csd4ni3l/grass_touching_captcha
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add word vector cache with JSON and add a constant for the word to compare with

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csd4ni3l
2025-08-09 10:30:57 +02:00
parent ba811d3ce7
commit a8ee1c90ae
2 changed files with 14 additions and 3 deletions
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1
constants.py
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@@ -7,5 +7,6 @@ JINA_HEADERS = {
RICKROLL_LINK = "https://www.youtube.com/watch?v=xvFZjo5PgG0"
UPLOAD_DIR = "uploads"
MINIMUM_COSINE_SIMILARITY = 0.7
WORD_TO_COMPARE = "hand touching grass"
UPLOAD_DIR = os.path.join(os.getcwd(), UPLOAD_DIR)

16
jina.py
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@@ -1,5 +1,5 @@
from constants import JINA_URL, JINA_HEADERS
import requests, dotenv, os, sys, numpy as np
from constants import JINA_URL, JINA_HEADERS, WORD_TO_COMPARE
import requests, dotenv, os, sys, json, numpy as np
def get_embedding(input: list):
headers = JINA_HEADERS
@@ -24,7 +24,17 @@ def get_embedding(input: list):
return [jina_object["embedding"] for jina_object in response.json()["data"]]
def get_grass_touching_similarity(image_url):
grass_image_embedding, grass_word_embedding = get_embedding([{"image": image_url}, {"text": "hand touching grass"}])
if os.path.exists("compared_word_emb.json"):
with open("compared_word_emb.json", "r") as file:
grass_word_embedding = json.load(file)
grass_image_embedding = get_embedding([{"image": image_url}])
else:
grass_image_embedding, grass_word_embedding = get_embedding([{"image": image_url}, {"text": WORD_TO_COMPARE}])
with open("compared_word_emb.json", "w") as file:
file.write(json.dumps(grass_word_embedding))
grass_image_embedding = np.array(grass_image_embedding)
grass_word_embedding = np.array(grass_word_embedding)