Change Database port to work on HuggingFace Spaces properly

config.py

@@ -6,11 +6,19 @@ qdrant_api_key = os.getenv('QDRANT_API_KEY')
 description = """This is a Kanji image search demo. Draw or upload an image of an individual Kanji character."""
 
 article = """
+### Getting better results
+
+Try different brush sizes. 
+Try to draw it centered in the middle of the canvas, both horizontally and vertically. 
+You may want to try using an external tool to draw then import a file.
+
+The results is sorted by estimated distance from the input, but will rarely give the exact Kanji you are searching for as the first result
+
 ### About this project
 
-You can find the source code as well as more information in https://github.com/etrotta/kanji_lookup
+It uses the "kha-white/manga-ocr-base" Vision Transformer Encoder model to create embeddings, then uses a vector database (qdrant) to find similar characters.
 
-It uses the "kha-white/manga-ocr-base" ViT Encoder model to create embeddings, then uses a vector database (qdrant) to find similar characters.
+You can find the code used to create the embeddings as well as more information in https://github.com/etrotta/kanji_lookup
 
-The vector database has been populated with over 10k characters from [The KANJIDIC project](https://www.edrdg.org/wiki/index.php/KANJIDIC_Project), each rendered in multiple fonts downloaded from Google Fonts
+The database has been populated with over 10000 characters from [The KANJIDIC project](https://www.edrdg.org/wiki/index.php/KANJIDIC_Project), each rendered in multiple fonts downloaded from Google Fonts
 """

database.py

@@ -4,7 +4,12 @@ from qdrant_client import QdrantClient, models
 
 from config import qdrant_location, qdrant_api_key
 
-qdrant = QdrantClient(qdrant_location, api_key=qdrant_api_key)
+qdrant = QdrantClient(
+    qdrant_location,
+    api_key=qdrant_api_key,
+    port=443,
+    timeout=30,
+)
 
 def search_vector(query_vector: torch.Tensor, limit: int=20) -> list[models.ScoredPoint]:
     hits = qdrant.search(