Add fetching mechanism

.gitattributes

@@ -33,4 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
-10402.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpg filter=lfs diff=lfs merge=lfs -text

requirements.txt

@@ -7,4 +7,5 @@ open_clip_torch
 numpy
 pandas
 Pillow
-pymilvus
+pymilvus
+requests

utils/fetch_image.py

@@ -0,0 +1,8 @@
+import requests
+from PIL import Image
+from io import BytesIO
+
+def fetch_image(url):
+    response = requests.get(url)
+    img = Image.open(BytesIO(response.content))
+    return img

utils/vector_database.py

@@ -1,6 +1,7 @@
 from PIL import Image
 from pymilvus import connections, Collection
 from pymilvus import FieldSchema, CollectionSchema, DataType, Collection
+from utils.fetch_image import fetch_image
 
 def load_collection(name):
     collection = Collection(name)
@@ -55,7 +56,7 @@ def search_in_milvus(collection, search_field, query_embedding, top_k=6):
         search_field, # Field to search in
         param=search_params,
         limit=top_k, # Top k results
-        output_fields=["image_id", "metadata"]
+        output_fields=["image_id", "metadata", "url"]
     )
 
     # Step 2: Extract the relevant information from the search results
@@ -63,12 +64,13 @@ def search_in_milvus(collection, search_field, query_embedding, top_k=6):
     for result in results[0]:  # The first element of 'results' contains the search results
         image_id = result.entity.get("image_id")  # Retrieve the image ID
         metadata = result.entity.get("metadata")  # Retrieve metadata (such as description, brand, etc.)
+        url = result.entity.get("url")  # Retrieve url to fetch image
         similarity_score = result.distance  # Retrieve similarity score (distance)
 
         # Load the image (you can use PIL to load the image)
         #image_path = "/content/drive/MyDrive/images/" + str(image_id) + ".jpg"
-        image_path = "/home/user/app/images/" + str(image_id) + ".jpg"
-        image = Image.open(image_path)
+        #image = Image.open(image_path)
+        image = fetch_image(url)
 
         # Append the image, metadata, and score to the search results
         search_results.append({