mirror of
https://github.com/blakeblackshear/frigate.git
synced 2024-12-23 19:11:14 +01:00
fa81d87dc0
* Updated documentation for the review endpoint * Updated documentation for the review/summary endpoint * Updated documentation for the review/summary endpoint * Documentation for the review activity audio and motion endpoints * Added responses for more review.py endpoints * Added responses for more review.py endpoints * Fixed review.py responses and proper path parameter names * Added body model for /reviews/viewed and /reviews/delete * Updated OpenAPI specification for the review controller endpoints * Run ruff format frigate * Drop significant_motion * Updated frigate-api.yaml * Deleted total_motion * Combine 2 models into generic
496 lines
15 KiB
Python
496 lines
15 KiB
Python
"""Review apis."""
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import datetime
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import logging
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from functools import reduce
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from pathlib import Path
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import pandas as pd
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from fastapi import APIRouter
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from fastapi.params import Depends
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from fastapi.responses import JSONResponse
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from peewee import Case, DoesNotExist, fn, operator
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from playhouse.shortcuts import model_to_dict
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from frigate.api.defs.generic_response import GenericResponse
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from frigate.api.defs.review_body import ReviewModifyMultipleBody
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from frigate.api.defs.review_query_parameters import (
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ReviewActivityMotionQueryParams,
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ReviewQueryParams,
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ReviewSummaryQueryParams,
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)
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from frigate.api.defs.review_responses import (
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ReviewActivityMotionResponse,
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ReviewSegmentResponse,
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ReviewSummaryResponse,
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)
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from frigate.api.defs.tags import Tags
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from frigate.models import Recordings, ReviewSegment
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from frigate.util.builtin import get_tz_modifiers
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logger = logging.getLogger(__name__)
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router = APIRouter(tags=[Tags.review])
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@router.get("/review", response_model=list[ReviewSegmentResponse])
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def review(params: ReviewQueryParams = Depends()):
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cameras = params.cameras
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labels = params.labels
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zones = params.zones
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reviewed = params.reviewed
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limit = params.limit
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severity = params.severity
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before = params.before or datetime.datetime.now().timestamp()
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after = (
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params.after
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or (datetime.datetime.now() - datetime.timedelta(hours=24)).timestamp()
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)
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clauses = [
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(
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(ReviewSegment.start_time > after)
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& (
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(ReviewSegment.end_time.is_null(True))
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| (ReviewSegment.end_time < before)
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)
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)
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]
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if cameras != "all":
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camera_list = cameras.split(",")
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clauses.append((ReviewSegment.camera << camera_list))
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if labels != "all":
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# use matching so segments with multiple labels
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# still match on a search where any label matches
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label_clauses = []
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filtered_labels = labels.split(",")
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for label in filtered_labels:
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label_clauses.append(
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(ReviewSegment.data["objects"].cast("text") % f'*"{label}"*')
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| (ReviewSegment.data["audio"].cast("text") % f'*"{label}"*')
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)
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label_clause = reduce(operator.or_, label_clauses)
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clauses.append((label_clause))
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if zones != "all":
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# use matching so segments with multiple zones
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# still match on a search where any zone matches
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zone_clauses = []
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filtered_zones = zones.split(",")
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for zone in filtered_zones:
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zone_clauses.append(
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(ReviewSegment.data["zones"].cast("text") % f'*"{zone}"*')
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)
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zone_clause = reduce(operator.or_, zone_clauses)
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clauses.append((zone_clause))
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if reviewed == 0:
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clauses.append((ReviewSegment.has_been_reviewed == False))
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if severity:
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clauses.append((ReviewSegment.severity == severity))
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review = (
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ReviewSegment.select()
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.where(reduce(operator.and_, clauses))
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.order_by(ReviewSegment.severity.asc())
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.order_by(ReviewSegment.start_time.desc())
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.limit(limit)
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.dicts()
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.iterator()
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)
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return JSONResponse(content=[r for r in review])
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@router.get("/review/summary", response_model=ReviewSummaryResponse)
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def review_summary(params: ReviewSummaryQueryParams = Depends()):
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hour_modifier, minute_modifier, seconds_offset = get_tz_modifiers(params.timezone)
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day_ago = (datetime.datetime.now() - datetime.timedelta(hours=24)).timestamp()
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month_ago = (datetime.datetime.now() - datetime.timedelta(days=30)).timestamp()
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cameras = params.cameras
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labels = params.labels
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zones = params.zones
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clauses = [(ReviewSegment.start_time > day_ago)]
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if cameras != "all":
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camera_list = cameras.split(",")
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clauses.append((ReviewSegment.camera << camera_list))
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if labels != "all":
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# use matching so segments with multiple labels
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# still match on a search where any label matches
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label_clauses = []
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filtered_labels = labels.split(",")
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for label in filtered_labels:
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label_clauses.append(
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(ReviewSegment.data["objects"].cast("text") % f'*"{label}"*')
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| (ReviewSegment.data["audio"].cast("text") % f'*"{label}"*')
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)
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label_clause = reduce(operator.or_, label_clauses)
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clauses.append((label_clause))
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if zones != "all":
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# use matching so segments with multiple zones
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# still match on a search where any zone matches
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zone_clauses = []
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filtered_zones = zones.split(",")
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for zone in filtered_zones:
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zone_clauses.append(
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(ReviewSegment.data["zones"].cast("text") % f'*"{zone}"*')
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)
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zone_clause = reduce(operator.or_, zone_clauses)
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clauses.append((zone_clause))
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last_24 = (
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ReviewSegment.select(
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fn.SUM(
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Case(
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None,
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[
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(
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(ReviewSegment.severity == "alert"),
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ReviewSegment.has_been_reviewed,
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)
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],
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0,
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)
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).alias("reviewed_alert"),
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fn.SUM(
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Case(
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None,
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[
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(
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(ReviewSegment.severity == "detection"),
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ReviewSegment.has_been_reviewed,
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)
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],
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0,
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)
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).alias("reviewed_detection"),
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fn.SUM(
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Case(
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None,
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[
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(
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(ReviewSegment.severity == "alert"),
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1,
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)
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],
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0,
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)
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).alias("total_alert"),
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fn.SUM(
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Case(
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None,
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[
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(
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(ReviewSegment.severity == "detection"),
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1,
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)
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],
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0,
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)
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).alias("total_detection"),
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)
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.where(reduce(operator.and_, clauses))
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.dicts()
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.get()
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)
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clauses = [(ReviewSegment.start_time > month_ago)]
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if cameras != "all":
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camera_list = cameras.split(",")
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clauses.append((ReviewSegment.camera << camera_list))
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if labels != "all":
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# use matching so segments with multiple labels
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# still match on a search where any label matches
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label_clauses = []
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filtered_labels = labels.split(",")
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for label in filtered_labels:
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label_clauses.append(
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(ReviewSegment.data["objects"].cast("text") % f'*"{label}"*')
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)
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label_clause = reduce(operator.or_, label_clauses)
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clauses.append((label_clause))
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last_month = (
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ReviewSegment.select(
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fn.strftime(
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"%Y-%m-%d",
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fn.datetime(
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ReviewSegment.start_time,
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"unixepoch",
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hour_modifier,
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minute_modifier,
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),
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).alias("day"),
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fn.SUM(
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Case(
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None,
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[
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(
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(ReviewSegment.severity == "alert"),
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ReviewSegment.has_been_reviewed,
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)
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],
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0,
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)
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).alias("reviewed_alert"),
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fn.SUM(
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Case(
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None,
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[
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(
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(ReviewSegment.severity == "detection"),
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ReviewSegment.has_been_reviewed,
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)
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],
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0,
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)
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).alias("reviewed_detection"),
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fn.SUM(
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Case(
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None,
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[
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(
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(ReviewSegment.severity == "alert"),
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1,
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)
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],
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0,
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)
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).alias("total_alert"),
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fn.SUM(
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Case(
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None,
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[
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(
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(ReviewSegment.severity == "detection"),
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1,
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)
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],
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0,
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)
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).alias("total_detection"),
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)
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.where(reduce(operator.and_, clauses))
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.group_by(
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(ReviewSegment.start_time + seconds_offset).cast("int") / (3600 * 24),
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)
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.order_by(ReviewSegment.start_time.desc())
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)
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data = {
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"last24Hours": last_24,
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}
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for e in last_month.dicts().iterator():
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data[e["day"]] = e
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return JSONResponse(content=data)
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@router.post("/reviews/viewed", response_model=GenericResponse)
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def set_multiple_reviewed(body: ReviewModifyMultipleBody):
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ReviewSegment.update(has_been_reviewed=True).where(
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ReviewSegment.id << body.ids
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).execute()
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return JSONResponse(
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content=({"success": True, "message": "Reviewed multiple items"}),
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status_code=200,
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)
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@router.post("/reviews/delete", response_model=GenericResponse)
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def delete_reviews(body: ReviewModifyMultipleBody):
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list_of_ids = body.ids
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reviews = (
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ReviewSegment.select(
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ReviewSegment.camera,
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ReviewSegment.start_time,
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ReviewSegment.end_time,
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)
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.where(ReviewSegment.id << list_of_ids)
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.dicts()
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.iterator()
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)
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recording_ids = []
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for review in reviews:
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start_time = review["start_time"]
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end_time = review["end_time"]
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camera_name = review["camera"]
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recordings = (
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Recordings.select(Recordings.id, Recordings.path)
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.where(
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Recordings.start_time.between(start_time, end_time)
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| Recordings.end_time.between(start_time, end_time)
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| (
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(start_time > Recordings.start_time)
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& (end_time < Recordings.end_time)
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)
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)
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.where(Recordings.camera == camera_name)
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.dicts()
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.iterator()
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)
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for recording in recordings:
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Path(recording["path"]).unlink(missing_ok=True)
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recording_ids.append(recording["id"])
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# delete recordings and review segments
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Recordings.delete().where(Recordings.id << recording_ids).execute()
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ReviewSegment.delete().where(ReviewSegment.id << list_of_ids).execute()
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return JSONResponse(
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content=({"success": True, "message": "Delete reviews"}), status_code=200
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)
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@router.get(
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"/review/activity/motion", response_model=list[ReviewActivityMotionResponse]
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)
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def motion_activity(params: ReviewActivityMotionQueryParams = Depends()):
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"""Get motion and audio activity."""
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cameras = params.cameras
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before = params.before or datetime.datetime.now().timestamp()
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after = (
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params.after
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or (datetime.datetime.now() - datetime.timedelta(hours=1)).timestamp()
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)
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# get scale in seconds
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scale = params.scale
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clauses = [(Recordings.start_time > after) & (Recordings.end_time < before)]
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clauses.append((Recordings.motion > 0))
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if cameras != "all":
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camera_list = cameras.split(",")
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clauses.append((Recordings.camera << camera_list))
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data: list[Recordings] = (
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Recordings.select(
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Recordings.camera,
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Recordings.start_time,
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Recordings.motion,
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)
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.where(reduce(operator.and_, clauses))
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.order_by(Recordings.start_time.asc())
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.dicts()
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.iterator()
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)
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# resample data using pandas to get activity on scaled basis
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df = pd.DataFrame(data, columns=["start_time", "motion", "camera"])
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if df.empty:
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logger.warning("No motion data found for the requested time range")
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return JSONResponse(content=[])
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df = df.astype(dtype={"motion": "float32"})
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# set date as datetime index
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df["start_time"] = pd.to_datetime(df["start_time"], unit="s")
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df.set_index(["start_time"], inplace=True)
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# normalize data
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motion = (
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df["motion"]
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.resample(f"{scale}s")
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.apply(lambda x: max(x, key=abs, default=0.0))
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.fillna(0.0)
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.to_frame()
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)
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cameras = df["camera"].resample(f"{scale}s").agg(lambda x: ",".join(set(x)))
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df = motion.join(cameras)
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length = df.shape[0]
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chunk = int(60 * (60 / scale))
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for i in range(0, length, chunk):
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part = df.iloc[i : i + chunk]
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min_val, max_val = part["motion"].min(), part["motion"].max()
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if min_val != max_val:
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df.iloc[i : i + chunk, 0] = (
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part["motion"].sub(min_val).div(max_val - min_val).mul(100).fillna(0)
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)
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else:
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df.iloc[i : i + chunk, 0] = 0.0
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# change types for output
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df.index = df.index.astype(int) // (10**9)
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normalized = df.reset_index().to_dict("records")
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return JSONResponse(content=normalized)
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@router.get("/review/event/{event_id}", response_model=ReviewSegmentResponse)
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def get_review_from_event(event_id: str):
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try:
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return JSONResponse(
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model_to_dict(
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ReviewSegment.get(
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ReviewSegment.data["detections"].cast("text") % f'*"{event_id}"*'
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)
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)
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)
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except DoesNotExist:
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return JSONResponse(
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content={"success": False, "message": "Review item not found"},
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status_code=404,
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)
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@router.get("/review/{review_id}", response_model=ReviewSegmentResponse)
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def get_review(review_id: str):
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try:
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return JSONResponse(
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content=model_to_dict(ReviewSegment.get(ReviewSegment.id == review_id))
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)
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except DoesNotExist:
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return JSONResponse(
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content={"success": False, "message": "Review item not found"},
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status_code=404,
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)
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@router.delete("/review/{review_id}/viewed", response_model=GenericResponse)
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def set_not_reviewed(review_id: str):
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try:
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review: ReviewSegment = ReviewSegment.get(ReviewSegment.id == review_id)
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except DoesNotExist:
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return JSONResponse(
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content=(
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{"success": False, "message": "Review " + review_id + " not found"}
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),
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status_code=404,
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)
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review.has_been_reviewed = False
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review.save()
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return JSONResponse(
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content=(
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{"success": True, "message": "Set Review " + review_id + " as not viewed"}
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),
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status_code=200,
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)
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