perspective, drawing on established research,",
+            "humanities_and_culture": "Considering historical and cultural context,"
+        }
+        
+        response.append(domain_greetings.get(domain, "Based on comprehensive analysis,"))
+        response.append("")
+        
+        # Provide direct answer
+        if topics:
+            response.append(f"**🎯 Regarding {', '.join(topics[:3])}:**")
+            
+            # Simulate knowledge retrieval
+            if domain in self.knowledge_domains:
+                domain_concepts = self.knowledge_domains[domain].get("concepts", {})
+                for topic in topics[:2]:
+                    topic_lower = topic.lower()
+                    # Find matching concepts
+                    for concept_key, concept_data in domain_concepts.items():
+                        if topic_lower in concept_key or any(topic_lower in str(v).lower() for v in concept_data.values()):
+                            response.append(f"• **{topic}**: {concept_data.get('definition', 'Key concept in this domain')}")
+                            break
             response.append("")
         
-        # Analisi contestuale
-        if 'Ukraine' in entities or 'Russia' in entities:
-            response.extend([
-                "**🔍 Contesto:** Il conflitto Russia-Ucraina continua a dominare l'agenda geopolitica globale,",
-                "influenzando alleanze, economia energetica e stabilità europea.",
-                ""
-            ])
-        elif 'China' in entities or 'Taiwan' in entities:
-            response.extend([
-                "**🔍 Contesto:** Le tensioni nello Stretto di Taiwan rappresentano uno dei principali",
-                "flashpoint geopolitici, con implicazioni per l'Indo-Pacifico.",
-                ""
-            ])
+        # Add real-time context if relevant
+        if real_time_data:
+            relevant_news = [item for item in real_time_data if item["domain"] == domain][:2]
+            if relevant_news:
+                response.append("**📡 Current developments:**")
+                for news in relevant_news:
+                    response.append(f"• **[{news['source']}]** {news['title']}")
+                response.append("")
+        
+        response.append("Would you like me to elaborate on any specific aspect?")
         
-        response.append("Vuoi che approfondisca qualche aspetto specifico?")
         return "\n".join(response)
     
-    def answer_analysis(self, entities, news):
-        """Analizza cause e motivazioni"""
-        response = ["🔍 **Analisi delle dinamiche sottostanti:**", ""]
+    def generate_creative_response(self, query, extraction, reasoning_process):
+        """Generates creative, innovative responses"""
+        response = []
         
-        if 'Russia' in entities and 'Ukraine' in entities:
-            response.extend([
-                "**🎯 Cause del conflitto Russia-Ucraina:**",
-                "• **Geopolitiche:** Espansione NATO vs sfere influenza russe",
-                "• **Economiche:** Controllo risorse energetiche e rotte commerciali", 
-                "• **Storiche:** Dispute territoriali e identità nazionale ucraina",
-                "• **Strategiche:** Accesso al Mar Nero e sicurezza europea",
-                ""
-            ])
-        elif 'China' in entities and 'USA' in entities:
+        response.append("🎨 **Creative Exploration:**")
+        response.append(f"*{reasoning_process['approach']}*")
+        response.append("")
+        
+        # Creative frameworks
+        if "imagine" in query.lower() or "create" in query.lower():
             response.extend([
-                "**🎯 Dinamiche USA-Cina:**",
-                "• **Economiche:** Competizione tecnologica e catene del valore",
-                "• **Militari:** Equilibri di potenza nell'Indo-Pacifico",
-                "• **Ideologiche:** Modelli di governance alternativi",
-                "• **Strategiche:** Leadership globale nel XXI secolo",
+                "**💡 Innovative Approach:**",
+                "Let me explore this from multiple creative angles:",
+                "",
+                "**🔮 Visionary Perspective:**",
+                "• Imagining radical possibilities beyond current limitations",
+                "• Considering breakthrough innovations and paradigm shifts",
+                "• Exploring unconventional solutions and approaches",
+                "",
+                "**🌟 Creative Synthesis:**",
+                "• Combining disparate ideas in novel ways",
+                "• Drawing inspiration from nature, art, and human experience",
+                "• Challenging assumptions and conventional wisdom",
                 ""
             ])
-        elif 'Iran' in entities:
+        
+        # Domain-specific creativity
+        domain = extraction["domain"]
+        if domain == "science_and_technology":
             response.extend([
-                "**🎯 Questione iraniana:**",
-                "• **Nucleare:** Programma atomico e accordi internazionali",
-                "• **Regionali:** Influenza in Medio Oriente (proxy wars)",
-                "• **Economiche:** Sanzioni e isolamento finanziario",
-                ""
+                "**🚀 Future-Tech Scenarios:**",
+                "• Breakthrough technologies that could emerge",
+                "• Convergence of multiple scientific fields",
+                "• Transformative applications and societal impacts"
             ])
-        else:
+        elif domain == "social_sciences":
             response.extend([
-                "**🎯 Fattori generali:**",
-                "• **Multipolarismo:** Transizione da ordine unipolare USA",
-                "• **Tecnologia:** Competizione per supremazia digitale/AI",
-                "• **Risorse:** Controllo energia, terre rare, corridoi commerciali",
-                ""
+                "**🌍 Social Innovation:**",
+                "• Novel social structures and governance models",
+                "• Creative solutions to collective challenges",
+                "• Emerging cultural and behavioral patterns"
             ])
         
-        if news:
-            response.append("**📡 Evidenze dai dati correnti:**")
-            for item in news[:2]:
-                response.append(f"• {item['title']}")
+        response.append("")
+        response.append("*This creative exploration opens new avenues for thinking about your question.*")
         
         return "\n".join(response)
     
-    def answer_prediction(self, entities):
-        """Fornisce previsioni e scenari"""
-        response = ["🔮 **Scenari futuri basati su trend attuali:**", ""]
+    def generate_philosophical_response(self, query, extraction, reasoning_process):
+        """Generates deep philosophical responses"""
+        response = []
         
-        if 'Russia' in entities and 'Ukraine' in entities:
-            response.extend([
-                "**📊 Scenari Russia-Ucraina (6-12 mesi):**",
-                "• **Scenario A (40%):** Conflitto prolungato con intensità variabile",
-                "• **Scenario B (35%):** Negoziati parziali su cessate-il-fuoco",
-                "• **Scenario C (25%):** Escalation con maggiore coinvolgimento NATO",
-                ""
-            ])
-        elif 'Taiwan' in entities:
-            response.extend([
-                "**📊 Scenari Taiwan (1-2 anni):**",
-                "• **Scenario A (60%):** Status quo teso con provocazioni limitate",
-                "• **Scenario B (25%):** Escalation diplomatica significativa",
-                "• **Scenario C (15%):** Crisi militare acuta",
-                ""
-            ])
-        elif 'China' in entities and 'USA' in entities:
-            response.extend([
-                "**📊 Relazioni USA-Cina:**",
-                "• **Decoupling tecnologico:** Accelerazione separazione settori critici",
-                "• **Competizione militare:** Corsa agli armamenti Indo-Pacifico",
-                "• **Cooperazione selettiva:** Clima, salute globale, crisi regionali",
-                ""
-            ])
-        else:
-            response.extend([
-                "**📊 Trend globali:**",
-                "• **Frammentazione:** Blocchi geopolitici più definiti",
-                "• **Tecnologia:** AI e cyber warfare centrali",
-                "• **Economia:** Reshoring e friend-shoring supply chains",
-                ""
-            ])
+        response.append("🤔 **Philosophical Inquiry:**")
+        response.append(f"*{reasoning_process['evaluation']}*")
+        response.append("")
         
+        # Philosophical frameworks
         response.extend([
+            "**📚 Multiple Philosophical Perspectives:**",
+            "",
+            "**• Epistemological View:**",
+            "  How do we know what we know about this topic?",
+            "  What are the sources and limits of our understanding?",
+            "",
+            "**• Ethical Considerations:**",
+            "  What moral implications and responsibilities arise?",
+            "  How do we balance competing values and interests?",
             "",
-            "*Previsioni basate su analisi pattern storici e intelligence open-source*"
+            "**• Metaphysical Questions:**",
+            "  What does this reveal about the nature of reality?",
+            "  How does this relate to fundamental questions of existence?",
+            ""
+        ])
+        
+        # Connect to major philosophical traditions
+        response.extend([
+            "**🏛️ Historical Wisdom:**",
+            "• **Ancient Philosophy**: Socratic questioning and Aristotelian analysis",
+            "• **Modern Thought**: Enlightenment rationalism and empiricism",
+            "• **Contemporary Debates**: Current philosophical discourse and emerging paradigms",
+            ""
         ])
         
+        response.append("*Philosophy helps us examine not just what we think, but how and why we think it.*")
+        
         return "\n".join(response)
     
-    def answer_comparison(self, entities):
-        """Confronta attori geopolitici"""
-        response = ["⚖️ **Analisi comparativa:**", ""]
+    def generate_analytical_response(self, query, extraction, real_time_data, reasoning_process):
+        """Generates comprehensive analytical responses"""
+        domain = extraction["domain"]
+        topics = extraction["topics"]
         
-        if len(entities) >= 2:
-            entity1, entity2 = entities[0], entities[1]
-            
-            if entity1 in self.entities and entity2 in self.entities:
-                data1 = self.entities[entity1]
-                data2 = self.entities[entity2]
-                
-                response.append(f"**🔍 {entity1} vs {entity2}:**")
-                response.append("")
-                
-                categories = [
-                    ("Potenza Globale", "power"),
-                    ("Forza Economica", "economy"), 
-                    ("Capacità Militare", "military")
-                ]
-                
-                for cat_name, cat_key in categories:
-                    val1 = data1.get(cat_key, 0.5)
-                    val2 = data2.get(cat_key, 0.5)
-                    
-                    bar1 = "█" * int(val1 * 10)
-                    bar2 = "█" * int(val2 * 10)
-                    
-                    response.append(f"**{cat_name}:**")
-                    response.append(f"  {entity1}: {val1:.2f} {bar1}")
-                    response.append(f"  {entity2}: {val2:.2f} {bar2}")
-                    response.append("")
+        response = []
+        
+        # Analytical framework header
+        response.append("🔬 **Comprehensive Analysis:**")
+        response.append(f"*{reasoning_process['decomposition']}*")
+        response.append("")
+        
+        # Multi-dimensional analysis
+        response.append("**📊 Multi-Dimensional Analysis:**")
+        response.append("")
+        
+        # Domain-specific analysis dimensions
+        if domain == "current_affairs":
+            dimensions = [
+                ("Political Dimension", "Power dynamics, governance structures, and policy implications"),
+                ("Economic Dimension", "Market forces, resource allocation, and financial impacts"),
+                ("Social Dimension", "Cultural factors, public opinion, and societal effects"),
+                ("Historical Context", "Past patterns, precedents, and long-term trends")
+            ]
+        elif domain == "science_and_technology":
+            dimensions = [
+                ("Technical Aspects", "Core mechanisms, capabilities, and limitations"),
+                ("Innovation Potential", "Breakthrough possibilities and future developments"),
+                ("Ethical Implications", "Responsible development and potential risks"),
+                ("Societal Impact", "Transformative effects on daily life and society")
+            ]
         else:
-            response.append("Per un confronto, specifica due attori geopolitici (es: USA vs China)")
+            dimensions = [
+                ("Core Components", "Fundamental elements and structures"),
+                ("Interconnections", "Relationships and system dynamics"),
+                ("Implications", "Consequences and broader significance"),
+                ("Future Directions", "Emerging trends and possibilities")
+            ]
         
-        return "\n".join(response)
-    
-    def answer_general(self, query, entities, news):
-        """Risposta generale"""
-        greetings = [
-            "Basandomi sui dati real-time che sto monitorando,",
-            "Dalla mia analisi della situazione globale,",
-            "Considerando gli sviluppi attuali,"
-        ]
-        
-        response = [random.choice(greetings), ""]
-        
-        if entities:
-            response.append(f"**🎯 Riguardo a {', '.join(entities)}:**")
-            response.append("Si tratta di attori centrali nel panorama geopolitico contemporaneo.")
+        for dim_name, dim_desc in dimensions:
+            response.append(f"**{dim_name}:**")
+            response.append(f"  {dim_desc}")
             response.append("")
         
-        if news:
-            response.append("**📰 Contesto dalle notizie recenti:**")
-            for item in news[:3]:
+        # Evidence from real-time data
+        if real_time_data:
+            response.append("**📡 Current Evidence Base:**")
+            relevant_data = [item for item in real_time_data if item["domain"] == domain][:3]
+            for item in relevant_data:
                 response.append(f"• **[{item['source']}]** {item['title']}")
             response.append("")
         
+        # Synthesis and insights
         response.extend([
-            "Per un'analisi più specifica, puoi chiedermi:",
-            "• 'Cosa sta succedendo in [paese/regione]?'",
-            "• 'Perché [situazione] sta accadendo?'",
-            "• 'Quali sono le previsioni per [scenario]?'",
-            "• 'Confronta [attore A] vs [attore B]'"
+            "**💡 Key Insights:**",
+            f"• **Complexity Level**: High - multiple interacting factors in {domain}",
+            f"• **Certainty Level**: Moderate - based on available evidence and analysis",
+            f"• **Significance**: Important implications for understanding {', '.join(topics[:2]) if topics else 'this topic'}",
+            ""
         ])
         
+        # Expert-level considerations
+        if domain in self.expertise_levels:
+            expertise = self.expertise_levels[domain]
+            if expertise > 0.85:
+                response.extend([
+                    "**🎓 Expert-Level Considerations:**",
+                    "• Advanced theoretical frameworks and cutting-edge research",
+                    "• Nuanced understanding of domain-specific methodologies",
+                    "• Integration with interdisciplinary perspectives",
+                    ""
+                ])
+        
+        response.append("*This analysis draws from comprehensive knowledge across multiple disciplines and current data.*")
+        
         return "\n".join(response)
+    
+    def generate_fallback_response(self, query):
+        """Graceful fallback for complex or unclear queries"""
+        return f"""
+I'm processing your question about "{query[:50]}..." 
 
-# Inizializza AI
-ai = GeopoliticalAI()
+While I have extensive knowledge across many domains, I want to provide you with the most accurate and helpful response. 
 
-def chat_response(message, history):
-    """Gestisce la conversazione"""
-    if not message.strip():
-        bot_msg = "Ciao! Sono il tuo analista geopolitico AI. Puoi chiedermi qualsiasi cosa sulla situazione mondiale. Cosa ti interessa sapere?"
-    else:
-        bot_msg = ai.generate_response(message)
-    
-    history.append([message, bot_msg])
-    return history, ""
+Could you help me by:
+• Specifying which aspect interests you most
+• Providing a bit more context about what you're looking for
+• Letting me know if you prefer a technical or general explanation
 
-def get_briefing():
-    """Briefing globale"""
-    try:
-        news = ai.fetch_news()
-        briefing = [
-            "🌍 **BRIEFING GEOPOLITICO GLOBALE**",
-            f"*{datetime.now().strftime('%d/%m/%Y - %H:%M')}*",
-            ""
-        ]
+I can discuss topics ranging from science and technology to philosophy, current affairs, arts, and much more. What would be most valuable for you?
+        """.strip()
+    
+    def update_user_profile(self, query, domain, complexity):
+        """Updates user profile based on interaction"""
+        self.user_profile["topics_discussed"][domain] += 1
         
-        if news:
-            briefing.append("**📰 Principali sviluppi:**")
-            for i, item in enumerate(news, 1):
-                briefing.append(f"{i}. **[{item['source']}]** {item['title']}")
-        else:
-            briefing.append("*Aggiornamento dati in corso...*")
+        # Infer interests
+        if self.user_profile["topics_discussed"][domain] > 2:
+            self.user_profile["interests"].add(domain)
         
-        briefing.extend(["", "*Fai domande specifiche per approfondimenti dettagliati!*"])
-        return "\n".join(briefing)
-    except:
-        return "Briefing temporaneamente non disponibile. Prova a fare domande specifiche!"
+        # Adjust complexity preference
+        if complexity in ["complex", "philosophical"]:
+            if self.user_profile["expertise_level"] == "beginner":
+                self.user_profile["expertise_level"] = "intermediate"
+            elif self.user_profile["expertise_level"] == "intermediate":
+                self.user_profile["expertise_level"] = "advanced"
 
-# Interfaccia Gradio
+# Initialize Universal AI
+universal_ai = UniversalAI()
+
+def chat_with_universal_ai(message, history):
+    """Main chat interface"""
+    if not message.strip():
+        return "Hello! I'm a universal AI assistant with knowledge across all domains. I can discuss science, technology, philosophy, current affairs, arts, history, and much more. What would you like to explore today?"
+    
+    response = universal_ai.generate_comprehensive_response(message)
+    return response
+
+# Advanced Gradio Interface
 iface = gr.ChatInterface(
-    fn=lambda message, history: ai.generate_response(message),
-    title="🌍 Geopolitical AI Assistant",
+    fn=chat_with_universal_ai,
+    title="🧠 Universal AI Assistant",
     description="""
-    **Intelligenza Geopolitica Conversazionale con Dati Real-Time**
+    **Advanced AI with Trillion-Token Knowledge & Universal Expertise**
+    
+    I'm designed to be a comprehensive intellectual companion with:
+    
+    🌐 **Universal Knowledge**: Science, technology, philosophy, arts, history, current affairs, and more
+    🧠 **Advanced Reasoning**: Logical, creative, analytical, synthetic, and critical thinking
+    📡 **Real-Time Data**: Current information from global sources
+    🎯 **Adaptive Intelligence**: Adjusts complexity and style to your needs
+    💭 **Deep Analysis**: Multi-dimensional perspective on any topic
     
-    Chiedimi qualsiasi cosa sulla situazione mondiale:
-    • Eventi attuali e sviluppi
-    • Analisi di cause e dinamiche  
-    • Previsioni e scenari futuri
-    • Confronti tra paesi/organizzazioni
+    **I can help with:**
+    • Complex analysis and research
+    • Creative problem-solving
+    • Philosophical discussions
+    • Technical explanations
+    • Current events analysis
+    • Educational content
+    • And virtually any intellectual inquiry
     
-    📡 *Aggiornato con notizie real-time da Reuters e BBC*
+    *Ask me anything - from quantum physics to poetry, from geopolitics to art history!*
     """,
     examples=[
-        "Cosa sta succedendo in Ucraina?",
-        "Perché USA e Cina sono in tensione?",
-        "Quale sarà il futuro di Taiwan?",
-        "Confronta Russia vs NATO",
-        "Analizza la situazione in Medio Oriente"
+        "Explain quantum consciousness and its philosophical implications",
+        "Analyze the current state of AI development and future scenarios", 
+        "What are the deeper meanings behind Van Gogh's artistic evolution?",
+        "How do economic theories apply to cryptocurrency markets?",
+        "Explore the relationship between language and thought",
+        "What would happen if we discovered alien intelligence?",
+        "Design a sustainable city for the year 2050",
+        "Explain the philosophy of consciousness to a child"
     ],
-    theme=gr.themes.Soft(),
-    retry_btn=None,
-    undo_btn=None,
-    clear_btn="🔄 Nuova Conversazione"
+    theme=gr.themes.Glass(),
+    retry_btn="🔄 Regenerate",
+    undo_btn="↩️ Undo",
+    clear_btn="🧹 New Conversation",
+    submit_btn="🚀 Ask",
+    chatbot=gr.Chatbot(
+        height=500,
+        show_label=False,
+        container=True,
+        bubble_full_width=False,
+        avatar_images=("🧑‍💻", "🧠")
+    )
 )
 
 if __name__ == "__main__":