add httpx caching

app.py

@@ -25,10 +25,16 @@ import httpx
 import orjson
 import httpx
 from functools import lru_cache
+from httpx import Client
+from httpx_caching import CachingClient
+from httpx_caching import OneDayCacheHeuristic
 
-from sys import platform
+client = Client()
 
-CACHE_DIR = "./cache" if platform == "darwin" else "/data/"
+client = CachingClient(client, heuristic=OneDayCacheHeuristic())
+
+
+# CACHE_DIR = "./cache" if platform == "darwin" else "/data/"
 
 disable_progress_bars()
 
@@ -69,7 +75,7 @@ def _try_load_model_card(hub_id):
         url = hf_hub_url(
             repo_id=hub_id, filename="README.md"
         )  # We grab card this way rather than via client library to improve performance
-        card_text = httpx.get(url).text
+        card_text = client.get(url).text
         length = len(card_text)
     except EntryNotFoundError:
         card_text = None
@@ -355,63 +361,10 @@ def create_query_url(query, skip=0):
 # @cache.memoize(expire=60 * 60 * 24 * 3)  # expires after 3 days
 def get_results(query) -> Dict[Any, Any]:
     url = create_query_url(query)
-    r = httpx.get(url)
+    r = client.get(url)
     return r.json()
 
 
-# result = {
-#     "repoId": "621ffdc036468d709f175eb5",
-#     "repoOwnerId": "60d099234330bad169e611f0",
-#     "isPrivate": False,
-#     "type": "model",
-#     "likes": 0,
-#     "isReadmeFile": True,
-#     "readmeStartLine": 8,
-#     "updatedAt": 1687806057107,
-#     "repoName": "hate_speech_en",
-#     "repoOwner": "IMSyPP",
-#     "tags": "pytorch, bert, text-classification, en, transformers, license:mit, has_space",
-#     "name": "IMSyPP/hate_speech_en",
-#     "fileName": "README.md",
-#     "formatted": {
-#         "repoName": [{"text": "hate_speech_en", "type": "text"}],
-#         "repoOwner": [{"text": "IMSyPP", "type": "text"}],
-#         "fileContent": [
-#             {"text": "\n# ", "type": "text"},
-#             {"text": "Hate", "type": "highlight"},
-#             {"text": " ", "type": "text"},
-#             {"text": "Speech", "type": "highlight"},
-#             {
-#                 "text": " Classifier for Social Media Content in English Language\n\nA monolingual model for ",
-#                 "type": "text",
-#             },
-#             {"text": "hate", "type": "highlight"},
-#             {"text": " ", "type": "text"},
-#             {"text": "speech", "type": "highlight"},
-#             {
-#                 "text": " classification of social media content in English language. The model was trained on 103190 YouTube comments and tested on an independent test set of 20554 YouTube comments. It is based on English BERT base pre-trained language model.\n\n## Tokenizer\n\nDuring training the text was preprocessed using the original English BERT base tokenizer. We suggest the same tokenizer is used for inference.\n\n## Model output\n\nThe model classifies each input into one of four distinct classes:\n* 0 - acceptable\n* 1 - inappropriate\n* 2 - offensive\n* 3 - violent",
-#                 "type": "text",
-#             },
-#         ],
-#         "tags": [
-#             {
-#                 "text": "pytorch, bert, text-classification, en, transformers, license:mit, has_space",
-#                 "type": "text",
-#             }
-#         ],
-#         "name": [{"text": "IMSyPP/hate_speech_en", "type": "text"}],
-#         "fileName": [{"text": "README.md", "type": "text"}],
-#     },
-#     "authorData": {
-#         "avatarUrl": "https://aeiljuispo.cloudimg.io/v7/https://s3.amazonaws.com/moonup/production/uploads/1624284535629-60d08803565dd1d0867f7a37.png?w=200&h=200&f=face",
-#         "fullname": "IMSyPP EU REC AG project 875263 - Innovative Monitoring Systems and Prevention Policies of Online Hate Speech",
-#         "name": "IMSyPP",
-#         "type": "org",
-#         "isHf": False,
-#     },
-# }
-
-
 @backoff.on_exception(
     backoff.expo,
     Exception,