Bernt
aee0f09db8
- Datafabrik: Dockerfile fix, agentorkestrering fungerar - Vision: Identify-modell, FAISS, OCR alla testade - API: Alla 7 integrationstester passerade - Upplösare: Entitetsupplösning verifierad
656 lines
27 KiB
Python
656 lines
27 KiB
Python
"""
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Landvex Intelligence Fusion Engine (LIFE) v2
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Operativ intelligensmotor med 7 kärnkomponenter:
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1. Evidence Graph (EG)
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2. Evidence Lineage
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3. Temporal Reality Engine
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4. Contradiction Knowledge Base
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5. Reality DNA
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6. Reality Memory
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7. Reality Hypothesis Engine
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Core Principle: Evidence Before Opinion
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"""
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from fastapi import APIRouter, Depends, HTTPException, status
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from sqlalchemy.orm import Session
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from typing import List, Optional, Dict, Any
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from datetime import datetime, timedelta
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from enum import Enum
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from app.database import get_db
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from app.core.security import get_current_user
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from app.models import User
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router = APIRouter(prefix="/life-v2", tags=["life_engine_v2"])
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# ─── 1. EVIDENCE GRAPH (EG) ──────────────────────────────────────────────────
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@router.get("/public/evidence-graph/{project_id}")
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async def get_evidence_graph(project_id: str):
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"""
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Evidence Graph - Varje observation är en nod i en graf.
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Observation → Object → Project → Programme → Organisation → Region → Country
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"""
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return {
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"project_id": project_id,
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"graph_structure": {
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"description": "Hierarchical evidence graph where every observation is a traceable node",
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"levels": [
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{
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"level": "Observation",
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"description": "Individual data points (photos, measurements, reports)",
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"example": "Satellite image showing construction progress on 2026-07-01",
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"count": 156,
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},
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{
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"level": "Object",
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"description": "Physical entities being observed",
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"example": "School building, water pump, road segment",
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"count": 12,
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},
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{
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"level": "Project",
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"description": "Development project being monitored",
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"example": "Mogadishu Primary School Renovation",
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"count": 1,
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},
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{
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"level": "Programme",
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"description": "Funding programme or initiative",
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"example": "UNICEF Education Programme 2025-2027",
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"count": 1,
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},
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{
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"level": "Organisation",
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"description": "Implementing or funding organization",
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"example": "UNICEF, Somali Education Consortium",
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"count": 2,
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},
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{
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"level": "Region",
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"description": "Geographic region",
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"example": "East Africa, Banadir Region",
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"count": 2,
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},
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{
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"level": "Country",
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"description": "National level",
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"example": "Somalia",
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"count": 1,
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},
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],
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},
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"example_trace": {
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"conclusion": "Construction is 85% complete",
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"evidence_chain": [
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{
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"node_type": "Observation",
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"id": "obs-001",
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"data": "Field photo showing exterior walls complete",
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"source": "QUIXZOOM contributor C-123",
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"confidence": 92,
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},
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{
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"node_type": "Observation",
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"id": "obs-002",
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"data": "Satellite imagery shows roof structure present",
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"source": "Sentinel-2",
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"confidence": 95,
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},
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{
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"node_type": "Object",
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"id": "obj-001",
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"name": "School Building Block A",
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"aggregated_confidence": 93,
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},
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{
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"node_type": "Project",
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"id": project_id,
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"name": "Mogadishu Primary School Renovation",
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"aggregated_confidence": 85,
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},
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],
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},
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"query_capabilities": [
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"Trace any conclusion back to original observations",
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"Find all observations supporting a specific claim",
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"Identify evidence gaps at any level",
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"Compare confidence across different branches",
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],
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}
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# ─── 2. EVIDENCE LINEAGE ─────────────────────────────────────────────────────
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@router.get("/public/evidence-lineage/{observation_id}")
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async def get_evidence_lineage(observation_id: str):
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"""
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Evidence Lineage - Komplett ursprung för varje datapunkt.
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Source → Collected → Validated → Cross-validated → AI confidence → Human verification → Historical revisions → Current confidence
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"""
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return {
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"observation_id": observation_id,
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"lineage": {
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"source": {
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"type": "QUIXZOOM field observation",
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"contributor_id": "C-123",
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"contributor_reputation": 94,
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"collection_device": "iPhone 14 Pro",
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"gps_accuracy": "±3 meters",
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"timestamp": "2026-07-03T14:30:00Z",
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},
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"collected": {
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"raw_data": "Original image + metadata",
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"checksum": "sha256:a1b2c3...",
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"collection_conditions": "Daylight, clear weather",
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"collector_notes": "Building exterior appears complete, interior work visible through windows",
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},
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"validated": {
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"ai_validation": {
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"model": "Landvex Construction Progress v2.1",
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"confidence": 88,
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"detected_features": ["walls", "roof", "windows", "scaffolding"],
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},
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"geospatial_validation": {
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"matches_expected_location": True,
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"location_deviation": "0.5 meters",
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},
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"temporal_validation": {
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"timestamp_verified": True,
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"sequence_consistent": True,
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},
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},
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"cross_validated": {
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"supporting_observations": [
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{"id": "obs-003", "source": "Satellite", "agreement": 95},
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{"id": "obs-004", "source": "Another contributor", "agreement": 82},
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],
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"conflicting_observations": [],
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"cross_validation_score": 91,
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},
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"ai_confidence": {
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"initial_score": 85,
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"after_validation": 88,
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"after_cross_validation": 91,
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"factors": [
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{"factor": "Image quality", "impact": +3},
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{"factor": "Multiple angles available", "impact": +5},
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{"factor": "Limited interior visibility", "impact": -2},
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],
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},
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"human_verification": {
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"verified_by": "Analyst-007",
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"verification_date": "2026-07-04",
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"verification_method": "Visual inspection + cross-reference with satellite",
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"human_confidence": 90,
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"notes": "Exterior completion confirmed. Interior estimate based on visible progress.",
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},
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"historical_revisions": [
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{
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"date": "2026-07-03",
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"confidence": 85,
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"change": "Initial upload",
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},
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{
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"date": "2026-07-04",
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"confidence": 88,
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"change": "AI validation complete",
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},
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{
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"date": "2026-07-04",
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"confidence": 91,
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"change": "Cross-validation with satellite complete",
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},
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],
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"current_confidence": {
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"score": 91,
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"level": "high",
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"last_updated": "2026-07-04T10:00:00Z",
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"next_review": "2026-07-11",
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},
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},
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"value": "When someone questions an analysis, show exactly how this observation was collected, validated, and verified.",
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}
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# ─── 3. TEMPORAL REALITY ENGINE ──────────────────────────────────────────────
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@router.get("/public/temporal/{project_id}")
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async def get_temporal_reality(project_id: str):
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"""
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Temporal Reality Engine - Tidsmaskin för att se hur verkligheten förändras.
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"""
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return {
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"project_id": project_id,
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"temporal_capabilities": [
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"What did the area look like six months ago?",
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"What has changed?",
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"How fast?",
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"When did the change begin?",
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],
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"timeline": [
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{
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"date": "2025-09-01",
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"description": "Project start",
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"satellite_image": "https://cdn.landvex.com/sat/2025-09-01/proj-001.jpg",
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"observations": 0,
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"ai_analysis": "Bare ground, no construction activity",
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"confidence": 95,
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},
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{
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"date": "2025-12-01",
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"description": "Foundation complete",
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"satellite_image": "https://cdn.landvex.com/sat/2025-12-01/proj-001.jpg",
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"observations": 12,
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"ai_analysis": "Foundation visible, materials on site",
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"confidence": 92,
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},
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{
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"date": "2026-03-01",
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"description": "Structure rising",
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"satellite_image": "https://cdn.landvex.com/sat/2026-03-01/proj-001.jpg",
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"observations": 45,
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"ai_analysis": "Walls visible, roof framework started",
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"confidence": 90,
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},
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{
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"date": "2026-06-01",
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"description": "Roof complete",
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"satellite_image": "https://cdn.landvex.com/sat/2026-06-01/proj-001.jpg",
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"observations": 89,
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"ai_analysis": "Roof complete, exterior walls finished",
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"confidence": 93,
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},
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{
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"date": "2026-07-03",
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"description": "Current state",
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"satellite_image": "https://cdn.landvex.com/sat/2026-07-03/proj-001.jpg",
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"observations": 156,
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"ai_analysis": "Exterior 95% complete, interior work ongoing",
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"confidence": 88,
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},
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],
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"change_detection": {
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"total_changes_detected": 23,
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"significant_changes": [
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{
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"date": "2025-12-15",
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"type": "construction_start",
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"description": "First visible construction activity",
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"confidence": 95,
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},
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{
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"date": "2026-02-20",
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"type": "acceleration",
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"description": "Construction pace increased 40%",
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"confidence": 82,
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},
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{
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"date": "2026-06-15",
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"type": "flooding_impact",
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"description": "Accessibility reduced due to flooding",
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"confidence": 92,
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},
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],
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},
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"temporal_queries": {
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"what_did_it_look_like_6_months_ago": {
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"date": "2026-01-03",
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"description": "Foundation complete, walls starting to rise",
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"satellite_image": "https://cdn.landvex.com/sat/2026-01-03/proj-001.jpg",
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},
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"what_has_changed": {
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"changes": [
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"Walls completed (0% → 100%)",
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"Roof added (0% → 100%)",
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"Windows installed (0% → 80%)",
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"Interior work started (0% → 60%)",
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],
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"time_period": "6 months",
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},
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"how_fast": {
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"average_progress_per_month": "12%",
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"fastest_month": "March 2026 (18%)",
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"slowest_month": "June 2026 (5%, due to flooding)",
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},
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"when_did_change_begin": {
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"construction_start": "2025-12-10",
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"acceleration": "2026-02-15",
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"flooding_impact": "2026-06-15",
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},
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},
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}
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# ─── 4. CONTRADICTION KNOWLEDGE BASE ─────────────────────────────────────────
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@router.get("/public/contradictions")
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async def get_contradiction_knowledge_base():
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"""
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Contradiction Knowledge Base - Alla identifierade avvikelser lagras för AI-träning.
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"""
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return {
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"description": "All identified discrepancies are stored to train AI on real-world project development patterns",
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"total_contradictions": 1247,
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"categories": {
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"schedule_variance": 342,
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"progress_reporting": 289,
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"operational_status": 198,
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"budget_execution": 156,
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"maintenance_gap": 134,
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"location_discrepancy": 67,
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"other": 61,
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},
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"example_entries": [
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{
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"id": "contr-001",
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"type": "schedule_variance",
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"project": "Mogadishu Primary School",
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"official_report": "95% complete (June 2026)",
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"satellite": "88% complete (July 2026)",
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"quixzoom": "85% complete (July 2026)",
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"news": "No relevant reports",
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"public_procurement": "Materials delivered through June",
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"confidence": 78,
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"resolution": "Pending follow-up observation",
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"lessons_learned": "Interior work often underreported in official progress",
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},
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{
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"id": "contr-002",
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"type": "operational_status",
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"project": "Nairobi Health Clinic",
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"official_report": "Fully operational",
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"satellite": "Activity detected",
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"quixzoom": "Reduced hours observed, staff shortage",
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"news": "Healthcare worker strike reported",
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"public_procurement": "No recent medical supply contracts",
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"confidence": 85,
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"resolution": "Partial - strike ended, staffing still reduced",
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"lessons_learned": "Operational status should include staffing levels",
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},
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],
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"ai_training_value": {
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"description": "After several years, AI can be trained on hundreds of thousands of real-world examples",
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"current_training_set": 1247,
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"projected_2027": 5000,
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"projected_2028": 15000,
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"use_cases": [
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"Predict likely discrepancy types by project category",
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"Identify early warning signals",
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"Benchmark reporting accuracy by organization type",
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"Improve confidence scoring models",
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],
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},
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}
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# ─── 5. REALITY DNA ──────────────────────────────────────────────────────────
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@router.get("/public/reality-dna/{project_id}")
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async def get_reality_dna(project_id: str):
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"""
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Reality DNA - Projektets fingeravtryck som kan jämföras med liknande projekt.
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"""
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return {
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"project_id": project_id,
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"dna_profile": {
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"description": "Unique fingerprint of project characteristics for benchmarking",
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"dimensions": [
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{
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"name": "Activity",
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"score": 78,
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"description": "Level of observable activity",
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"indicators": ["Worker presence", "Equipment operation", "Material delivery"],
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},
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{
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"name": "Maintenance",
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"score": 65,
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"description": "Observable maintenance quality",
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"indicators": ["Physical condition", "Repair frequency", "Upkeep standards"],
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},
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{
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"name": "Infrastructure",
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"score": 82,
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"description": "Infrastructure completeness and quality",
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"indicators": ["Construction progress", "Material quality", "Specification compliance"],
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},
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{
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"name": "Community Usage",
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"score": 70,
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"description": "Actual utilization by community",
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"indicators": ["Visitor counts", "Usage patterns", "User feedback"],
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},
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{
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"name": "Traffic",
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"score": 75,
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"description": "Transportation and accessibility",
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"indicators": ["Road condition", "Transport frequency", "Accessibility"],
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},
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{
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"name": "Economic Activity",
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"score": 60,
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"description": "Economic enablement",
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"indicators": ["Local employment", "Market activity", "Income effects"],
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},
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{
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"name": "Environmental Status",
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"score": 72,
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"description": "Environmental impact",
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"indicators": ["Resource efficiency", "Pollution", "Ecosystem health"],
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},
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{
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"name": "Safety",
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"score": 80,
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"description": "Observable safety conditions",
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"indicators": ["Structural integrity", "Safety equipment", "Hazard presence"],
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},
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{
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"name": "Operational Continuity",
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"score": 68,
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"description": "Uninterrupted service delivery",
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"indicators": ["Uptime", "Service consistency", "Disruption frequency"],
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},
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],
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},
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"benchmarking": {
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"similar_projects": [
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{
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"project_id": "proj-089",
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"name": "Kismayo School Construction",
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"similarity_score": 87,
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"comparison": {
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"activity": "+5%",
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"maintenance": "-3%",
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"infrastructure": "+2%",
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"community_usage": "+8%",
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},
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},
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{
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"project_id": "proj-156",
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"name": "Garowe Education Center",
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"similarity_score": 82,
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"comparison": {
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"activity": "-2%",
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"maintenance": "+4%",
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"infrastructure": "-1%",
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"community_usage": "+5%",
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},
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},
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],
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"category_average": {
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"activity": 75,
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"maintenance": 68,
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"infrastructure": 80,
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"community_usage": 72,
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"traffic": 73,
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"economic_activity": 65,
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"environmental_status": 70,
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"safety": 78,
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"operational_continuity": 70,
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},
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},
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}
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# ─── 6. REALITY MEMORY ───────────────────────────────────────────────────────
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@router.get("/public/reality-memory/{project_id}")
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async def get_reality_memory(project_id: str):
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"""
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Reality Memory - LIFE kastar aldrig bort något.
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Varje observation, AI-bedömning och förändring sparas.
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"""
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return {
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"project_id": project_id,
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"memory_principles": [
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"Every observation is preserved",
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"Every AI judgment is preserved",
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"Every change is preserved",
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"Full reproducibility at any point in time",
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],
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"storage_stats": {
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"total_observations": 45000,
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"total_ai_judgments": 12500,
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"total_changes_detected": 8900,
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"total_revisions": 23400,
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"storage_size_tb": 2.4,
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"retention_period": "Permanent",
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},
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"time_travel": {
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"description": "Reconstruct how the system reasoned at any point in time",
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"example_queries": [
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{
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"query": "What did we know on March 15, 2026?",
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"answer": "At that time, we had 45 observations showing 60% completion. AI confidence was 75%. No contradictions detected.",
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},
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{
|
|
"query": "When did we first detect the flooding impact?",
|
|
"answer": "First detected on June 16, 2026, based on satellite change detection and 3 field observations.",
|
|
},
|
|
{
|
|
"query": "How has our confidence evolved?",
|
|
"answer": "Started at 65% (March), increased to 78% (April), peaked at 85% (May), dropped to 82% (July due to discrepancy).",
|
|
},
|
|
],
|
|
},
|
|
"audit_trail": {
|
|
"description": "Complete audit trail for compliance and research",
|
|
"capabilities": [
|
|
"Reproduce any historical analysis",
|
|
"Track confidence evolution",
|
|
"Identify when contradictions were first detected",
|
|
"Show how AI models improved over time",
|
|
"Demonstrate compliance with verification standards",
|
|
],
|
|
},
|
|
}
|
|
|
|
|
|
# ─── 7. REALITY HYPOTHESIS ENGINE ────────────────────────────────────────────
|
|
|
|
@router.get("/public/hypotheses/{project_id}")
|
|
async def get_hypotheses(project_id: str):
|
|
"""
|
|
Reality Hypothesis Engine - Inte "Det här är orsaken" utan
|
|
"Utifrån tillgänglig evidens finns följande möjliga förklaringar..."
|
|
"""
|
|
return {
|
|
"project_id": project_id,
|
|
"disclaimer": "These are hypotheses ranked by how well they are supported by observable data. They are not certainties.",
|
|
"observed_phenomenon": "Construction progress slower than reported (85% observed vs 95% reported)",
|
|
"hypotheses": [
|
|
{
|
|
"rank": 1,
|
|
"hypothesis": "Interior work is behind schedule while exterior is nearly complete",
|
|
"supporting_evidence": [
|
|
"Field observations show exterior walls and roof complete",
|
|
"Windows visible but interior not accessible",
|
|
"Satellite cannot detect interior progress",
|
|
],
|
|
"confidence": 78,
|
|
"support_strength": "strong",
|
|
"recommended_action": "Request interior photos or schedule inspection",
|
|
},
|
|
{
|
|
"rank": 2,
|
|
"hypothesis": "Flooding in June caused 2-week delay",
|
|
"supporting_evidence": [
|
|
"Satellite shows flooding in area (June 15)",
|
|
"Weather data confirms 120% average rainfall",
|
|
"Field observations show reduced activity June 15-30",
|
|
],
|
|
"confidence": 72,
|
|
"support_strength": "moderate",
|
|
"recommended_action": "Verify if delay is reflected in revised timeline",
|
|
},
|
|
{
|
|
"rank": 3,
|
|
"hypothesis": "Material supply disruption affected interior work",
|
|
"supporting_evidence": [
|
|
"Procurement data shows delayed deliveries",
|
|
"Some materials visible on site but not installed",
|
|
],
|
|
"confidence": 55,
|
|
"support_strength": "weak",
|
|
"recommended_action": "Check supplier delivery records",
|
|
},
|
|
{
|
|
"rank": 4,
|
|
"hypothesis": "Reporting methodology differs from observation methodology",
|
|
"supporting_evidence": [
|
|
"Official report may count 'started' as 'complete'",
|
|
"Different measurement standards possible",
|
|
],
|
|
"confidence": 45,
|
|
"support_strength": "speculative",
|
|
"recommended_action": "Review reporting methodology with implementing partner",
|
|
},
|
|
],
|
|
"evidence_gaps": [
|
|
"Interior completion percentage (need photos)",
|
|
"Revised construction timeline from contractor",
|
|
"Material delivery receipts for June-July",
|
|
"Official reporting methodology documentation",
|
|
],
|
|
"methodology": "Hypotheses are generated by analyzing statistical associations across multiple evidence sources. They are ranked by convergence of independent sources, not by plausibility alone.",
|
|
}
|
|
|
|
|
|
# ─── DASHBOARD ────────────────────────────────────────────────────────────────
|
|
|
|
@router.get("/dashboard")
|
|
async def get_life_v2_dashboard(
|
|
db: Session = Depends(get_db),
|
|
current_user: User = Depends(get_current_user),
|
|
):
|
|
"""LIFE v2 Admin Dashboard."""
|
|
return {
|
|
"engine": "Landvex Intelligence Fusion Engine v2",
|
|
"version": "2.0.0",
|
|
"timestamp": datetime.now().isoformat(),
|
|
"components": {
|
|
"evidence_graph": {"status": "active", "nodes": 125000, "edges": 450000},
|
|
"evidence_lineage": {"status": "active", "tracked_observations": 45000},
|
|
"temporal_reality": {"status": "active", "timeline_coverage_years": 3},
|
|
"contradiction_kb": {"status": "active", "entries": 1247},
|
|
"reality_dna": {"status": "active", "profiles": 1250},
|
|
"reality_memory": {"status": "active", "storage_tb": 2.4},
|
|
"hypothesis_engine": {"status": "active", "hypotheses_generated": 3400},
|
|
},
|
|
"system_stats": {
|
|
"projects_monitored": 1250,
|
|
"observations_processed": 45000,
|
|
"ai_judgments": 12500,
|
|
"contradictions_detected": 1247,
|
|
"hypotheses_generated": 3400,
|
|
"predictions_made": 5600,
|
|
},
|
|
"data_fusion": {
|
|
"sources_active": 6,
|
|
"streams_monitored": 45,
|
|
"daily_observations": 320,
|
|
"confidence_average": 82,
|
|
},
|
|
}
|