Merge branch 'main' of https://github.com/arosha-w/CrimeLinkAnalyzer_backend into arosha

Author: arosha-w

database/facial_recognition_tables.sql

@@ -0,0 +1,267 @@
+-- ====================================================================
+-- Facial Recognition System - Database Schema
+-- ====================================================================
+-- This schema supports the facial recognition feature for CrimeLinkAnalyzer
+-- Created: December 11, 2025
+-- ====================================================================
+
+-- Enable required PostgreSQL extension for EXCLUDE constraints
+-- The btree_gist extension is required for using EXCLUDE constraints with equality operators
+-- This allows us to enforce "one primary photo per criminal" at the database level
+CREATE EXTENSION IF NOT EXISTS btree_gist;
+
+-- Drop existing tables if they exist (for clean setup)
+DROP TABLE IF EXISTS facial_recognition_logs CASCADE;
+DROP TABLE IF EXISTS suspect_photos CASCADE;
+DROP TABLE IF EXISTS criminals CASCADE;
+
+-- ====================================================================
+-- CRIMINALS TABLE
+-- ====================================================================
+-- Stores criminal records with biometric data
+CREATE TABLE criminals (
+    criminal_id SERIAL PRIMARY KEY,
+    name VARCHAR(255) NOT NULL,
+    nic VARCHAR(20) UNIQUE,
+    alias VARCHAR(255),
+    date_of_birth DATE,
+    gender VARCHAR(10) CHECK (gender IN ('Male', 'Female', 'Other')),
+    address TEXT,
+    nationality VARCHAR(100) DEFAULT 'Sri Lankan',
+    
+    -- Crime information stored as JSONB for flexibility
+    crime_history JSONB DEFAULT '[]'::jsonb,
+    
+    -- Primary photo reference
+    primary_photo_url VARCHAR(500),
+    
+    -- Face embedding - stored as BYTEA (binary)
+    -- This is the average embedding from all photos
+    face_embedding BYTEA,
+    embedding_model VARCHAR(50) DEFAULT 'buffalo_sc',
+    embedding_dimension INTEGER DEFAULT 512,
+    
+    -- Status tracking
+    status VARCHAR(20) DEFAULT 'active' CHECK (status IN ('active', 'inactive', 'archived')),
+    risk_level VARCHAR(20) DEFAULT 'medium' CHECK (risk_level IN ('low', 'medium', 'high', 'critical')),
+    
+    -- Audit fields
+    created_by INTEGER, -- User ID who created this record
+    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+    updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+    
+    -- Metadata
+    notes TEXT,
+    last_seen_location VARCHAR(255),
+    last_seen_date DATE
+);
+
+-- ====================================================================
+-- SUSPECT_PHOTOS TABLE
+-- ====================================================================
+-- Stores multiple photos per criminal for better accuracy
+CREATE TABLE suspect_photos (
+    photo_id SERIAL PRIMARY KEY,
+    criminal_id INTEGER NOT NULL REFERENCES criminals(criminal_id) ON DELETE CASCADE,
+    
+    -- Photo storage
+    photo_url VARCHAR(500) NOT NULL,
+    photo_hash VARCHAR(64) UNIQUE, -- SHA-256 hash to prevent duplicates
+    file_size_bytes INTEGER,
+    
+    -- Face detection metadata
+    face_embedding BYTEA NOT NULL, -- Individual photo embedding
+    face_confidence DECIMAL(5,2), -- Detection confidence (0-100)
+    face_bbox JSONB, -- Bounding box coordinates {x, y, width, height}
+    
+    -- Photo metadata
+    is_primary BOOLEAN DEFAULT FALSE,
+    photo_quality VARCHAR(20) CHECK (photo_quality IN ('low', 'medium', 'high', 'excellent')),
+    image_width INTEGER,
+    image_height INTEGER,
+    
+    -- Source tracking
+    source VARCHAR(100), -- e.g., 'manual_upload', 'cctv', 'arrest_record'
+    source_date DATE,
+    
+    -- Audit
+    uploaded_by INTEGER, -- User ID
+    uploaded_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+    
+    -- Constraints
+    CONSTRAINT only_one_primary_per_criminal 
+        EXCLUDE USING gist (criminal_id WITH =) 
+        WHERE (is_primary = true)
+);
+
+-- ====================================================================
+-- FACIAL_RECOGNITION_LOGS TABLE
+-- ====================================================================
+-- Audit trail for all facial recognition requests
+CREATE TABLE facial_recognition_logs (
+    log_id SERIAL PRIMARY KEY,
+    
+    -- Request details
+    analysis_type VARCHAR(50) DEFAULT 'suspect_match', -- 'suspect_match', 'criminal_registration'
+    uploaded_image_url VARCHAR(500),
+    uploaded_image_hash VARCHAR(64),
+    
+    -- Face detection results
+    face_detected BOOLEAN DEFAULT FALSE,
+    face_count INTEGER DEFAULT 0,
+    face_quality VARCHAR(20),
+    
+    -- Matching results
+    matches_found INTEGER DEFAULT 0,
+    best_match_criminal_id INTEGER REFERENCES criminals(criminal_id),
+    best_match_similarity DECIMAL(5,2), -- Percentage (0-100)
+    match_threshold DECIMAL(5,2) DEFAULT 75.00,
+    
+    -- All matches stored as JSONB for detailed analysis
+    all_matches JSONB DEFAULT '[]'::jsonb,
+    
+    -- Performance metrics
+    processing_time_ms INTEGER,
+    model_version VARCHAR(50),
+    
+    -- Security & Audit
+    requested_by INTEGER, -- User ID
+    user_role VARCHAR(50),
+    ip_address INET,
+    user_agent TEXT,
+    
+    -- Timestamps
+    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+    
+    -- Investigation reference
+    case_id VARCHAR(100),
+    investigation_notes TEXT
+);
+
+-- ====================================================================
+-- INDEXES FOR PERFORMANCE
+-- ====================================================================
+
+-- Criminal table indexes
+CREATE INDEX idx_criminal_nic ON criminals(nic);
+CREATE INDEX idx_criminal_name ON criminals USING gin(to_tsvector('english', name));
+CREATE INDEX idx_criminal_status ON criminals(status) WHERE status = 'active';
+CREATE INDEX idx_criminal_risk_level ON criminals(risk_level);
+CREATE INDEX idx_criminal_created_at ON criminals(created_at DESC);
+
+-- Suspect photos indexes
+CREATE INDEX idx_suspect_photos_criminal_id ON suspect_photos(criminal_id);
+CREATE INDEX idx_suspect_photos_primary ON suspect_photos(criminal_id, is_primary) WHERE is_primary = true;
+CREATE INDEX idx_suspect_photos_hash ON suspect_photos(photo_hash);
+
+-- Facial recognition logs indexes
+CREATE INDEX idx_fr_logs_created_at ON facial_recognition_logs(created_at DESC);
+CREATE INDEX idx_fr_logs_user ON facial_recognition_logs(requested_by);
+CREATE INDEX idx_fr_logs_best_match ON facial_recognition_logs(best_match_criminal_id) WHERE best_match_criminal_id IS NOT NULL;
+CREATE INDEX idx_fr_logs_case_id ON facial_recognition_logs(case_id) WHERE case_id IS NOT NULL;
+
+-- ====================================================================
+-- TRIGGERS FOR AUTO-UPDATE
+-- ====================================================================
+
+-- Automatically update updated_at timestamp
+CREATE OR REPLACE FUNCTION update_updated_at_column()
+RETURNS TRIGGER AS $$
+BEGIN
+    NEW.updated_at = CURRENT_TIMESTAMP;
+    RETURN NEW;
+END;
+$$ LANGUAGE plpgsql;
+
+CREATE TRIGGER update_criminals_updated_at
+    BEFORE UPDATE ON criminals
+    FOR EACH ROW
+    EXECUTE FUNCTION update_updated_at_column();
+
+-- ====================================================================
+-- UTILITY FUNCTIONS
+-- ====================================================================
+
+-- Function to calculate average embedding from multiple photos
+CREATE OR REPLACE FUNCTION calculate_average_embedding(p_criminal_id INTEGER)
+RETURNS BYTEA AS $$
+DECLARE
+    avg_embedding BYTEA;
+BEGIN
+    -- This will be called from Python after uploading multiple photos
+    -- Python will handle the actual embedding averaging logic
+    -- This function is a placeholder for future stored procedure implementation
+    RETURN NULL;
+END;
+$$ LANGUAGE plpgsql;
+
+-- Function to search similar faces (placeholder - actual search done in Python)
+CREATE OR REPLACE FUNCTION search_similar_faces(
+    p_embedding BYTEA,
+    p_threshold DECIMAL DEFAULT 0.75,
+    p_limit INTEGER DEFAULT 10
+)
+RETURNS TABLE (
+    criminal_id INTEGER,
+    name VARCHAR,
+    similarity DECIMAL
+) AS $$
+BEGIN
+    -- Actual similarity search is performed in Python using numpy
+    -- This is a placeholder for documentation
+    RETURN QUERY SELECT NULL::INTEGER, NULL::VARCHAR, NULL::DECIMAL LIMIT 0;
+END;
+$$ LANGUAGE plpgsql;
+
+-- ====================================================================
+-- INITIAL DATA / SEED DATA
+-- ====================================================================
+
+-- Insert sample criminal record for testing
+INSERT INTO criminals (
+    name, 
+    nic, 
+    date_of_birth, 
+    gender, 
+    crime_history,
+    status,
+    risk_level,
+    notes
+) VALUES (
+    'Test Suspect One',
+    '199012345678',
+    '1990-05-15',
+    'Male',
+    '[{"crime_type": "Theft", "date": "2023-03-10", "status": "Convicted", "sentence": "2 years"}]'::jsonb,
+    'active',
+    'medium',
+    'Sample criminal record for testing facial recognition system'
+);
+
+-- ====================================================================
+-- PERMISSIONS & SECURITY
+-- ====================================================================
+
+-- Grant appropriate permissions (adjust based on your user roles)
+-- GRANT SELECT, INSERT, UPDATE ON criminals TO crimelink_app_user;
+-- GRANT SELECT, INSERT ON suspect_photos TO crimelink_app_user;
+-- GRANT INSERT ON facial_recognition_logs TO crimelink_app_user;
+
+-- ====================================================================
+-- COMMENTS FOR DOCUMENTATION
+-- ====================================================================
+
+COMMENT ON TABLE criminals IS 'Stores criminal records with biometric face embeddings for facial recognition';
+COMMENT ON COLUMN criminals.face_embedding IS 'Average face embedding vector stored as binary data (512-dimensional float32 array)';
+COMMENT ON COLUMN criminals.crime_history IS 'JSON array of crime records: [{crime_type, date, status, sentence}]';
+
+COMMENT ON TABLE suspect_photos IS 'Multiple photos per criminal for improved recognition accuracy';
+COMMENT ON COLUMN suspect_photos.face_embedding IS 'Individual face embedding for this specific photo';
+COMMENT ON COLUMN suspect_photos.photo_hash IS 'SHA-256 hash to prevent duplicate photo uploads';
+
+COMMENT ON TABLE facial_recognition_logs IS 'Audit trail for all facial recognition analysis requests';
+COMMENT ON COLUMN facial_recognition_logs.all_matches IS 'JSON array of all matches: [{criminal_id, similarity, confidence}]';
+
+-- ====================================================================
+-- END OF SCHEMA
+-- ====================================================================

src/main/java/com/crimeLink/analyzer/config/RestTemplateConfig.java

@@ -0,0 +1,23 @@
+package com.crimeLink.analyzer.config;
+
+import org.springframework.context.annotation.Bean;
+import org.springframework.context.annotation.Configuration;
+import org.springframework.http.client.SimpleClientHttpRequestFactory;
+import org.springframework.web.client.RestTemplate;
+
+/**
+ * Configuration for RestTemplate bean used to communicate with ML microservices.
+ * Part of the hybrid monolith + microservices architecture.
+ */
+@Configuration
+public class RestTemplateConfig {
+
+    @Bean
+    public RestTemplate restTemplate() {
+        SimpleClientHttpRequestFactory factory = new SimpleClientHttpRequestFactory();
+        // Set timeout for ML service calls (30 seconds for heavy processing)
+        factory.setConnectTimeout(10000);  // 10 seconds connection timeout
+        factory.setReadTimeout(30000);     // 30 seconds read timeout (ML processing can be slow)
+        return new RestTemplate(factory);
+    }
+}

src/main/java/com/crimeLink/analyzer/config/SecurityConfig.java

@@ -46,6 +46,13 @@ public SecurityFilterChain securityFilterChain(HttpSecurity http) throws Excepti
                         .requestMatchers("/api/auth/**").permitAll()
                         .requestMatchers("/api/health").permitAll()
                         .requestMatchers("/api/admin/health").permitAll()
+                        .requestMatchers("/api/facial/health").permitAll()  // ML service health check
+                        .requestMatchers("/api/call-analysis/health").permitAll()  // ML service health check
+                        
+                        // ML Service endpoints - Investigator role only
+                        .requestMatchers("/api/call-analysis/**").hasRole("Investigator")
+                        .requestMatchers("/api/facial/**").hasRole("Investigator")
+                        
                         .requestMatchers("/api/database/**").permitAll()
                         .requestMatchers("/api/test").permitAll()
                         .requestMatchers("/api/debug/**").permitAll() // 🔍 Debug endpoints