Update dependeies and filter logic.

app/backend/data_engine.py

@@ -7,7 +7,7 @@ from typing import List
 import pandas as pd
 
 from app.backend.constant import ModelProvider
-from utils.cache_decorator import cache_with_custom_key
+from utils.cache_decorator import cache_df_with_custom_key
 
 COLUMNS = ['model_name', 'group_name', 'leaderboard', 'dataset_name',
            'embd_dtype', 'embd_dim', 'num_params', 'max_tokens', 'similarity',
@@ -75,7 +75,7 @@ class DataEngine:
                 return True
         return organization in providers
 
-    @cache_with_custom_key("json_result")
+    @cache_df_with_custom_key("json_result")
     def jsons_to_df(self):
 
         results_list = self.results

app/ui/component/filter_component.py

@@ -20,7 +20,7 @@ class FilterComponent:
         self.embd_dims = embd_dims
         self.similarities = similarities
 
-    def _validate_input(self,value:str):
+    def _validate_input(self, value: str):
         if value == "":
             return value
         try:
@@ -95,5 +95,4 @@ class FilterComponent:
                 scale=2,
             )
 
-
         return navigations, model_provides, evaluation_metrics, embd_dtypes, embd_dims, similarities, max_tokens

app/ui/component/subtabs_component.py

@@ -12,7 +12,7 @@ class SubtabsComponent:
         self.data_engine = data_engine
 
     def show(self, navigations: str = None, model_provides: List = None, evaluation_metrics: str = None,
-             embd_dtypes: str = None, embd_dims: List = None, similarity: str = None, max_tokens: str = None):
+             embd_dtypes: str = None, embd_dims: List = None, similarity: str = None, max_tokens: int = None):
 
         df_result = self.data_engine.jsons_to_df()
 
@@ -39,7 +39,9 @@ class SubtabsComponent:
                         gr_df = gr.Dataframe(
                             df_leaderboard,
                             datatype=COLUMNS_TYPES,
-                            interactive=False
+                            # interactive=True,
+
+                            type="pandas"
                         )
 
                         items.append(gr_df)
@@ -50,7 +52,10 @@ class SubtabsComponent:
                             gr_df = gr.Dataframe(
                                 df,
                                 datatype=COLUMNS_TYPES,
-                                interactive=False
+                                # interactive=True,
+                                height=500,
+                                type="pandas"
+
                             )
                             items.append(gr_df)
             items.append(column)

app/ui/home.py

@@ -1,3 +1,5 @@
+import time
+
 import pandas as pd
 
 from app.backend.constant import Navigation, ModelProvider, EvaluationMetric, EmbdDtype, EmbdDim, Similarity
@@ -11,6 +13,7 @@ import gradio as gr
 NUM_DATASETS = 1
 NUM_SCORES = 2
 NUM_MODELS = 3
+HANDLING = False
 
 
 def init_home():
@@ -46,20 +49,33 @@ def init_home():
                    inputs=[navigations, model_provides, evaluation_metrics, embd_dtypes, embd_dims, similarities,
                            max_tokens], outputs=columns)
 
-        gr.on(triggers=[navigations.change,
-                        model_provides.change,
-                        evaluation_metrics.change,
-                        embd_dtypes.change,
-                        embd_dims.change,
-                        similarities.change,
-                        max_tokens.change
-                        ],
-              inputs=[navigations, model_provides, evaluation_metrics, embd_dtypes, embd_dims, similarities,
-                      max_tokens],
-              fn=sub_tabs.show,
-              outputs=columns,
-              trigger_mode="once"
-              )
+        navigations.change(trigger_mode="once", fn=sub_tabs.show,
+                           inputs=[navigations, model_provides, evaluation_metrics, embd_dtypes, embd_dims,
+                                   similarities,
+                                   max_tokens], outputs=columns)
+        model_provides.change(trigger_mode="once", fn=sub_tabs.show,
+                              inputs=[navigations, model_provides, evaluation_metrics, embd_dtypes, embd_dims,
+                                      similarities,
+                                      max_tokens], outputs=columns)
+        evaluation_metrics.change(trigger_mode="once", fn=sub_tabs.show,
+                                  inputs=[navigations, model_provides, evaluation_metrics, embd_dtypes, embd_dims,
+                                          similarities,
+                                          max_tokens], outputs=columns)
+        embd_dtypes.change(trigger_mode="once", fn=sub_tabs.show,
+                           inputs=[navigations, model_provides, evaluation_metrics, embd_dtypes, embd_dims,
+                                   similarities,
+                                   max_tokens], outputs=columns)
+        embd_dims.change(trigger_mode="once", fn=sub_tabs.show,
+                         inputs=[navigations, model_provides, evaluation_metrics, embd_dtypes, embd_dims, similarities,
+                                 max_tokens], outputs=columns)
+
+        similarities.change(trigger_mode="once", fn=sub_tabs.show,
+                            inputs=[navigations, model_provides, evaluation_metrics, embd_dtypes, embd_dims,
+                                    similarities,
+                                    max_tokens], outputs=columns)
+        max_tokens.change(trigger_mode="always_last", fn=sub_tabs.show,
+                          inputs=[navigations, model_provides, evaluation_metrics, embd_dtypes, embd_dims, similarities,
+                                  max_tokens], outputs=columns)
 
-    block.queue(max_size=10)
+    block.queue(max_size=1)
     return block

requirements.txt

@@ -1,4 +1,3 @@
-gradio==4.44.1
+gradio==4.20.0
 uvicorn>=0.14.0
 fastapi<0.113.0
-cachetools==5.5.0

utils/cache_decorator.py

@@ -3,23 +3,36 @@
 # @Author   : q275343119
 # @File     : cache_decorator.py
 # @Description:
-from cachetools import TTLCache
+import time
 from functools import wraps
+import pandas as pd
 
-CACHE = TTLCache(maxsize=5, ttl=3600)
+CACHE = {}
+TTL = 3600
 
 
-def cache_with_custom_key(cache_key: str):
+def cache_df_with_custom_key(cache_key: str):
     def decorator(func):
         @wraps(func)
         def wrapper(*args, **kwargs):
-            if cache_key in CACHE:
-                return CACHE[cache_key]
+            if cache_key in CACHE and CACHE[cache_key].get("expiry") - time.time() < TTL:
+                return CACHE[cache_key]["data"]
 
-            result = func(*args, **kwargs)
-            CACHE[cache_key] = result
-            return result
+            result: pd.DataFrame = func(*args, **kwargs)
+            if result is not None and not result.empty:
+                d = {"expiry": time.time(), "data": result}
+                CACHE[cache_key] = d
+                return result
+
+            CACHE[cache_key]["expiry"] += TTL
+            return CACHE[cache_key]["data"]
 
         return wrapper
 
     return decorator
+
+
+if __name__ == '__main__':
+    a = time.time()
+    time.sleep(5)
+    print(time.time() - a)