97 lines
3.0 KiB
Python
97 lines
3.0 KiB
Python
from typing import Union, Optional, List
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from pydantic import BaseModel
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from fastapi import FastAPI, Request, Depends
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from CommonCode.settings import get_logger
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import logging
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from fastapi.responses import StreamingResponse
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import os
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import sys
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import json
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import time
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from util import embed_scores as ES
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from fastapi_server_session import SessionManager, RedisSessionInterface, Session
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import redis
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from datetime import timedelta
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app = FastAPI()
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session_manager = SessionManager(
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interface=RedisSessionInterface(redis.from_url("redis://localhost"))
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)
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logger = get_logger(__name__,'/var/log/vector_search_logs/main_embed_scores', stdout=True, systemd=False, level = logging.INFO)
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r = redis.Redis(host='localhost', port=6379, db=15)
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class VideosPostRequest(BaseModel):
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query: str = "A cat and a human",
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threshold: float = 0.10,
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c_dirs: Optional[List[str]] = None,
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task_id: str = 'compute_log'
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@app.post("/videos.json")
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async def videos_json(
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vpr: VideosPostRequest,
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session: Session = Depends(session_manager.use_session),
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):
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query = vpr.query
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threshold = vpr.threshold
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c_dirs = vpr.c_dirs
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task_id = vpr.task_id
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if c_dirs is None:
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c_dirs = [
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# "/mnt/hdd_24tb_1/videos/ftp/leopards2/2025/08/26",
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# "/srv/ftp_tcc/leopards1/2025/08/27",
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# "/srv/ftp_tcc/leopards1/2025/08/28",
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# "/srv/ftp_tcc/leopards1/2025/08/29",
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# "/srv/ftp_tcc/leopards1/2025/08/30",
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# "/srv/ftp_tcc/leopards1/2025/08/31",
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# "/srv/ftp_tcc/leopards1/2025/09/01",
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# "/srv/ftp_tcc/leopards1/2025/09/02",
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# "/srv/ftp_tcc/leopards1/2025/09/03",
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# "/srv/ftp_tcc/leopards1/2025/09/04",
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# "/srv/ftp_tcc/leopards1/2025/09/05",
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# "/srv/ftp_tcc/leopards1/2025/09/06",
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# "/srv/ftp_tcc/leopards1/2025/09/07",
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"/srv/ftp_tcc/leopards1/2025/09/08",
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"/srv/ftp_tcc/leopards1/2025/09/09",
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"/srv/ftp_tcc/leopards1/2025/09/10",
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"/srv/ftp_tcc/leopards1/2025/09/11",
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]
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print(','.join([str(x) for x in c_dirs]))
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message = {'task':'SCHEDULED','when':[str(x) for x in c_dirs], 'time':time.time()}
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r.rpush(task_id, json.dumps(message))
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for x in c_dirs:
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message = {'task':'QUEUEING', 'when': str(x), 'time': time.time()}
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r.rpush(task_id, json.dumps(message))
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folder_scores = ES.calculate_embedding_score_in_folders(
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tuple(c_dirs), threshold=threshold, query=query, redis_key = task_id)
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# if p_hits != ES.calculate_embedding_score_in_folders.cache_info().hits:
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# logger.info("FROM CACHE")
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# else:pp
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# logger.info("COMPUTED FROM SCRATCH")
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folder_scores["breaks"] = ES.add_breaks_between_videos(folder_scores)
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folder_scores['videos'] = ES.collapse_scores_to_maxmin_avg(folder_scores)
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session["folder_scores"] = folder_scores
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return folder_scores
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class ClickEvent(BaseModel):
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timestamp: float
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class ClickResponse(BaseModel):
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path: str
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timeoffset: float
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