envs

helpers.py

@@ -305,102 +305,103 @@ class Examples:
         """
         Caches all of the examples so that their predictions can be shown immediately.
         """
-        if Context.root_block is None:
-            raise ValueError("Cannot cache examples if not in a Blocks context")
-        if Path(self.cached_file).exists():
-            print(
-                f"Using cache from '{utils.abspath(self.cached_folder)}' directory. If method or examples have changed since last caching, delete this folder to clear cache.\n"
-            )
-        else:
-            print(f"Caching examples at: '{utils.abspath(self.cached_folder)}'")
-            cache_logger = CSVLogger()
-
-            generated_values = []
-            if inspect.isgeneratorfunction(self.fn):
-
-                def get_final_item(*args):  # type: ignore
-                    x = None
-                    generated_values.clear()
-                    for x in self.fn(*args):  # noqa: B007  # type: ignore
-                        generated_values.append(x)
-                    return x
-
-                fn = get_final_item
-            elif inspect.isasyncgenfunction(self.fn):
-
-                async def get_final_item(*args):
-                    x = None
-                    generated_values.clear()
-                    async for x in self.fn(*args):  # noqa: B007  # type: ignore
-                        generated_values.append(x)
-                    return x
-
-                fn = get_final_item
-            else:
-                fn = self.fn
-
-            # create a fake dependency to process the examples and get the predictions
-            from gradio.events import EventListenerMethod
-
-            dependency, fn_index = Context.root_block.set_event_trigger(
-                [EventListenerMethod(Context.root_block, "load")],
-                fn=fn,
-                inputs=self.inputs_with_examples,  # type: ignore
-                outputs=self.outputs,  # type: ignore
-                preprocess=self.preprocess and not self._api_mode,
-                postprocess=self.postprocess and not self._api_mode,
-                batch=self.batch,
-            )
-
-            assert self.outputs is not None
-            cache_logger.setup(self.outputs, self.cached_folder)
-            for example_id, _ in enumerate(self.examples):
-                print(f"Caching example {example_id + 1}/{len(self.examples)}")
-                processed_input = self.processed_examples[example_id]
-                if self.batch:
-                    processed_input = [[value] for value in processed_input]
-                with utils.MatplotlibBackendMananger():
-                    prediction = await Context.root_block.process_api(
-                        fn_index=fn_index,
-                        inputs=processed_input,
-                        request=None,
-                    )
-                output = prediction["data"]
-                if len(generated_values):
-                    output = merge_generated_values_into_output(
-                        self.outputs, generated_values, output
-                    )
-
-                if self.batch:
-                    output = [value[0] for value in output]
-                cache_logger.flag(output)
-            # Remove the "fake_event" to prevent bugs in loading interfaces from spaces
-            Context.root_block.dependencies.remove(dependency)
-            Context.root_block.fns.pop(fn_index)
-
-        # Remove the original load_input_event and replace it with one that
-        # also populates the input. We do it this way to to allow the cache()
-        # method to be called independently of the create() method
-        index = Context.root_block.dependencies.index(self.load_input_event)
-        Context.root_block.dependencies.pop(index)
-        Context.root_block.fns.pop(index)
-
-        def load_example(example_id):
-            processed_example = self.non_none_processed_examples[
-                example_id
-            ] + self.load_from_cache(example_id)
-            return utils.resolve_singleton(processed_example)
-
-        self.load_input_event = self.dataset.click(
-            load_example,
-            inputs=[self.dataset],
-            outputs=self.inputs_with_examples + self.outputs,  # type: ignore
-            show_progress="hidden",
-            postprocess=False,
-            queue=False,
-            api_name=self.api_name,  # type: ignore
-        )
+        # if Context.root_block is None:
+        #     raise ValueError("Cannot cache examples if not in a Blocks context")
+        # if Path(self.cached_file).exists():
+        #     print(
+        #         f"Using cache from '{utils.abspath(self.cached_folder)}' directory. If method or examples have changed since last caching, delete this folder to clear cache.\n"
+        #     )
+        # else:
+        #     print(f"Caching examples at: '{utils.abspath(self.cached_folder)}'")
+        #     cache_logger = CSVLogger()
+
+        #     generated_values = []
+        #     if inspect.isgeneratorfunction(self.fn):
+
+        #         def get_final_item(*args):  # type: ignore
+        #             x = None
+        #             generated_values.clear()
+        #             for x in self.fn(*args):  # noqa: B007  # type: ignore
+        #                 generated_values.append(x)
+        #             return x
+
+        #         fn = get_final_item
+        #     elif inspect.isasyncgenfunction(self.fn):
+
+        #         async def get_final_item(*args):
+        #             x = None
+        #             generated_values.clear()
+        #             async for x in self.fn(*args):  # noqa: B007  # type: ignore
+        #                 generated_values.append(x)
+        #             return x
+
+        #         fn = get_final_item
+        #     else:
+        #         fn = self.fn
+
+        #     # create a fake dependency to process the examples and get the predictions
+        #     from gradio.events import EventListenerMethod
+
+        #     dependency, fn_index = Context.root_block.set_event_trigger(
+        #         [EventListenerMethod(Context.root_block, "load")],
+        #         fn=fn,
+        #         inputs=self.inputs_with_examples,  # type: ignore
+        #         outputs=self.outputs,  # type: ignore
+        #         preprocess=self.preprocess and not self._api_mode,
+        #         postprocess=self.postprocess and not self._api_mode,
+        #         batch=self.batch,
+        #     )
 
+        #     assert self.outputs is not None
+        #     cache_logger.setup(self.outputs, self.cached_folder)
+        #     for example_id, _ in enumerate(self.examples):
+        #         print(f"Caching example {example_id + 1}/{len(self.examples)}")
+        #         processed_input = self.processed_examples[example_id]
+        #         if self.batch:
+        #             processed_input = [[value] for value in processed_input]
+        #         with utils.MatplotlibBackendMananger():
+        #             prediction = await Context.root_block.process_api(
+        #                 fn_index=fn_index,
+        #                 inputs=processed_input,
+        #                 request=None,
+        #             )
+        #         output = prediction["data"]
+        #         if len(generated_values):
+        #             output = merge_generated_values_into_output(
+        #                 self.outputs, generated_values, output
+        #             )
+
+        #         if self.batch:
+        #             output = [value[0] for value in output]
+        #         cache_logger.flag(output)
+        #     # Remove the "fake_event" to prevent bugs in loading interfaces from spaces
+        #     Context.root_block.dependencies.remove(dependency)
+        #     Context.root_block.fns.pop(fn_index)
+
+        # # Remove the original load_input_event and replace it with one that
+        # # also populates the input. We do it this way to to allow the cache()
+        # # method to be called independently of the create() method
+        # index = Context.root_block.dependencies.index(self.load_input_event)
+        # Context.root_block.dependencies.pop(index)
+        # Context.root_block.fns.pop(index)
+
+        # def load_example(example_id):
+        #     processed_example = self.non_none_processed_examples[
+        #         example_id
+        #     ] + self.load_from_cache(example_id)
+        #     return utils.resolve_singleton(processed_example)
+
+        # self.load_input_event = self.dataset.click(
+        #     load_example,
+        #     inputs=[self.dataset],
+        #     outputs=self.inputs_with_examples + self.outputs,  # type: ignore
+        #     show_progress="hidden",
+        #     postprocess=False,
+        #     queue=False,
+        #     api_name=self.api_name,  # type: ignore
+        # )
+        pass 
+    
     def load_from_cache(self, example_id: int) -> list[Any]:
         """Loads a particular cached example for the interface.
         Parameters: