File size: 6,463 Bytes
f11a85d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 |
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")
|