import chromadb
import hashlib

import pandas as pd
from chromadb.config import Settings
from chromadb.utils.embedding_functions.ollama_embedding_function import OllamaEmbeddingFunction
from tqdm import tqdm

# 读取 csv 文件
csv_file_path = r'D:/Files/csv/output/output.csv'
image_path = r'D:/images-clean'

df = pd.read_csv(csv_file_path, encoding='Windows-1252')

# 创建 Chroma 客户端
client = chromadb.Client(Settings(is_persistent=True, persist_directory="/vector_db"))
# client = chromadb.Client(Settings(is_persistent=True, persist_directory="./service/search_image_with_text/vector_db"))
# client = chromadb.Client(Settings(is_persistent=True, persist_directory="D:/workspace/AiDLab/vector_db"))
# 创建集合
# embedding_fn = OllamaEmbeddingFunction(url="http://localhost:11434/api/embeddings", model_name="mxbai-embed-large")
embedding_fn = OllamaEmbeddingFunction(url="http://10.1.1.240:11434/api/embeddings", model_name="mxbai-embed-large")


def create_collection():
    collection = client.get_or_create_collection("sub_sketches_description", embedding_function=embedding_fn)

    # 存储数据，包括自定义属性
    images_description = []
    images_metadata = []
    ids = []
    batch_size = 41666  # 最大批量大小
    for index, row in tqdm(df.iterrows()):
        # 将图片的md5作为id
        with open(image_path + row['path'], 'rb') as f:
            image_data = f.read()
            md5_value = hashlib.md5(image_data).hexdigest()
            ids.append(md5_value)
        images_description.append(row['description'])
        images_metadata.append({
            "gender": row['gender'],
            "path": row['path']
        })

        # 将数据添加到集合
        # 每达到 batch_size 就执行一次 upsert
        if len(ids) >= batch_size:
            collection.upsert(
                ids=list(ids),
                documents=images_description,
                metadatas=images_metadata  # 添加自定义属性
            )
            # 清空列表以准备下一批数据
            ids.clear()
            images_description.clear()
            images_metadata.clear()

    if ids:
        collection.upsert(
            ids=list(ids),
            documents=images_description,
            metadatas=images_metadata  # 添加自定义属性
        )

    print("Data successfully stored in the vector database.")


def query(gender, content):
    collection = client.get_collection("sub_sketches_description", embedding_function=embedding_fn)
    # 6. 查询相似内容
    user_gender = gender  # 用户输入的性别
    user_content = content  # 用户输入的内容

    results = collection.query(
        query_texts=user_content,
        n_results=5,  # 返回前 5 个结果
        where={"gender": user_gender}  # 根据性别过滤
    )

    # 输出结果
    resp = []
    for document, result in zip(results['documents'][0], results['metadatas'][0]):
        # print("Path:", result['path'])
        # print("Content:", document)
        resp.append(result['path'])
    return resp


if __name__ == '__main__':
    # create_collection()
    query("female", "I need a long sleeve dress")