diff --git a/assets/new update/database.py b/assets/new update/database.py
deleted file mode 100644
index 0fc52fe..0000000
--- a/assets/new update/database.py	
+++ /dev/null
@@ -1,27 +0,0 @@
-import json
-
-
-def load_db():
-  try:
-    with open("images.json", "r") as file:
-      return json.load(file)
-  except FileNotFoundError:
-    return []
-
-
-def save_db(data):
-  with open("images.json", "w") as file:
-    json.dump(data, file, indent=4)
-
-
-def add_image_to_db(file_path, image_hash):
-  db_data = load_db()
-  db_data.append({"hash": image_hash, "path": file_path})
-  save_db(db_data)
-
-
-def init_db():
-  db_data = load_db()
-  if not isinstance(db_data, list):
-    db_data = []
-    save_db(db_data)
diff --git a/assets/new update/image_utils.py b/assets/new update/image_utils.py
deleted file mode 100644
index 79b2bb6..0000000
--- a/assets/new update/image_utils.py	
+++ /dev/null
@@ -1,93 +0,0 @@
-import aiohttp
-import cv2
-import numpy as np
-from database import load_db
-from skimage.metrics import structural_similarity as ssim
-import asyncio
-from bs4 import BeautifulSoup
-
-
-async def download_image(session, url):
-  async with session.get(url) as response:
-    if response.status == 200:
-      image_data = await response.read()
-      image_array = np.frombuffer(image_data, np.uint8)
-      image = cv2.imdecode(image_array, cv2.IMREAD_COLOR)
-      return image
-  raise Exception('Image download failed')
-
-
-async def get_image_urls_from_page(session, page_url):
-  async with session.get(page_url) as response:
-    if response.status == 200:
-      html_content = await response.text()
-      soup = BeautifulSoup(html_content, 'html.parser')
-      image_elements = soup.select('.ty-product-img .cm-image')
-      return [image['src'] for image in image_elements]
-  return []
-
-
-async def process_image(session, image_entry, target_image):
-  try:
-    image_urls = await get_image_urls_from_page(session, image_entry["url"])
-    for image_url in image_urls:
-      current_image = await download_image(session, image_url)
-      optimal_size = max(max(target_image.shape[:2]),
-                         max(current_image.shape[:2]))
-      optimal_size = min(1024, optimal_size)
-      target_image_resized = cv2.resize(target_image,
-                                        (optimal_size, optimal_size))
-      current_image_resized = cv2.resize(current_image,
-                                         (optimal_size, optimal_size))
-      target_gray = cv2.cvtColor(target_image_resized, cv2.COLOR_BGR2GRAY)
-      current_gray = cv2.cvtColor(current_image_resized, cv2.COLOR_BGR2GRAY)
-      ssim_index = ssim(target_gray, current_gray)
-      orb = cv2.ORB_create(nfeatures=500)
-      target_keypoints, target_descriptors = orb.detectAndCompute(
-          target_gray, None)
-      current_keypoints, current_descriptors = orb.detectAndCompute(
-          current_gray, None)
-      if target_descriptors is None or current_descriptors is None:
-        return (0, image_entry["url"])
-      index_params = dict(algorithm=6,
-                          table_number=6,
-                          key_size=12,
-                          multi_probe_level=1)
-      search_params = dict(checks=50)
-      flann = cv2.FlannBasedMatcher(index_params, search_params)
-      matches = flann.knnMatch(target_descriptors, current_descriptors, k=2)
-      good_matches = [m for m, n in matches if m.distance < 0.75 * n.distance]
-      feature_score = len(good_matches) / float(len(target_keypoints))
-      target_hist = cv2.calcHist([target_image_resized], [0, 1, 2], None,
-                                 [32, 32, 32], [0, 256, 0, 256, 0, 256])
-      current_hist = cv2.calcHist([current_image_resized], [0, 1, 2], None,
-                                  [32, 32, 32], [0, 256, 0, 256, 0, 256])
-      cv2.normalize(target_hist, target_hist)
-      cv2.normalize(current_hist, current_hist)
-      hist_score = cv2.compareHist(target_hist, current_hist,
-                                   cv2.HISTCMP_CORREL)
-      final_score = (feature_score + ssim_index + hist_score) / 3
-      return (final_score, image_entry["url"])
-  except Exception as e:
-    print(f"Failed to process image {image_entry['url']} due to {e}")
-    return (0, image_entry["url"])
-
-
-async def find_similar_images(file_path):
-  db_data = load_db()
-  target_image = cv2.imread(file_path)
-  async with aiohttp.ClientSession() as session:
-    tasks = [
-        process_image(session, entry, target_image) for entry in db_data
-        if "url" in entry
-    ]
-    results = await asyncio.gather(*tasks)
-  valid_results = filter(lambda x: x[0] > 0, results)
-  sorted_results = sorted(valid_results, key=lambda x: x[0], reverse=True)[:5]
-  seen_urls = set()
-  top_similar_images = []
-  for result in sorted_results:
-    if result[1] not in seen_urls:
-      top_similar_images.append(result[1])
-      seen_urls.add(result[1])
-  return top_similar_images
diff --git a/assets/new update/images.json b/assets/new update/images.json
deleted file mode 100644
index 333b58a..0000000
--- a/assets/new update/images.json	
+++ /dev/null
@@ -1,4 +0,0 @@
-[
-    {
-        "url": "https://website.ru/mebel/.com
-    }]
diff --git a/assets/new update/main.py b/assets/new update/main.py
deleted file mode 100644
index 3c5c641..0000000
--- a/assets/new update/main.py	
+++ /dev/null
@@ -1,57 +0,0 @@
-from fastapi import FastAPI, File, UploadFile, Form
-from fastapi.responses import HTMLResponse
-import aiohttp
-import hashlib
-import os
-from image_utils import find_similar_images, download_image
-from database import init_db, add_image_to_db
-
-app = FastAPI()
-
-init_db()
-
-
-@app.get("/", response_class=HTMLResponse)
-async def upload_form():
-  with open('templates/upload_form.html', 'r') as file:
-    html_content = file.read()
-  return HTMLResponse(content=html_content)
-
-
-@app.post("/upload/")
-async def create_upload_file(file: UploadFile = File(...)):
-  contents = await file.read()
-  image_hash = hashlib.sha256(contents).hexdigest()
-  file_path = f'uploads/{image_hash}.jpg'
-  os.makedirs(os.path.dirname(file_path), exist_ok=True)
-  with open(file_path, 'wb') as f:
-    f.write(contents)
-  add_image_to_db(file_path, image_hash)
-  similar_images = await find_similar_images(file_path)
-  return {
-      "message": "File uploaded successfully",
-      "filename": file.filename,
-      "similar_images": similar_images
-  }
-
-
-@app.post("/upload_from_url/")
-async def upload_from_url(image_url: str = Form(...)):
-  async with aiohttp.ClientSession() as session:
-    async with session.get(image_url) as response:
-      if response.status == 200:
-        contents = await response.read()
-        image_hash = hashlib.sha256(contents).hexdigest()
-        file_path = f'uploads/{image_hash}.jpg'
-        os.makedirs(os.path.dirname(file_path), exist_ok=True)
-        with open(file_path, 'wb') as f:
-          f.write(contents)
-        add_image_to_db(file_path, image_hash)
-        similar_images = await find_similar_images(file_path)
-        return {
-            "message": "File uploaded successfully from URL",
-            "filename": os.path.basename(file_path),
-            "similar_images": similar_images
-        }
-      else:
-        return {"message": "Failed to download image from URL"}