Import Vector lab project
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"""
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Analysis API endpoints for full case analysis pipeline.
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Pipeline: distance → geo → scoring → psychotype → claude → merged result
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"""
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from fastapi import APIRouter, Depends, HTTPException
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from sqlalchemy.orm import Session
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from pydantic import BaseModel, Field
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from typing import List, Optional, Dict, Any
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from uuid import UUID
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from datetime import datetime
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import time
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from database import get_db
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from models import Case, AnalysisLog, User
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from api.v1.auth import get_current_user
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# Import services
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from services.distance_service import calculate_max_distance
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from services.geo_service import build_search_zones
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from services.scoring_service import WeightedScorer
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from services.psychotype_service import (
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detect_psychotype,
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get_psychotype_modifiers,
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get_search_recommendations
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)
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from services.claude_service import analyze_case as claude_analyze
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router = APIRouter()
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class AnalysisRequest(BaseModel):
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"""Request for full case analysis"""
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case_id: UUID = Field(..., description="ID случая для анализа")
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class ZoneResult(BaseModel):
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"""Search zone with score and recommendations"""
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priority: int
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name: str
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direction: str
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distance_km: float
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score: float
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reasoning: str
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forest_pct: Optional[float] = None
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road_density: Optional[float] = None
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water_distance_km: Optional[float] = None
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class AnalysisResponse(BaseModel):
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"""Full analysis result"""
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case_id: UUID
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analyzed_at: datetime
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# Distance calculation
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max_distance_km: float
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# Psychotype (if available)
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psychotype: Optional[str] = None
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psychotype_modifiers: Optional[Dict[str, Any]] = None
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psychotype_recommendations: Optional[Dict[str, Any]] = None
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# Zones
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zones: List[ZoneResult]
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# Claude analysis (if available)
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urgency: Optional[str] = None
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key_locations: Optional[List[str]] = None
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immediate_actions: Optional[List[str]] = None
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behavioral_prediction: Optional[str] = None
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summary: Optional[str] = None
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# Meta
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execution_time_ms: float
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services_used: List[str]
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class SavedAnalysisResponse(BaseModel):
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"""Saved analysis result from database"""
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case_id: UUID
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analysis_log: Dict[str, Any]
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created_at: datetime
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@router.post("/analyze", response_model=AnalysisResponse, status_code=200)
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async def analyze_full_case(
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request: AnalysisRequest,
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db: Session = Depends(get_db),
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current_user: User = Depends(get_current_user)
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):
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"""
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Запустить полный анализ случая.
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Пайплайн:
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1. Distance service - расчет максимальной дистанции
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2. Geo service - построение зон поиска
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3. Scoring service - оценка и ранжирование зон
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4. Psychotype service - определение психотипа (если есть данные)
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5. Claude service - интеллектуальный анализ (опционально)
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6. Merge results - объединение результатов
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Требуется аутентификация (operator, field, admin).
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"""
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start_time = time.time()
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services_used = []
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# 1. Получить случай из БД
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case = db.query(Case).filter(Case.id == request.case_id).first()
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if not case:
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raise HTTPException(status_code=404, detail=f"Case {request.case_id} not found")
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# Подготовить данные для анализа
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case_data = {
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'age': case.age_years,
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'gender': case.gender,
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'elapsed_hours': case.elapsed_hours or 1.0,
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'terrain_primary': case.terrain[0] if case.terrain else 'лес',
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'season': case.season or 'лето',
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'temperature_c': case.temperature_c or 20.0,
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'has_transport': case.has_transport,
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'has_diagnosis': case.has_diagnosis,
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'diagnosis_type': case.diagnosis_type or [],
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'tnp_lat': case.tnp_lat,
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'tnp_lon': case.tnp_lon,
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}
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# 2. Distance service - расчет максимальной дистанции
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try:
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max_distance = calculate_max_distance(case_data)
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services_used.append('distance')
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except Exception as e:
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raise HTTPException(status_code=500, detail=f"Distance calculation failed: {str(e)}")
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# 3. Geo service - построение зон поиска (если есть координаты)
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zones_data = []
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if case.tnp_lat and case.tnp_lon:
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try:
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zones_data = await build_search_zones(
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lat=case.tnp_lat,
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lon=case.tnp_lon,
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case_data=case_data,
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max_distance_km=max_distance
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)
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services_used.append('geo')
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except Exception as e:
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# Geo service опционален, продолжаем без него
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print(f"Geo service failed: {e}")
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# 4. Scoring service - оценка и ранжирование зон
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scored_zones = []
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if zones_data:
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try:
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scorer = WeightedScorer()
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for zone in zones_data:
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zone_dict = zone.model_dump() if hasattr(zone, 'model_dump') else zone
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score = scorer.score_zone(zone_dict, case_data, max_distance_km=max_distance)
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zone_dict['score'] = score
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zone_dict['reasoning'] = f"Оценка на основе {len(case_data)} факторов"
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scored_zones.append(zone_dict)
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# Сортировать по score
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scored_zones.sort(key=lambda z: z.get('score', 0), reverse=True)
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services_used.append('scoring')
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except Exception as e:
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print(f"Scoring service failed: {e}")
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scored_zones = _create_fallback_zones(max_distance)
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else:
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# Если geo не работает, создаем базовые зоны
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scored_zones = _create_fallback_zones(max_distance)
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# 5. Psychotype service - определение психотипа
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psychotype = None
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psychotype_modifiers = None
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psychotype_recommendations = None
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if case.psychotype_answers:
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try:
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psychotype = detect_psychotype(case.psychotype_answers)
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psychotype_modifiers = get_psychotype_modifiers(psychotype)
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psychotype_recommendations = get_search_recommendations(psychotype)
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services_used.append('psychotype')
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# Применить модификаторы психотипа к зонам
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scored_zones = _apply_psychotype_modifiers(scored_zones, psychotype_modifiers)
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except Exception as e:
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print(f"Psychotype service failed: {e}")
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# 6. Claude service - интеллектуальный анализ (опционально)
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urgency = None
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key_locations = None
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immediate_actions = None
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behavioral_prediction = None
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summary = None
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try:
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claude_result = await claude_analyze(case_data)
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urgency = claude_result.urgency
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key_locations = claude_result.key_locations
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immediate_actions = claude_result.immediate_actions
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behavioral_prediction = claude_result.behavioral_prediction
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summary = claude_result.summary
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services_used.append('claude')
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except Exception as e:
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# Claude опционален, продолжаем без него
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print(f"Claude service failed: {e}")
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# 7. Формируем результат
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zones_result = [
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ZoneResult(
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priority=i + 1,
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name=zone.get('name', f"Зона {zone.get('direction', 'N')}"),
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direction=zone.get('direction', 'N'),
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distance_km=zone.get('distance_km', 0),
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score=zone.get('score', 0),
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reasoning=zone.get('reasoning', 'Автоматическая оценка'),
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forest_pct=zone.get('forest_pct'),
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road_density=zone.get('road_density'),
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water_distance_km=zone.get('water_distance_km')
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)
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for i, zone in enumerate(scored_zones[:10]) # Топ-10 зон
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]
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execution_time = (time.time() - start_time) * 1000
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# 8. Сохранить результат в analysis_log
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analysis_result = {
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'max_distance_km': max_distance,
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'psychotype': psychotype,
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'psychotype_modifiers': psychotype_modifiers,
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'zones': [z.model_dump() for z in zones_result],
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'urgency': urgency,
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'key_locations': key_locations,
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'immediate_actions': immediate_actions,
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'summary': summary,
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'services_used': services_used,
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'execution_time_ms': execution_time
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}
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# Обновить case.analysis_log
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case.analysis_log = analysis_result
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db.commit()
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# Создать запись в AnalysisLog
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log_entry = AnalysisLog(
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case_id=case.id,
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user_id=current_user.id,
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analysis_type='full_pipeline',
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input_data={'case_id': str(case.id)},
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output_data=analysis_result,
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execution_time=execution_time / 1000,
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status='success'
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)
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db.add(log_entry)
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db.commit()
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return AnalysisResponse(
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case_id=case.id,
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analyzed_at=datetime.utcnow(),
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max_distance_km=max_distance,
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psychotype=psychotype,
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psychotype_modifiers=psychotype_modifiers,
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psychotype_recommendations=psychotype_recommendations,
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zones=zones_result,
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urgency=urgency,
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key_locations=key_locations,
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immediate_actions=immediate_actions,
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behavioral_prediction=behavioral_prediction,
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summary=summary,
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execution_time_ms=execution_time,
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services_used=services_used
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)
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@router.get("/analyze/{case_id}", response_model=SavedAnalysisResponse)
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async def get_saved_analysis(
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case_id: UUID,
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db: Session = Depends(get_db),
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current_user: User = Depends(get_current_user)
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):
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"""
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Получить сохранённый результат анализа.
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Возвращает последний analysis_log из таблицы cases.
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Требуется аутентификация (operator, field, admin).
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"""
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case = db.query(Case).filter(Case.id == case_id).first()
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if not case:
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raise HTTPException(status_code=404, detail=f"Case {case_id} not found")
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if not case.analysis_log:
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raise HTTPException(
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status_code=404,
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detail=f"No analysis found for case {case_id}. Run POST /analyze first."
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)
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return SavedAnalysisResponse(
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case_id=case.id,
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analysis_log=case.analysis_log,
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created_at=case.created_at
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)
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def _create_fallback_zones(max_distance: float) -> List[Dict[str, Any]]:
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"""Create basic zones when geo/scoring services fail"""
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directions = ['N', 'NE', 'E', 'SE', 'S', 'SW', 'W', 'NW']
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zones = []
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for i, direction in enumerate(directions):
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zones.append({
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'direction': direction,
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'distance_km': max_distance * 0.8,
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'score': 100 - (i * 10),
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'name': f"Сектор {direction}",
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'reasoning': 'Базовая оценка (сервисы недоступны)'
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})
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return zones
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def _apply_psychotype_modifiers(zones: List[Dict], modifiers: Dict) -> List[Dict]:
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"""Apply psychotype modifiers to zone scores"""
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if not modifiers:
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return zones
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# Применяем модификаторы зон из психотипа
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for zone in zones:
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distance = zone.get('distance_km', 0)
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# Определяем зону дистанции
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if distance < 0.5:
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modifier = modifiers.get('zone_0_500', 1.0)
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elif distance < 1.5:
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modifier = modifiers.get('zone_500_1500', 1.0)
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elif distance < 2.5:
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modifier = modifiers.get('zone_1500_2500', 1.0)
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else:
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modifier = modifiers.get('zone_2500plus', 1.0)
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# Применяем модификатор к score
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zone['score'] = zone.get('score', 0) * modifier
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zone['reasoning'] += f" (психотип: ×{modifier:.1f})"
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# Пересортировать по score
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zones.sort(key=lambda z: z.get('score', 0), reverse=True)
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return zones
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