# I updated the code and connected S3 storage for analysis

image_utils.py

@@ -5,54 +5,91 @@
 from tensorflow.keras.applications.resnet50 import ResNet50, preprocess_input
 from tensorflow.keras.models import Model
 from tensorflow.keras.preprocessing import image
-from database import load_db
+import boto3
+from botocore.client import Config
 import asyncio
 from sklearn.metrics.pairwise import cosine_similarity
-import os
+
+S3_BUCKET_NAME = 'your-new-bucket-name'
+S3_REGION = 'your-new-region'
+S3_ACCESS_KEY = 'your-new-access-key'
+S3_SECRET_ACCESS_KEY = 'your-new-secret-access-key'
+S3_ENDPOINT_URL = 'https://your-new-endpoint'
+
+s3_client = boto3.client(
+    's3',
+    endpoint_url=S3_ENDPOINT_URL,
+    aws_access_key_id=S3_ACCESS_KEY,
+    aws_secret_access_key=S3_SECRET_ACCESS_KEY,
+    config=Config(region_name=S3_REGION, signature_version='s3v4'),
+)
 
 base_model = ResNet50(weights='imagenet', include_top=False, pooling='avg')
 model = Model(inputs=base_model.input, outputs=base_model.output)
 
 
-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)
-      img = cv2.imdecode(image_array, cv2.IMREAD_COLOR)
-      return img
-    raise Exception('Image download failed')
+async def download_image_from_s3(file_name):
+  try:
+    print(f"Downloading image from S3: {file_name}")
+    response = s3_client.get_object(Bucket=S3_BUCKET_NAME, Key=file_name)
+    image_data = response['Body'].read()
+    image_array = np.frombuffer(image_data, np.uint8)
+    img = cv2.imdecode(image_array, cv2.IMREAD_COLOR)
+    print("Image downloaded from S3")
+    return img
+  except Exception as e:
+    print(f"Failed to download image {file_name} from S3 due to {e}")
+    raise Exception('Image download from S3 failed')
 
 
 def extract_features(img):
+  print("Extracting features from the image")
   img = cv2.resize(img, (224, 224))
   img_array = image.img_to_array(img)
   img_array = np.expand_dims(img_array, axis=0)
   img_array = preprocess_input(img_array)
   features = model.predict(img_array)
+  print("Features extracted")
   return features
 
 
-async def compare_images(session, image_data, target_features):
+async def compare_images(session, file_name, target_features):
   try:
-    current_image = await download_image(session, image_data["url"])
+    current_image = await download_image_from_s3(file_name)
     current_features = extract_features(current_image)
     similarity = cosine_similarity(current_features, target_features)
+    print(f"Image similarity for {file_name}: {similarity[0][0]}")
     if similarity[0][0] > 0.5:
-      return (similarity[0][0], image_data["url"])
+      return (similarity[0][0], file_name)
   except Exception as e:
-    print(f"Failed to compare image {image_data['url']} due to {e}")
+    print(f"Failed to compare image {file_name} due to {e}")
   return (0, "")
 
 
+async def list_s3_images():
+  try:
+    response = s3_client.list_objects_v2(Bucket=S3_BUCKET_NAME)
+    images = [
+        item['Key'] for item in response.get('Contents', [])
+        if item['Key'].endswith(('.jpg', '.jpeg', '.png'))
+    ]
+    print(f"Found {len(images)} images in S3 bucket")
+    return images
+  except Exception as e:
+    print(f"Failed to list images in S3 bucket: {e}")
+    raise Exception('Listing images in S3 failed')
+
+
 async def find_similar_images(file_path):
-  db_data = load_db()
   target_image = cv2.imread(file_path)
   target_features = extract_features(target_image)
+
+  file_names = await list_s3_images()
+
   async with aiohttp.ClientSession() as session:
     tasks = [
-        compare_images(session, entry, target_features) for entry in db_data
-        if "url" in entry
+        compare_images(session, file_name, target_features)
+        for file_name in file_names
     ]
     results = await asyncio.gather(*tasks)
 
@@ -62,6 +99,7 @@ async def find_similar_images(file_path):
   similar_images = []
   for result in sorted_results:
     if result[1]:
-      similar_images.append(result[1])
+      similar_images.append(f"{S3_ENDPOINT_URL}/{S3_BUCKET_NAME}/{result[1]}")
 
+  print(f"Found {len(similar_images)} similar images")
   return similar_images