modify preprocess to use pydantic models (#6181)

* modify preprocess to use pydantic models

* changes

* add changeset

* fix

* fix

* fix typing

* save

* revert queuing changes

* fix

* fix

* notebook

* fix

* changes

* add changeset

* fix functional tests

---------

Co-authored-by: gradio-pr-bot <gradio-pr-bot@users.noreply.github.com>

.changeset/short-doodles-lose.md

@@ -0,0 +1,6 @@
+---
+"@gradio/uploadbutton": minor
+"gradio": minor
+---
+
+feat:modify preprocess to use pydantic models

demo/chatbot_multimodal/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: chatbot_multimodal"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/chatbot_multimodal/avatar.png"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import os\n", "import time\n", "\n", "# Chatbot demo with multimodal input (text, markdown, LaTeX, code blocks, image, audio, & video). Plus shows support for streaming text.\n", "\n", "\n", "def add_text(history, text):\n", "    history = history + [(text, None)]\n", "    return history, gr.Textbox(value=\"\", interactive=False)\n", "\n", "\n", "def add_file(history, file):\n", "    history = history + [((file.name,), None)]\n", "    return history\n", "\n", "\n", "def bot(history):\n", "    response = \"**That's cool!**\"\n", "    history[-1][1] = \"\"\n", "    for character in response:\n", "        history[-1][1] += character\n", "        time.sleep(0.05)\n", "        yield history\n", "\n", "\n", "with gr.Blocks() as demo:\n", "    chatbot = gr.Chatbot(\n", "        [],\n", "        elem_id=\"chatbot\",\n", "        bubble_full_width=False,\n", "        avatar_images=(None, (os.path.join(os.path.abspath(''), \"avatar.png\"))),\n", "    )\n", "\n", "    with gr.Row():\n", "        txt = gr.Textbox(\n", "            scale=4,\n", "            show_label=False,\n", "            placeholder=\"Enter text and press enter, or upload an image\",\n", "            container=False,\n", "        )\n", "        btn = gr.UploadButton(\"\ud83d\udcc1\", file_types=[\"image\", \"video\", \"audio\"])\n", "\n", "    txt_msg = txt.submit(add_text, [chatbot, txt], [chatbot, txt], queue=False).then(\n", "        bot, chatbot, chatbot, api_name=\"bot_response\"\n", "    )\n", "    txt_msg.then(lambda: gr.Textbox(interactive=True), None, [txt], queue=False)\n", "    file_msg = btn.upload(add_file, [chatbot, btn], [chatbot], queue=False).then(\n", "        bot, chatbot, chatbot\n", "    )\n", "\n", "demo.queue()\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: chatbot_multimodal"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/chatbot_multimodal/avatar.png"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import os\n", "import time\n", "\n", "# Chatbot demo with multimodal input (text, markdown, LaTeX, code blocks, image, audio, & video). Plus shows support for streaming text.\n", "\n", "\n", "def add_text(history, text):\n", "    history = history + [(text, None)]\n", "    return history, gr.Textbox(value=\"\", interactive=False)\n", "\n", "\n", "def add_file(history, file):\n", "    history = history + [((file.name,), None)]\n", "    return history\n", "\n", "\n", "def bot(history):\n", "    response = \"**That's cool!**\"\n", "    history[-1][1] = \"\"\n", "    for character in response:\n", "        history[-1][1] += character\n", "        time.sleep(0.05)\n", "        yield history\n", "\n", "\n", "with gr.Blocks() as demo:\n", "    chatbot = gr.Chatbot(\n", "        [],\n", "        elem_id=\"chatbot\",\n", "        bubble_full_width=False,\n", "        avatar_images=(None, (os.path.join(os.path.abspath(''), \"avatar.png\"))),\n", "    )\n", "\n", "    with gr.Row():\n", "        txt = gr.Textbox(\n", "            scale=4,\n", "            show_label=False,\n", "            placeholder=\"Enter text and press enter, or upload an image\",\n", "            container=False,\n", "        )\n", "        btn = gr.UploadButton(\"\ud83d\udcc1\", file_types=[\"image\", \"video\", \"audio\"])\n", "\n", "    txt_msg = txt.submit(add_text, [chatbot, txt], [chatbot, txt], queue=False).then(\n", "        bot, chatbot, chatbot, api_name=\"bot_response\"\n", "    )\n", "    txt_msg.then(lambda: gr.Textbox(interactive=True), None, [txt], queue=False)\n", "    file_msg = btn.upload(add_file, [chatbot, btn], [chatbot], queue=False).then(\n", "        bot, chatbot, chatbot\n", "    )\n", "\n", "demo.queue()\n", "if __name__ == \"__main__\":\n", "    demo.launch(allowed_paths=[\"avatar.png\"])\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/chatbot_multimodal/run.py

@@ -51,4 +51,4 @@ def bot(history):
 
 demo.queue()
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch(allowed_paths=["avatar.png"])

demo/clear_components/run.py

@@ -153,6 +153,7 @@ def evaluate_values(*args):
             are_false.append(a == "#000000")
         else:
             are_false.append(not a)
+    print(args)
     return all(are_false)
 
 

gradio/blocks.py

@@ -341,7 +341,7 @@ def __init__(
         self.postprocess = postprocess
         self.tracks_progress = tracks_progress
         self.concurrency_limit = concurrency_limit
-        self.concurrency_id = concurrency_id or id(fn)
+        self.concurrency_id = concurrency_id or str(id(fn))
         self.batch = batch
         self.max_batch_size = max_batch_size
         self.total_runtime = 0
@@ -1260,6 +1260,14 @@ def preprocess_data(
                     inputs_cached = processing_utils.move_files_to_cache(
                         inputs[i], block
                     )
+                    if getattr(block, "data_model", None) and inputs_cached is not None:
+                        if issubclass(block.data_model, GradioModel):  # type: ignore
+                            print("block.data_model", block.data_model, block)
+                            print("1inputs_cached", inputs_cached)
+                            inputs_cached = block.data_model(**inputs_cached)  # type: ignore
+                        elif issubclass(block.data_model, GradioRootModel):  # type: ignore
+                            print("2inputs_cached", inputs_cached)
+                            inputs_cached = block.data_model(root=inputs_cached)  # type: ignore
                     processed_input.append(block.preprocess(inputs_cached))
         else:
             processed_input = inputs

gradio/components/annotated_image.py

@@ -103,20 +103,21 @@ def __init__(
 
     def postprocess(
         self,
-        y: tuple[
+        value: tuple[
             np.ndarray | _Image.Image | str,
             list[tuple[np.ndarray | tuple[int, int, int, int], str]],
-        ],
+        ]
+        | None,
     ) -> AnnotatedImageData | None:
         """
         Parameters:
-            y: Tuple of base image and list of subsections, with each subsection a two-part tuple where the first element is a 4 element bounding box or a 0-1 confidence mask, and the second element is the label.
+            value: Tuple of base image and list of subsections, with each subsection a two-part tuple where the first element is a 4 element bounding box or a 0-1 confidence mask, and the second element is the label.
         Returns:
             Tuple of base image file and list of subsections, with each subsection a two-part tuple where the first element image path of the mask, and the second element is the label.
         """
-        if y is None:
+        if value is None:
             return None
-        base_img = y[0]
+        base_img = value[0]
         if isinstance(base_img, str):
             base_img_path = base_img
             base_img = np.array(_Image.open(base_img))
@@ -144,7 +145,7 @@ def hex_to_rgb(value):
             lv = len(value)
             return [int(value[i : i + lv // 3], 16) for i in range(0, lv, lv // 3)]
 
-        for mask, label in y[1]:
+        for mask, label in value[1]:
             mask_array = np.zeros((base_img.shape[0], base_img.shape[1]))
             if isinstance(mask, np.ndarray):
                 mask_array = mask
@@ -188,5 +189,7 @@ def hex_to_rgb(value):
     def example_inputs(self) -> Any:
         return {}
 
-    def preprocess(self, x: Any) -> Any:
-        return x
+    def preprocess(
+        self, payload: AnnotatedImageData | None
+    ) -> AnnotatedImageData | None:
+        return payload

gradio/components/audio.py

@@ -162,20 +162,12 @@ def example_inputs(self) -> Any:
         return "https://github.com/gradio-app/gradio/raw/main/test/test_files/audio_sample.wav"
 
     def preprocess(
-        self, x: dict[str, Any] | None
+        self, payload: FileData | None
     ) -> tuple[int, np.ndarray] | str | None:
-        """
-        Parameters:
-            x: dictionary with keys "path", "crop_min", "crop_max".
-        Returns:
-            audio in requested format
-        """
-        if x is None:
-            return x
+        if payload is None:
+            return payload
 
-        payload: FileData = FileData(**x)
         assert payload.path
-
         # Need a unique name for the file to avoid re-using the same audio file if
         # a user submits the same audio file twice
         temp_file_path = Path(payload.path)
@@ -211,50 +203,50 @@ def preprocess(
             )
 
     def postprocess(
-        self, y: tuple[int, np.ndarray] | str | Path | bytes | None
-    ) -> FileData | None | bytes:
+        self, value: tuple[int, np.ndarray] | str | Path | bytes | None
+    ) -> FileData | bytes | None:
         """
         Parameters:
-            y: audio data in either of the following formats: a tuple of (sample_rate, data), or a string filepath or URL to an audio file, or None.
+            value: audio data in either of the following formats: a tuple of (sample_rate, data), or a string filepath or URL to an audio file, or None.
         Returns:
             base64 url data
         """
-        if y is None:
+        if value is None:
             return None
-        if isinstance(y, bytes):
+        if isinstance(value, bytes):
             if self.streaming:
-                return y
+                return value
             file_path = processing_utils.save_bytes_to_cache(
-                y, "audio", cache_dir=self.GRADIO_CACHE
+                value, "audio", cache_dir=self.GRADIO_CACHE
             )
-        elif isinstance(y, tuple):
-            sample_rate, data = y
+        elif isinstance(value, tuple):
+            sample_rate, data = value
             file_path = processing_utils.save_audio_to_cache(
                 data, sample_rate, format=self.format, cache_dir=self.GRADIO_CACHE
             )
         else:
-            if not isinstance(y, (str, Path)):
-                raise ValueError(f"Cannot process {y} as Audio")
-            file_path = str(y)
+            if not isinstance(value, (str, Path)):
+                raise ValueError(f"Cannot process {value} as Audio")
+            file_path = str(value)
         return FileData(path=file_path)
 
     def stream_output(
-        self, y, output_id: str, first_chunk: bool
+        self, value, output_id: str, first_chunk: bool
     ) -> tuple[bytes | None, Any]:
         output_file = {
             "path": output_id,
             "is_stream": True,
         }
-        if y is None:
+        if value is None:
             return None, output_file
-        if isinstance(y, bytes):
-            return y, output_file
-        if client_utils.is_http_url_like(y["path"]):
-            response = requests.get(y["path"])
+        if isinstance(value, bytes):
+            return value, output_file
+        if client_utils.is_http_url_like(value["path"]):
+            response = requests.get(value["path"])
             binary_data = response.content
         else:
-            output_file["orig_name"] = y["orig_name"]
-            file_path = y["path"]
+            output_file["orig_name"] = value["orig_name"]
+            file_path = value["path"]
             is_wav = file_path.endswith(".wav")
             with open(file_path, "rb") as f:
                 binary_data = f.read()

gradio/components/bar_plot.py

@@ -258,15 +258,15 @@ def create_plot(
         return chart
 
     def postprocess(
-        self, y: pd.DataFrame | dict | None
+        self, value: pd.DataFrame | dict | None
     ) -> AltairPlotData | dict | None:
         # if None or update
-        if y is None or isinstance(y, dict):
-            return y
+        if value is None or isinstance(value, dict):
+            return value
         if self.x is None or self.y is None:
             raise ValueError("No value provided for required parameters `x` and `y`.")
         chart = self.create_plot(
-            value=y,
+            value=value,
             x=self.x,
             y=self.y,
             color=self.color,
@@ -288,12 +288,10 @@ def postprocess(
             sort=self.sort,  # type: ignore
         )
 
-        return AltairPlotData(
-            **{"type": "altair", "plot": chart.to_json(), "chart": "bar"}
-        )
+        return AltairPlotData(type="altair", plot=chart.to_json(), chart="bar")
 
     def example_inputs(self) -> dict[str, Any]:
         return {}
 
-    def preprocess(self, x: Any) -> Any:
-        return x
+    def preprocess(self, payload: AltairPlotData) -> AltairPlotData:
+        return payload

gradio/components/base.py

@@ -48,21 +48,21 @@ class ComponentBase(ABC, metaclass=ComponentMeta):
     EVENTS: list[EventListener | str] = []
 
     @abstractmethod
-    def preprocess(self, x: Any) -> Any:
+    def preprocess(self, payload: Any) -> Any:
         """
         Any preprocessing needed to be performed on function input.
         """
-        return x
+        return payload
 
     @abstractmethod
-    def postprocess(self, y):
+    def postprocess(self, value):
         """
         Any postprocessing needed to be performed on function output.
         """
-        return y
+        return value
 
     @abstractmethod
-    def as_example(self, y):
+    def as_example(self, value):
         """
         Return the input data in a way that can be displayed by the examples dataset component in the front-end.
 
@@ -88,7 +88,7 @@ def example_inputs(self) -> Any:
         pass
 
     @abstractmethod
-    def flag(self, x: Any | GradioDataModel, flag_dir: str | Path = "") -> str:
+    def flag(self, payload: Any | GradioDataModel, flag_dir: str | Path = "") -> str:
         """
         Write the component's value to a format that can be stored in a csv or jsonl format for flagging.
         """
@@ -97,13 +97,13 @@ def flag(self, x: Any | GradioDataModel, flag_dir: str | Path = "") -> str:
     @abstractmethod
     def read_from_flag(
         self,
-        x: Any,
+        payload: Any,
         flag_dir: str | Path | None = None,
     ) -> GradioDataModel | Any:
         """
         Convert the data from the csv or jsonl file into the component state.
         """
-        return x
+        return payload
 
     @property
     @abstractmethod
@@ -267,26 +267,26 @@ def api_info(self) -> dict[str, Any]:
             f"The api_info method has not been implemented for {self.get_block_name()}"
         )
 
-    def flag(self, x: Any, flag_dir: str | Path = "") -> str:
+    def flag(self, payload: Any, flag_dir: str | Path = "") -> str:
         """
         Write the component's value to a format that can be stored in a csv or jsonl format for flagging.
         """
         if self.data_model:
-            x = self.data_model.from_json(x)
-            return x.copy_to_dir(flag_dir).model_dump_json()
-        return x
+            payload = self.data_model.from_json(payload)
+            return payload.copy_to_dir(flag_dir).model_dump_json()
+        return payload
 
     def read_from_flag(
         self,
-        x: Any,
+        payload: Any,
         flag_dir: str | Path | None = None,
     ):
         """
         Convert the data from the csv or jsonl file into the component state.
         """
         if self.data_model:
-            return self.data_model.from_json(json.loads(x))
-        return x
+            return self.data_model.from_json(json.loads(payload))
+        return payload
 
 
 class FormComponent(Component):
@@ -295,11 +295,11 @@ def get_expected_parent(self) -> type[Form] | None:
             return None
         return Form
 
-    def preprocess(self, x: Any) -> Any:
-        return x
+    def preprocess(self, payload: Any) -> Any:
+        return payload
 
-    def postprocess(self, y):
-        return y
+    def postprocess(self, value):
+        return value
 
 
 class StreamingOutput(metaclass=abc.ABCMeta):
@@ -308,7 +308,9 @@ def __init__(self, *args, **kwargs) -> None:
         self.streaming: bool
 
     @abc.abstractmethod
-    def stream_output(self, y, output_id: str, first_chunk: bool) -> tuple[bytes, Any]:
+    def stream_output(
+        self, value, output_id: str, first_chunk: bool
+    ) -> tuple[bytes, Any]:
         pass
 
 

gradio/components/button.py

@@ -77,11 +77,11 @@ def __init__(
     def skip_api(self):
         return True
 
-    def preprocess(self, x: Any) -> Any:
-        return x
+    def preprocess(self, payload: str) -> str:
+        return payload
 
-    def postprocess(self, y):
-        return y
+    def postprocess(self, value: str) -> str:
+        return value
 
     def example_inputs(self) -> Any:
         return None