import clip
import faiss
from PIL import Image
from pypdf import PdfReader
import pandas as pd
import re
import os
import fitz
import torch
import numpy as np
from tqdm import tqdm
import base64


class RAG:
    def __init__(
            self,
            fais_index_path,
            clip_model="ViT-B/32",
            reranker=None,
            device="cpu",
            image_invoice_index_path=None,
            path_to_invoices=None,
            path_to_images=None
    ):
        self.index = faiss.read_index(fais_index_path)
        self.model, self.preprocess = clip.load(clip_model, device=device)
        self.device = device
        if image_invoice_index_path:
            self.image_invoice_index = pd.read_csv(image_invoice_index_path)
        self.path_to_invoices = path_to_invoices
        self.path_to_images = path_to_images
        self.reranker = reranker

    @staticmethod
    def image_to_base64(image_path):
        with open(image_path, "rb") as image_file:
            return base64.b64encode(image_file.read())

    def search_text(self, text, k=1):
        text_features = self.model.encode_text(clip.tokenize([text]).to(self.device))
        text_features /= text_features.norm(dim=-1, keepdim=True)
        text_features = text_features.detach().numpy()
        distances, indices = self.index.search(text_features, k)
        return distances, indices

    def search_image(self, image=None, image_path=None, k=1):
        if image is None and image_path is None:
            raise ValueError("Either image or image_path must be provided.")
        if image is None:
            image = Image.open(image_path)
        image_input = self.preprocess(image).unsqueeze(0).to(self.device)
        image_features = self.model.encode_image(image_input)
        image_features /= image_features.norm(dim=-1, keepdim=True)
        image_features = image_features.detach().numpy()
        distances, indices = self.index.search(image_features, k)
        return distances, indices

    def find_invoice(self, image=None, image_path=None, return_only_path=True, k=1, damage_description=None):
        if self.image_invoice_index is None:
            raise ValueError("No index for invoices found.")
        _, indices = self.search_image(image=image, image_path=image_path, k=k)
        img_ids = self.image_invoice_index.iloc[indices[0]]['img_id'].values
        invoices = self.image_invoice_index[self.image_invoice_index['img_id'].isin(img_ids)]['invoice'].values.tolist()
        images_paths = self.image_invoice_index[self.image_invoice_index['img_id'].isin(img_ids)]['image'].values.tolist()

        if self.reranker:
            if damage_description is None:
                raise ValueError("Damage description must be provided.")
            # images = [self.image_to_base64(f"{self.path_to_images}/{img_path}") for img_path in images_paths]

            images = [f"{self.path_to_images}/{img_path}" for img_path in images_paths]
            results = self.reranker.rank(damage_description, images, doc_ids=invoices)
            invoices = [doc.doc_id for doc in results]
            print(invoices)

        if return_only_path:
            return invoices, images_paths

        if not self.path_to_invoices:
            raise ValueError("Path to data must be provided.")

        invoices_tables = []

        for invoice in invoices:
            pdf_path = f"{self.path_to_invoices}/{invoice}"
            reader = PdfReader(pdf_path)
            page = reader.pages[0]
            text = page.extract_text()

            table_text = re.search(r"Beschädigtes Teil.*?Gesamtsumme:.*?EUR", text, re.DOTALL).group()

            lines = table_text.splitlines()
            header = lines[0]
            other_text = "\n".join(lines[1:])
            cleaned_text = re.sub(r"(?<!\d)\n", " ", other_text)

            table = header + "\n" + cleaned_text

            inv = table.split("\n")
            reformatted_inv = "Beschädigtes Teil | Teilkosten (EUR) | Arbeitsstunden | Arbeitskosten (EUR/Stunde) | Gesamtkosten (EUR)\n" + "\n".join(
                " ".join(inv[i].split(" ")[:-4]) + " | " + ' | '.join(inv[i].split(" ")[-4:]) for i in
                range(1, len(inv) - 1)) + "\n" + inv[-1]

            invoices_tables.append(reformatted_inv)

        return invoices_tables, invoices


def build_rag(directory):
    invoices = os.listdir(f"{directory}/invoices_validated")
    invoices = [i for i in invoices if i.endswith(".pdf")]

    image_invoice = []
    os.makedirs(f"{directory}/images", exist_ok=True)
    os.makedirs(f"{directory}/invoices", exist_ok=True)

    for invoice in invoices:
        doc = fitz.open(f"{directory}/invoices_validated/{invoice}")

        page = doc[1]
        image_list = page.get_images(full=True)
        text = page.get_text()

        xref = image_list[0][0]
        base_image = doc.extract_image(xref)
        image_bytes = base_image["image"]
        image_name = invoice.replace(".pdf", ".png")
        with open(f"{directory}/images/{image_name}", "wb") as img_file:
            img_file.write(image_bytes)

        doc.delete_pages(range(1, doc.page_count))
        doc.save(f"{directory}/invoices/{invoice}")
        doc.close()

        image_invoice.append({
            "invoice": invoice,
            "image": image_name,
            "description": text
        })

    image_invoice = pd.DataFrame(image_invoice)

    device = "cuda" if torch.cuda.is_available() else "cpu"
    model, preprocess = clip.load("ViT-B/32", device=device)
    images = image_invoice["image"].tolist()

    embeddings = []
    image_indices = []
    img_ids = []

    for idx, img_path in enumerate(tqdm(images)):
        image = Image.open(f"{directory}/images/{img_path}")
        img_ids.append(idx)
        inputs = preprocess(image).unsqueeze(0).to(device)

        with torch.no_grad():
            image_embedding = model.encode_image(inputs)

        image_embedding = image_embedding / image_embedding.norm(dim=-1, keepdim=True)
        embeddings.append(image_embedding.cpu().numpy().astype("float32"))
        image_indices.append(img_path)

    image_invoice["img_id"] = img_ids
    image_invoice.to_csv(f"{directory}/image_invoice.csv", index=False)

    embeddings_np = np.vstack(embeddings)

    dimension = embeddings_np.shape[1]
    index = faiss.IndexFlatIP(dimension)
    index.add(embeddings_np)

    faiss.write_index(index, f"{directory}/invoice_index.faiss")