HISTORICAL_ART_ANALYSIS_API

#!/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
    )