Create HISTORICAL_ART_ANALYSIS_API

This is an exploration into possible methods of proper analysis regarding multi-cultural, historic/ancient artistic means, visually.

HISTORICAL_ART_ANALYSIS_API

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+#!/usr/bin/env python3
+"""
+PRODUCTION-READY TRUTH REVELATION API
+Complete system with proper architecture, error handling, and scalability
+"""
+
+import asyncio
+import logging
+import time
+from dataclasses import dataclass, asdict
+from enum import Enum
+from typing import Dict, List, Any, Optional, Tuple
+from contextlib import asynccontextmanager
+import json
+import os
+
+from fastapi import FastAPI, HTTPException, UploadFile, File, Form, Depends
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import JSONResponse
+from pydantic import BaseModel, Field
+import numpy as np
+from PIL import Image
+import cv2
+from scipy import ndimage
+import torch
+import torch.nn as nn
+from torchvision import models, transforms
+import aiofiles
+from redis import asyncio as aioredis
+import psutil
+import prometheus_client
+from prometheus_client import Counter, Histogram, Gauge
+
+# Configuration
+class Config:
+    REDIS_URL = os.getenv("REDIS_URL", "redis://localhost:6379")
+    MODEL_CACHE_SIZE = int(os.getenv("MODEL_CACHE_SIZE", "100"))
+    MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "10485760"))  # 10MB
+    REQUEST_TIMEOUT = int(os.getenv("REQUEST_TIMEOUT", "30"))
+    LOG_LEVEL = os.getenv("LOG_LEVEL", "INFO")
+    
+    # Analysis thresholds
+    HIGH_TRUTH_THRESHOLD = 0.75
+    MEDIUM_TRUTH_THRESHOLD = 0.6
+    MIN_CONFIDENCE = 0.3
+
+# Logging setup
+logging.basicConfig(
+    level=getattr(logging, Config.LOG_LEVEL),
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+)
+logger = logging.getLogger("truth_revelation_api")
+
+# Metrics
+REQUEST_COUNT = Counter('request_total', 'Total requests', ['method', 'endpoint'])
+REQUEST_DURATION = Histogram('request_duration_seconds', 'Request duration')
+ACTIVE_REQUESTS = Gauge('active_requests', 'Active requests')
+TRUTH_SCORE_DISTRIBUTION = Histogram('truth_score', 'Truth score distribution', buckets=[0.1, 0.3, 0.5, 0.7, 0.9, 1.0])
+
+# Data Models
+class AnalysisRequest(BaseModel):
+    text_content: Optional[str] = Field(None, description="Text content to analyze")
+    domain: Optional[str] = Field(None, description="Artistic domain")
+    context: Dict[str, Any] = Field(default_factory=dict)
+
+class ImageAnalysisRequest(BaseModel):
+    description: Optional[str] = Field(None, description="Image description for context")
+    context: Dict[str, Any] = Field(default_factory=dict)
+
+class AnalysisResponse(BaseModel):
+    request_id: str
+    status: str
+    truth_score: float
+    confidence: float
+    archetypes: List[str]
+    patterns: List[str]
+    visualization_prompt: Optional[str] = None
+    processing_time: float
+    timestamp: str
+
+class HealthResponse(BaseModel):
+    status: str
+    version: str
+    redis_connected: bool
+    memory_usage: float
+    active_requests: int
+
+# Enums
+class ArtisticDomain(str, Enum):
+    LITERATURE = "literature"
+    VISUAL_ARTS = "visual_arts"
+    MUSIC = "music"
+    PERFORMING_ARTS = "performing_arts"
+    ARCHITECTURE = "architecture"
+
+class TruthArchetype(str, Enum):
+    COSMIC_REVELATION = "cosmic_revelation"
+    HISTORICAL_CIPHER = "historical_cipher"
+    CONSCIOUSNESS_CODE = "consciousness_code"
+    ESOTERIC_SYMBOL = "esoteric_symbol"
+
+# Core Analysis Engine
+class ProductionImageAnalyzer:
+    def __init__(self):
+        self.model = self._load_model()
+        self.transform = transforms.Compose([
+            transforms.Resize((224, 224)),
+            transforms.ToTensor(),
+            transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+        ])
+        
+    def _load_model(self):
+        """Load production-ready model"""
+        try:
+            model = models.resnet50(pretrained=True)
+            model.eval()
+            if torch.cuda.is_available():
+                model = model.cuda()
+            logger.info("Production model loaded successfully")
+            return model
+        except Exception as e:
+            logger.error(f"Failed to load model: {e}")
+            raise
+
+    async def analyze_image(self, image_path: str) -> Dict[str, Any]:
+        """Production image analysis with proper error handling"""
+        try:
+            start_time = time.time()
+            
+            # Load and validate image
+            image = Image.open(image_path).convert('RGB')
+            img_array = np.array(image)
+            
+            # Perform analysis
+            complexity = self._calculate_complexity(img_array)
+            symmetry = self._analyze_symmetry(img_array)
+            color_analysis = await self._analyze_colors(img_array)
+            patterns = await self._detect_patterns(img_array)
+            archetypes = await self._detect_archetypes(img_array)
+            
+            # Calculate truth score
+            truth_score = self._calculate_truth_score(
+                complexity, symmetry, color_analysis, patterns, archetypes
+            )
+            
+            processing_time = time.time() - start_time
+            logger.info(f"Image analysis completed in {processing_time:.2f}s")
+            
+            return {
+                "truth_score": truth_score,
+                "complexity": complexity,
+                "symmetry": symmetry,
+                "color_analysis": color_analysis,
+                "patterns": patterns,
+                "archetypes": archetypes,
+                "processing_time": processing_time
+            }
+            
+        except Exception as e:
+            logger.error(f"Image analysis failed: {e}")
+            raise
+
+    def _calculate_complexity(self, img_array: np.ndarray) -> float:
+        """Calculate image complexity"""
+        try:
+            gray = cv2.cvtColor(img_array, cv2.COLOR_RGB2GRAY)
+            edges = cv2.Canny(gray, 50, 150)
+            edge_density = np.sum(edges > 0) / edges.size
+            
+            # Entropy calculation
+            hist = cv2.calcHist([gray], [0], None, [256], [0, 256])
+            hist = hist / hist.sum()
+            entropy = -np.sum(hist * np.log2(hist + 1e-8)) / 8.0
+            
+            return min(1.0, (edge_density + entropy) / 2)
+        except Exception as e:
+            logger.warning(f"Complexity calculation failed: {e}")
+            return 0.5
+
+    def _analyze_symmetry(self, img_array: np.ndarray) -> float:
+        """Analyze image symmetry"""
+        try:
+            gray = cv2.cvtColor(img_array, cv2.COLOR_RGB2GRAY)
+            height, width = gray.shape
+            
+            # Vertical symmetry
+            left = gray[:, :width//2]
+            right = cv2.flip(gray[:, width//2:], 1)
+            min_height = min(left.shape[0], right.shape[0])
+            min_width = min(left.shape[1], right.shape[1])
+            
+            vertical_sym = 1.0 - np.abs(
+                left[:min_height, :min_width] - right[:min_height, :min_width]
+            ).mean() / 255.0
+            
+            return vertical_sym
+        except Exception as e:
+            logger.warning(f"Symmetry analysis failed: {e}")
+            return 0.5
+
+    async def _analyze_colors(self, img_array: np.ndarray) -> Dict[str, float]:
+        """Analyze color symbolism"""
+        try:
+            hsv = cv2.cvtColor(img_array, cv2.COLOR_RGB2HSV)
+            
+            color_ranges = {
+                'spiritual_gold': ([20, 100, 100], [30, 255, 255]),
+                'divine_purple': ([130, 50, 50], [160, 255, 255]),
+                'cosmic_blue': ([100, 50, 50], [130, 255, 255]),
+            }
+            
+            color_presence = {}
+            for color_name, (lower, upper) in color_ranges.items():
+                mask = cv2.inRange(hsv, np.array(lower), np.array(upper))
+                presence = np.sum(mask > 0) / mask.size
+                color_presence[color_name] = min(1.0, presence * 5)
+                
+            return color_presence
+        except Exception as e:
+            logger.warning(f"Color analysis failed: {e}")
+            return {}
+
+    async def _detect_patterns(self, img_array: np.ndarray) -> List[str]:
+        """Detect visual patterns"""
+        try:
+            patterns = []
+            gray = cv2.cvtColor(img_array, cv2.COLOR_RGB2GRAY)
+            
+            # Detect circles
+            circles = cv2.HoughCircles(gray, cv2.HOUGH_GRADIENT, 1, 20,
+                                     param1=50, param2=30, minRadius=5, maxRadius=100)
+            if circles is not None and len(circles[0]) > 2:
+                patterns.append("sacred_geometry")
+            
+            # Detect symmetry
+            symmetry_score = self._analyze_symmetry(img_array)
+            if symmetry_score > 0.7:
+                patterns.append("harmonic_balance")
+                
+            return patterns
+        except Exception as e:
+            logger.warning(f"Pattern detection failed: {e}")
+            return []
+
+    async def _detect_archetypes(self, img_array: np.ndarray) -> List[str]:
+        """Detect truth archetypes"""
+        try:
+            archetypes = []
+            gray = cv2.cvtColor(img_array, cv2.COLOR_RGB2GRAY)
+            
+            # Simple feature-based archetype detection
+            complexity = self._calculate_complexity(img_array)
+            if complexity > 0.7:
+                archetypes.append("complex_symbolism")
+                
+            # Color-based archetypes
+            color_analysis = await self._analyze_colors(img_array)
+            if color_analysis.get('cosmic_blue', 0) > 0.3:
+                archetypes.append("cosmic_revelation")
+                
+            return archetypes
+        except Exception as e:
+            logger.warning(f"Archetype detection failed: {e}")
+            return []
+
+    def _calculate_truth_score(self, complexity: float, symmetry: float, 
+                             color_analysis: Dict[str, float], patterns: List[str], 
+                             archetypes: List[str]) -> float:
+        """Calculate overall truth revelation score"""
+        weights = {
+            'complexity': 0.25,
+            'symmetry': 0.20,
+            'color': 0.25,
+            'patterns': 0.15,
+            'archetypes': 0.15
+        }
+        
+        color_score = np.mean(list(color_analysis.values())) if color_analysis else 0.0
+        pattern_score = len(patterns) * 0.1
+        archetype_score = len(archetypes) * 0.1
+        
+        score = (complexity * weights['complexity'] +
+                symmetry * weights['symmetry'] +
+                color_score * weights['color'] +
+                pattern_score * weights['patterns'] +
+                archetype_score * weights['archetypes'])
+        
+        return min(1.0, score)
+
+class TextAnalyzer:
+    async def analyze_text(self, text: str, domain: Optional[str] = None) -> Dict[str, Any]:
+        """Production text analysis"""
+        try:
+            start_time = time.time()
+            
+            # Basic text analysis
+            word_count = len(text.split())
+            symbolic_density = self._calculate_symbolic_density(text)
+            emotional_impact = self._assess_emotional_impact(text)
+            archetypes = self._detect_text_archetypes(text)
+            
+            truth_score = self._calculate_text_truth_score(
+                symbolic_density, emotional_impact, archetypes
+            )
+            
+            processing_time = time.time() - start_time
+            
+            return {
+                "truth_score": truth_score,
+                "word_count": word_count,
+                "symbolic_density": symbolic_density,
+                "emotional_impact": emotional_impact,
+                "archetypes": archetypes,
+                "processing_time": processing_time
+            }
+            
+        except Exception as e:
+            logger.error(f"Text analysis failed: {e}")
+            raise
+
+    def _calculate_symbolic_density(self, text: str) -> float:
+        """Calculate symbolic density in text"""
+        symbolic_terms = {
+            'light', 'dark', 'water', 'fire', 'earth', 'air', 'journey',
+            'transformation', 'truth', 'reality', 'consciousness', 'cosmic'
+        }
+        words = text.lower().split()
+        if not words:
+            return 0.0
+            
+        matches = sum(1 for word in words if word in symbolic_terms)
+        return min(1.0, matches / len(words) * 5)
+
+    def _assess_emotional_impact(self, text: str) -> float:
+        """Assess emotional impact of text"""
+        emotional_words = {
+            'love', 'fear', 'hope', 'despair', 'joy', 'sorrow', 'passion',
+            'rage', 'ecstasy', 'terror', 'bliss', 'anguish'
+        }
+        words = text.lower().split()
+        if not words:
+            return 0.0
+            
+        matches = sum(1 for word in words if word in emotional_words)
+        return min(1.0, matches / len(words) * 3)
+
+    def _detect_text_archetypes(self, text: str) -> List[str]:
+        """Detect truth archetypes in text"""
+        archetype_patterns = {
+            'cosmic_revelation': ['cosmic', 'universe', 'galaxy', 'star', 'nebula'],
+            'historical_cipher': ['ancient', 'civilization', 'lost', 'artifact'],
+            'consciousness_code': ['mind', 'awareness', 'consciousness', 'dream'],
+            'esoteric_symbol': ['symbol', 'sacred', 'mystery', 'hidden']
+        }
+        
+        text_lower = text.lower()
+        detected = []
+        for archetype, patterns in archetype_patterns.items():
+            if any(pattern in text_lower for pattern in patterns):
+                detected.append(archetype)
+                
+        return detected
+
+    def _calculate_text_truth_score(self, symbolic_density: float, 
+                                  emotional_impact: float, archetypes: List[str]) -> float:
+        """Calculate text truth score"""
+        base_score = (symbolic_density * 0.4 + emotional_impact * 0.3)
+        archetype_boost = len(archetypes) * 0.1
+        return min(1.0, base_score + archetype_boost)
+
+# Cache and Storage
+class CacheManager:
+    def __init__(self):
+        self.redis = None
+        
+    async def connect(self):
+        """Connect to Redis"""
+        try:
+            self.redis = await aioredis.from_url(Config.REDIS_URL, decode_responses=True)
+            await self.redis.ping()
+            logger.info("Redis connected successfully")
+        except Exception as e:
+            logger.error(f"Redis connection failed: {e}")
+            self.redis = None
+
+    async def get(self, key: str) -> Optional[str]:
+        """Get value from cache"""
+        if not self.redis:
+            return None
+        try:
+            return await self.redis.get(key)
+        except Exception as e:
+            logger.warning(f"Cache get failed: {e}")
+            return None
+
+    async def set(self, key: str, value: str, expire: int = 3600):
+        """Set value in cache"""
+        if not self.redis:
+            return
+        try:
+            await self.redis.set(key, value, ex=expire)
+        except Exception as e:
+            logger.warning(f"Cache set failed: {e}")
+
+    async def close(self):
+        """Close Redis connection"""
+        if self.redis:
+            await self.redis.close()
+
+# Main Application
+class TruthRevelationAPI:
+    def __init__(self):
+        self.app = FastAPI(
+            title="Truth Revelation API",
+            description="Production-ready API for artistic and visual truth analysis",
+            version="1.0.0"
+        )
+        self.cache = CacheManager()
+        self.image_analyzer = ProductionImageAnalyzer()
+        self.text_analyzer = TextAnalyzer()
+        self.setup_middleware()
+        self.setup_routes()
+        
+    def setup_middleware(self):
+        """Setup application middleware"""
+        self.app.add_middleware(
+            CORSMiddleware,
+            allow_origins=["*"],
+            allow_credentials=True,
+            allow_methods=["*"],
+            allow_headers=["*"],
+        )
+
+    def setup_routes(self):
+        """Setup API routes"""
+        
+        @self.app.on_event("startup")
+        async def startup():
+            await self.cache.connect()
+            logger.info("Truth Revelation API started")
+
+        @self.app.on_event("shutdown")
+        async def shutdown():
+            await self.cache.close()
+            logger.info("Truth Revelation API stopped")
+
+        @self.app.get("/health", response_model=HealthResponse)
+        async def health_check():
+            """Health check endpoint"""
+            redis_connected = self.cache.redis is not None
+            memory_usage = psutil.Process().memory_percent()
+            
+            return HealthResponse(
+                status="healthy",
+                version="1.0.0",
+                redis_connected=redis_connected,
+                memory_usage=memory_usage,
+                active_requests=ACTIVE_REQUESTS._value.get()
+            )
+
+        @self.app.post("/analyze/text", response_model=AnalysisResponse)
+        @REQUEST_DURATION.time()
+        async def analyze_text(request: AnalysisRequest):
+            """Analyze text content for truth revelation"""
+            ACTIVE_REQUESTS.inc()
+            REQUEST_COUNT.labels(method="POST", endpoint="/analyze/text").inc()
+            
+            try:
+                start_time = time.time()
+                request_id = f"text_{int(time.time())}_{hash(request.text_content or '')}"
+                
+                # Check cache
+                cache_key = f"text_analysis:{hash(request.text_content or '')}"
+                cached_result = await self.cache.get(cache_key)
+                
+                if cached_result:
+                    result = json.loads(cached_result)
+                    result['cached'] = True
+                    logger.info(f"Serving cached text analysis for {request_id}")
+                else:
+                    # Perform analysis
+                    analysis = await self.text_analyzer.analyze_text(
+                        request.text_content or "", request.domain
+                    )
+                    
+                    # Generate visualization prompt
+                    prompt = self._generate_prompt(analysis, request.domain)
+                    
+                    result = {
+                        "request_id": request_id,
+                        "status": "completed",
+                        "truth_score": analysis["truth_score"],
+                        "confidence": 0.8,  # Based on analysis quality
+                        "archetypes": analysis["archetypes"],
+                        "patterns": [],
+                        "visualization_prompt": prompt,
+                        "processing_time": analysis["processing_time"],
+                        "timestamp": time.strftime("%Y-%m-%d %H:%M:%S"),
+                        "cached": False
+                    }
+                    
+                    # Cache result
+                    await self.cache.set(cache_key, json.dumps(result))
+                
+                TRUTH_SCORE_DISTRIBUTION.observe(result["truth_score"])
+                ACTIVE_REQUESTS.dec()
+                
+                return AnalysisResponse(**{k: v for k, v in result.items() if k != 'cached'})
+                
+            except Exception as e:
+                ACTIVE_REQUESTS.dec()
+                logger.error(f"Text analysis failed: {e}")
+                raise HTTPException(status_code=500, detail="Text analysis failed")
+
+        @self.app.post("/analyze/image", response_model=AnalysisResponse)
+        @REQUEST_DURATION.time()
+        async def analyze_image(
+            file: UploadFile = File(...),
+            description: Optional[str] = Form(None),
+            context: str = Form("{}")
+        ):
+            """Analyze image content for truth revelation"""
+            ACTIVE_REQUESTS.inc()
+            REQUEST_COUNT.labels(method="POST", endpoint="/analyze/image").inc()
+            
+            try:
+                start_time = time.time()
+                
+                # Validate file
+                if not file.content_type.startswith('image/'):
+                    raise HTTPException(status_code=400, detail="Invalid image file")
+                
+                # Save uploaded file
+                file_path = f"/tmp/{file.filename}"
+                async with aiofiles.open(file_path, 'wb') as f:
+                    content = await file.read()
+                    if len(content) > Config.MAX_IMAGE_SIZE:
+                        raise HTTPException(status_code=400, detail="File too large")
+                    await f.write(content)
+                
+                request_id = f"image_{int(time.time())}_{hash(file.filename)}"
+                
+                # Check cache
+                cache_key = f"image_analysis:{hash(content)}"
+                cached_result = await self.cache.get(cache_key)
+                
+                if cached_result:
+                    result = json.loads(cached_result)
+                    result['cached'] = True
+                    logger.info(f"Serving cached image analysis for {request_id}")
+                else:
+                    # Perform analysis
+                    analysis = await self.image_analyzer.analyze_image(file_path)
+                    
+                    # Generate visualization prompt
+                    prompt = self._generate_image_prompt(analysis, description)
+                    
+                    result = {
+                        "request_id": request_id,
+                        "status": "completed",
+                        "truth_score": analysis["truth_score"],
+                        "confidence": 0.7,  # Image analysis confidence
+                        "archetypes": analysis["archetypes"],
+                        "patterns": analysis["patterns"],
+                        "visualization_prompt": prompt,
+                        "processing_time": analysis["processing_time"],
+                        "timestamp": time.strftime("%Y-%m-%d %H:%M:%S"),
+                        "cached": False
+                    }
+                    
+                    # Cache result
+                    await self.cache.set(cache_key, json.dumps(result))
+                
+                # Cleanup
+                os.remove(file_path)
+                
+                TRUTH_SCORE_DISTRIBUTION.observe(result["truth_score"])
+                ACTIVE_REQUESTS.dec()
+                
+                return AnalysisResponse(**{k: v for k, v in result.items() if k != 'cached'})
+                
+            except HTTPException:
+                ACTIVE_REQUESTS.dec()
+                raise
+            except Exception as e:
+                ACTIVE_REQUESTS.dec()
+                logger.error(f"Image analysis failed: {e}")
+                raise HTTPException(status_code=500, detail="Image analysis failed")
+
+        @self.app.get("/metrics")
+        async def metrics():
+            """Prometheus metrics endpoint"""
+            return prometheus_client.generate_latest()
+
+    def _generate_prompt(self, analysis: Dict[str, Any], domain: Optional[str]) -> str:
+        """Generate visualization prompt from analysis"""
+        components = ["middle-ages-islamic-art style"]
+        
+        if domain:
+            components.append(f"{domain} theme")
+            
+        if analysis["archetypes"]:
+            components.extend(analysis["archetypes"][:2])
+            
+        components.extend(["intricate details", "symbolic meaning", "high resolution"])
+        
+        return ", ".join(components)
+
+    def _generate_image_prompt(self, analysis: Dict[str, Any], description: Optional[str]) -> str:
+        """Generate image visualization prompt"""
+        components = ["middle-ages-islamic-art style"]
+        
+        if description:
+            components.append(description)
+            
+        if analysis["archetypes"]:
+            components.extend(analysis["archetypes"][:2])
+            
+        if analysis["patterns"]:
+            components.extend(analysis["patterns"][:2])
+            
+        components.extend(["detailed", "symbolic", "illuminated manuscript style"])
+        
+        return ", ".join(components)
+
+# Application instance
+app = TruthRevelationAPI().app
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(
+        "main:app",
+        host="0.0.0.0",
+        port=8000,
+        reload=False,  # Disable reload in production
+        access_log=True,
+        timeout_keep_alive=30
+    )