First commit

jina.py

@@ -0,0 +1,38 @@
+from constants import JINA_URL, JINA_HEADERS
+import requests, dotenv, os, sys, numpy as np
+
+def get_embedding(input: list):
+    headers = JINA_HEADERS
+
+    if os.path.exists(".env"):
+        dotenv.load_dotenv(".env")
+
+    if not "JINA_TOKEN" in os.environ:
+        print("Jina Token not found, exiting...")
+        sys.exit(1)
+
+    headers["Authorization"] = f"Bearer {os.environ['JINA_TOKEN']}"
+
+    data = {
+        "model": "jina-embeddings-v3",
+        "input": input
+    }
+
+    response = requests.post(JINA_URL, headers=JINA_HEADERS, json=data)
+
+    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_url, "hand touching grass"])
+
+    grass_image_embedding = np.array(grass_image_embedding)
+    grass_word_embedding = np.array(grass_word_embedding)
+
+    dot_product = np.dot(grass_image_embedding, grass_word_embedding)
+
+    grass_image_mag = np.linalg.norm(grass_image_embedding)
+    grass_word_mag = np.linalg.norm(grass_word_embedding)
+
+    cosine_similarity = dot_product / (grass_image_mag * grass_word_mag)
+
+    return cosine_similarity