import gradio as gr
from vector_db.vector_db_client import VectorDB
from PIL import Image
from transformers import AutoProcessor, CLIPModel
import os
import uuid
from tcvectordb.model.document import SearchParams
import traceback

LOCAL_MODEL_PATH = "download_model.local_model_path"
MODEL_NAME = "download_model.model_name"
LOCAL_GRAPH_PATH = "graph_upload.local_graph_path"

class ChatSearch:
    def __init__(self, config, vdb: VectorDB):
        self.vdb = vdb
        self.model_name = config.get(MODEL_NAME)
        self.local_model_path = config.get(LOCAL_MODEL_PATH)
        self.local_graph_path = config.get(LOCAL_GRAPH_PATH)
        self.model_cache_directory = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), self.local_model_path, self.model_name)
        self.graph_cache_directory = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), self.local_graph_path)

    def initial_model(self):
        model = CLIPModel.from_pretrained(self.model_cache_directory)
        processor = AutoProcessor.from_pretrained(self.model_cache_directory)
        return model, processor
    
    def search_result(self, image):
        if image is None:
            return "请先上传图片..."
        
        if not os.path.exists(self.model_cache_directory):
            return f"缓存目录 {self.model_cache_directory} 不存在，无法初始化模型。"
        
        model, processor = self.initial_model()
        try:
            # 生成唯一的文件名
            unique_filename = f"{uuid.uuid4().hex}.png"
            image_path = os.path.join(self.graph_cache_directory, unique_filename)
            
            # 保存图片到指定文件夹
            image.save(image_path)
            
            image_vector = self._process_image(image_path, model, processor).squeeze().tolist()  # 转换为一维列表
            
            # 假设你的 VectorDB 支持图片搜索
            collection = self.vdb.get_collection()
            res = collection.search(
                vectors=[image_vector],
                params=SearchParams(ef=200),
                limit=10,
                output_fields=['local_graph_path']
            )

            results = []
            for i, docs in enumerate(res):
                for doc in docs:
                    image_path = doc['local_graph_path']
                    try:
                        image = Image.open(image_path)
                        results.append(image)
                    except Exception as e:
                        print(f"无法加载图片 {image_path}: {e}")
            return results
        except Exception as e:
            print(f"问题：{e}\n")
            error_trace = traceback.format_exc()
            print(error_trace)
    
    def _process_image(self, image_path, emb_model, processor):
        """
        处理单个图片文件，将其转换为向量。

        参数:
        image_path (str): 图片文件的路径。

        返回:
        torch.Tensor: 图片的向量表示。
        """
        image = Image.open(image_path)
        inputs = processor(images=image, return_tensors="pt")
        image_features = emb_model.get_image_features(**inputs)
        return image_features

    def get_chart(self):
        return gr.Interface(
            fn=self.search_result,
            inputs=gr.Image(type="pil", label="上传图片"),
            outputs=gr.Gallery(label="检索结果"),
            theme="soft",
            description="上传图片进行检索",
            allow_flagging="never"
        )