feat: add GCS

src/cloud_storage/__init__.py

@@ -0,0 +1,11 @@
+from .google_cloud_storage import GoogleCloudStorage
+
+storage_map = {
+    "gcp": GoogleCloudStorage,
+}
+
+
+def get_cloud_storage(ingestion_mode):
+    if ingestion_mode not in storage_map:
+        raise ValueError(f"Invalid storage mode: {ingestion_mode}")
+    return storage_map[ingestion_mode]()

src/cloud_storage/base_storage.py

@@ -0,0 +1,13 @@
+class BaseStorage:
+    def __init__(self):
+        self.client = None
+
+    def upload_file_from_content(
+        self, bucket_name: str, bucket_file_path: str, file_content: str
+    ) -> str:
+        raise NotImplementedError
+
+    def upload_file_from_path(
+        self, bucket_name: str, bucket_file_path: str, source_file_path: str
+    ) -> str:
+        raise NotImplementedError

src/cloud_storage/google_cloud_storage.py

@@ -0,0 +1,34 @@
+from google.cloud import storage
+from google.oauth2 import service_account
+
+from .base_storage import BaseStorage
+
+
+class GoogleCloudStorage(BaseStorage):
+    def __init__(self, service_account_path: str):
+        credentials = service_account.Credentials.from_service_account_file(
+            service_account_path
+        )
+        self.client = storage.Client(credentials=credentials)
+
+    def upload_file_from_content(
+        self, bucket_name: str, bucket_file_path: str, file_content: bytes
+    ) -> str:
+        """Uploads a file to a Google Cloud Storage bucket
+
+        Args:
+            bucket_name (str): Bucket name
+            bucket_file_path (str):  Path to the file to be uploaded
+            file_content (bytes): Path to the file to be uploaded
+
+        Returns:
+            str: URL of the uploaded file
+        """
+        print(f"gs://{bucket_name}/{bucket_file_path}")
+
+        bucket = self.client.get_bucket(bucket_name)
+        blob = bucket.blob(f"{bucket_file_path}")
+        blob.upload_from_string(file_content)
+
+        print(f"File uploaded to gs://{bucket_name}/{bucket_file_path}")
+        return f"gs://{bucket_name}/{bucket_file_path}"

src/fetch_places.py

@@ -8,11 +8,12 @@ from data_models.park_manager import ParkManager
 from data_models.image_manager import ImageManager
 from data_models.openai_manager import OpenAIManager
 from openai_predictions import process_agent_predictions
-
+from cloud_storage.google_cloud_storage import GoogleCloudStorage
 
 park_manager = ParkManager()
 image_manager = ImageManager()
 prediction_manager = OpenAIManager()
+gcs = GoogleCloudStorage("ialab-fr-7047e56bef0b.json")
 
 
 def nearby_search(coordinates: tuple) -> Tuple[str, str, dict]:
@@ -144,12 +145,11 @@ def fetch_place_photos(place_id: str, place_name: str, photos: dict) -> int:
         max_height = photo["heightPx"]
 
         photo_binary = fetch_photo(place_id, photo_id, max_width, max_height)
-        file_name = f"{folder_path}/{photo_id[:150]}.jpg"
-        with open(file_name, "wb") as f:
-            f.write(photo_binary)
 
-        image_id = image_manager.add_image(file_name, datetime.now(), park_id=park_id)
-        predictions = process_agent_predictions(file_name)
+        file_name = f"{place_name}/{photo_id[:150]}.jpg"
+        bucket_path = gcs.upload_file_from_content("suad_park", file_name, photo_binary)
+        image_id = image_manager.add_image(bucket_path, datetime.now(), park_id=park_id)
+        predictions = process_agent_predictions(file_name, photo_binary)
         prediction_manager.add_predictions(image_id, predictions)
 
     print(f"{len(photos)} photos fetched for place: {place_name}")

src/openai_predictions.py

@@ -6,9 +6,8 @@ from openai_agent import Agent
 
 agent = Agent("./prompts")
 
-
 # Function to encode the image
-def encode_image(image_path: str):
+def encode_image_from_path(image_path: str) -> str:
     if not os.path.exists(image_path):
         raise FileNotFoundError(f"Image not found at {image_path}")
 
@@ -16,8 +15,12 @@ def encode_image(image_path: str):
         return base64.b64encode(image_file.read()).decode("utf-8")
 
 
-def process_agent_predictions(file_path):
-    base64_image = encode_image(file_path)
+def encode_image(photo_binary: bytes) -> str:
+    return base64.b64encode(photo_binary).decode("utf-8")
+
+
+def process_agent_predictions(file_path: str, photo_binary: bytes):
+    base64_image = encode_image(photo_binary)
 
     prompts = [
         "built_elements",