[ADD] First demo with 2500 images

.gitattributes

@@ -32,3 +32,4 @@ 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
+resources/embeddings.npy filter=lfs diff=lfs merge=lfs -text

geoguessr_bot/guessr/global_embedder_guessr.py

@@ -1,6 +1,7 @@
 from dataclasses import dataclass
 
 from PIL import Image
+import pandas as pd
 
 from geoguessr_bot.guessr import AbstractGuessr
 from geoguessr_bot.interfaces import Coordinate
@@ -15,16 +16,28 @@ class GlobalEmbedderGuessr(AbstractGuessr):
 
     embedder: AbstractImageEmbedder
     retriever: Retriever
+    metadata_path: str
+
+    def __post_init__(self):
+        """Load metadata
+        """
+        metadata = pd.read_csv(self.metadata_path)
+        self.image_to_coordinate = {
+            image.split("/")[-1]: Coordinate(latitude=latitude, longitude=longitude)
+            for image, latitude, longitude in zip(metadata["path"], metadata["latitude"], metadata["longitude"])
+        }
+        
 
     def guess(self, image: Image) -> Coordinate:
         """Guess a coordinate from an image
         """
         # Embed image
-        image_embedding = self.embedder.embed(image)
-
+        image_embedding = self.embedder.embed(image)[None, :]
+        
         # Retrieve nearest neighbors
         nearest_neighbors = self.retriever.retrieve(image_embedding)
+        nearest_neighbor = nearest_neighbors[0][0]
 
         # Guess coordinate
-        guess_coordinate = self.retriever.image_to_coordinate[nearest_neighbors[0][0]]
+        guess_coordinate = self.image_to_coordinate[nearest_neighbor]
         return guess_coordinate

geoguessr_bot/retriever/retriever.py

@@ -5,8 +5,9 @@ import faiss
 
 
 class Retriever:
-    def __init__(self, embeddings_path: str):
+    def __init__(self, embeddings_path: str, n_neighbors: int = 5):
         self.embeddings: Dict[str, np.ndarray] = self.load_embeddings(embeddings_path)
+        self.n_neighbors = n_neighbors
 
         # Keep track of image names
         self.image_to_index = {image_name: i for i, image_name in enumerate(self.embeddings.keys())}
@@ -24,9 +25,8 @@ class Retriever:
         """
         return np.load(embeddings_path, allow_pickle=True).item()
 
-    def retrieve(self, queries: np.ndarray, n_neighbors: int = 5) -> List[List[str]]:
+    def retrieve(self, queries: np.ndarray) -> List[List[str]]:
         """Retrieve nearest neighbors indexes from queries
         """
-        dist, indexes = self.index.search(queries, n_neighbors)
+        _, indexes = self.index.search(queries, self.n_neighbors)
         return [[self.index_to_image[i] for i in index] for index in indexes]
-