import logging
from PIL import Image
import requests
from transformers import AutoProcessor, CLIPVisionModel
import numpy as np
import torch
from openai import OpenAI
from functools import lru_cache

from utils.utils import pil_image_to_data_url

class ImageSearcher:
    def __init__(self, image_collection, cars_collection, search_index):
        self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
        self.model = CLIPVisionModel.from_pretrained("openai/clip-vit-large-patch14").to(self.device)
        self.processor = AutoProcessor.from_pretrained("openai/clip-vit-large-patch14")
        self.client = OpenAI()
        self.cars_collection = cars_collection
        self.image_collection = image_collection
        self.search_index = search_index
        self.car_list = self.cars_collection.distinct("makeModel")
        self.logger = logging.getLogger(__name__)

    def compute_image_embeddings(self, image):
        try:
            inputs = self.processor(images=image, return_tensors="pt").to(self.device)
            with torch.no_grad():
                outputs = self.model(**inputs)
            pooled_output = outputs.pooler_output 
            pooled_output_np = pooled_output.cpu().numpy().reshape(-1).tolist()
            return pooled_output_np
        except Exception as e:
            self.logger.error(f"Error processing image: {str(e)}")
            return None

    def classify_car(self, image):

        image_url = pil_image_to_data_url(image)

        prompt = f"Classify the car into one of the following categories: {self.car_list}. If the car is not in the list, return 'unknown'. Return the label only."
        try:
            response = self.client.chat.completions.create(
                model="gpt-4o-mini",
                messages=[
                    {
                        "role": "user",
                        "content": [
                            {"type": "text", "text": prompt},
                            {"type": "image_url", "image_url": {"url": image_url}},
                        ],
                    }
                ],
                max_tokens=300,
            )
            self.logger.info(f"Classified car: {response.choices[0].message.content}")
            return response.choices[0].message.content
        except Exception as e:
            self.logger.error(f"Error classifying car {image_url}: {str(e)}")
            return "unknown"

    def __call__(self, image, k=3):

        image = image.convert("RGB")

        img_embedding = self.compute_image_embeddings(image)
        if img_embedding is None:
            self.logger.warning(f"Invalid image")
            return {"error": "Invalid image provided"}

        detected_car = self.classify_car(image)

        pipeline = [
            {
                '$vectorSearch': {
                    'index': self.search_index,
                    'filter': {'makeModel': detected_car} if detected_car != 'unknown' else {},
                    'path': 'embedding', 
                    'queryVector': img_embedding,
                    'limit': k,
                    'numCandidates': 100,
                }
            },
            {
                '$lookup': {
                    'from': self.cars_collection.name,
                    'localField': 'id',
                    'foreignField': '_id',
                    'as': 'inventoryDetails'
                }
            },
            {'$unwind': '$inventoryDetails'},
            {'$replaceRoot': {'newRoot': '$inventoryDetails'}},
            {
                '$project': {
                    'description': 0,         # Exclude 'description'
                    'variants': 0,            # Exclude 'variants'
                    'review_embedding': 0,     # Exclude 'review_embedding'
                    'feature_embedding':0
                }
            }
        ]

        try:
            result = list(self.image_collection.aggregate(pipeline))
            return {"detected_car": detected_car, "results": result}
        except Exception as e:
            self.logger.error(f"Error searching for similar cars: {str(e)}")
            return {"detected_car": detected_car, "error": "An error occurred during the search"}