import glob
from pymilvus import MilvusClient
from pymilvus.client.types import LoadState

import argparse
import os
import json
from CommonCode.util import exit_if_not_ipython, get_cset_for_file_matching, is_ipython
from CommonCode import kwq
from CommonCode.settings import get_logger, LogColorize
from kafka import TopicPartition
from kafka.structs import OffsetAndMetadata
from pprint import pprint
import numpy as np
pfm = LogColorize.embeds_in_db
logger = get_logger(__name__,'/var/log/ml_vision_logs/03_put_into_vectordb', stdout=True, systemd=False)

input_topic = kwq.TOPICS.videos_with_nuggets

client_id='embedding_place_in_db_1'
group_id = client_id

consumer = kwq.create_consumer(input_topic, group_id = group_id, client_id = client_id)
c_part = TopicPartition(input_topic, 0)
consumer.assign([c_part])


producer = kwq.producer
model_type = 'ViT-L-16-SigLIP2-512'

def get_db_embed_done_path(vpath):
    return os.path.splitext(vpath)[0]+'.oclip.orin.indb'


#video_file_to_upload='/srv/ftp/ptz/2025/04/14/PTZBackRight_00_20250414063817.mp4'


def get_vec_path(vpath):
    return os.path.splitext(vpath)[0]+'.oclip.orin'

def get_date(vpath):
    split_entries = os.path.splitext(vpath)[0].split('/')
    return ''.join(split_entries[-4:-1])

def get_camera_name(vpath):
    split_entries = os.path.splitext(vpath)[0].split('/')
    return split_entries[split_entries.index('ftp')+1]

def upload_vector_file(video_file_to_upload):
    client = MilvusClient(
        uri="http://localhost:19530"
    )

    db_done_path = get_db_embed_done_path(video_file_to_upload)
    if os.path.exists(db_done_path):
        print('Already exists in DB, skipping upload')
#        return

    video_file_to_upload = get_vec_path(video_file_to_upload)

    with open(video_file_to_upload,'r') as jj:
        vecs = json.load(jj)


    embeds = [x['score'] for x in vecs['scores']]
    fr_nums =  [x['frame'] for x in vecs['scores']]

    fname_root = video_file_to_upload.rsplit('/',1)[-1].split('.')[0]
    fc = fname_root.split('_')[-1]

#    data = list()
    filepath = video_file_to_upload.replace('/srv/ftp/','').replace('/mergedfs/ftp','').split('.')[-0]

    data_v2 = list()
    date = get_date(filepath)
    for embed, frame_num in zip(embeds, fr_nums):
        fg = '{0:05g}'.format(frame_num)
        id_num = int(fc+fg)
        embeds_as_np = np.asarray(embed, dtype=np.float16)
        to_put_2 = dict(primary_id= id_num, filepath=filepath, frame_number = int(frame_num), so400m=embeds_as_np, date=str(date))
        data_v2.append(to_put_2)

    cam_name = get_camera_name(video_file_to_upload)
    client.insert(collection_name = f'nuggets_{cam_name}_so400m_siglip2', data=data_v2)
    client.close()

    
    with open(db_done_path,'w') as ff:
        ff.write("")
        
    print(f'Inserting into DB, {video_file_to_upload}')



for msg in consumer:
    key = msg.key
    value = msg.value
    file_path = value['filepath']
    success = False
    try:
        upload_vector_file(value['filepath'])
        success = True
        logger.info(f"SUCCESS_UPLOADING :{pfm(file_path)}")
    except Exception as e:
        logger.info(f"ERROR_UPLOADING :{pfm(file_path)} + {e}")

    d_send = {'value':msg.value, 'key':msg.key}

    if success:
        send_topic = kwq.TOPICS.videos_embedding_in_db
    else:
        send_topic = kwq.TOPICS.videos_embedding_in_db_fail
        

    producer.send(send_topic, **d_send)