""" This file defines a useful high-level abstraction to build Gradio chatbots: ChatInterface. """ from __future__ import annotations import builtins import copy import dataclasses import inspect import os import warnings from collections.abc import AsyncGenerator, Callable, Generator from functools import wraps from typing import Any, Literal, Union, cast from anyio.to_thread import run_sync from gradio_client.documentation import document from gradio import utils from gradio.blocks import Blocks from gradio.components import ( JSON, BrowserState, Button, Chatbot, Component, Dataset, Markdown, MultimodalTextbox, State, Textbox, get_component_instance, ) from gradio.components.chatbot import ( ChatMessage, ExampleMessage, Message, MessageDict, NormalizedMessageDict, ) from gradio.components.multimodal_textbox import MultimodalPostprocess, MultimodalValue from gradio.events import Dependency, EditData, SelectData from gradio.flagging import ChatCSVLogger from gradio.helpers import create_examples as Examples # noqa: N812 from gradio.helpers import special_args, update from gradio.i18n import I18nData from gradio.layouts import Accordion, Column, Group, Row @document() class ChatInterface(Blocks): """ ChatInterface is Gradio's high-level abstraction for creating chatbot UIs, and allows you to create a web-based demo around a chatbot model in a few lines of code. Only one parameter is required: fn, which takes a function that governs the response of the chatbot based on the user input and chat history. Additional parameters can be used to control the appearance and behavior of the demo. Example: import gradio as gr def echo(message, history): return message demo = gr.ChatInterface(fn=echo, examples=[{"text": "hello", "text": "hola", "text": "merhaba"}], title="Echo Bot") demo.launch() Demos: chatinterface_random_response, chatinterface_streaming_echo, chatinterface_artifacts Guides: creating-a-chatbot-fast, sharing-your-app """ def __init__( self, fn: Callable, *, multimodal: bool = False, chatbot: Chatbot | None = None, textbox: Textbox | MultimodalTextbox | None = None, additional_inputs: str | Component | list[str | Component] | None = None, additional_inputs_accordion: str | Accordion | None = None, additional_outputs: Component | list[Component] | None = None, editable: bool = False, examples: list[str] | list[MultimodalValue] | list[list] | None = None, example_labels: list[str] | None = None, example_icons: list[str] | None = None, run_examples_on_click: bool = True, cache_examples: bool | None = None, cache_mode: Literal["eager", "lazy"] | None = None, title: str | I18nData | None = None, description: str | None = None, flagging_mode: Literal["never", "manual"] | None = None, flagging_options: list[str] | tuple[str, ...] | None = ("Like", "Dislike"), flagging_dir: str = ".gradio/flagged", analytics_enabled: bool | None = None, autofocus: bool = True, autoscroll: bool = True, submit_btn: str | bool | None = True, stop_btn: str | bool | None = True, concurrency_limit: int | None | Literal["default"] = "default", delete_cache: tuple[int, int] | None = None, show_progress: Literal["full", "minimal", "hidden"] = "minimal", fill_height: bool = True, fill_width: bool = False, api_name: str | None = None, api_description: str | None | Literal[False] = None, api_visibility: Literal["public", "private", "undocumented"] = "public", save_history: bool = False, validator: Callable | None = None, ): """ Parameters: fn: the function to wrap the chat interface around. The function should accept two parameters: a `str` representing the input message and `list` of openai-style dictionaries: {"role": "user" | "assistant", "content": `str` | {"path": `str`} | `gr.Component`} representing the chat history. The function should return/yield a `str` (for a simple message), a supported Gradio component (e.g. gr.Image to return an image), a `dict` (for a complete openai-style message response), or a `list` of such messages. multimodal: if True, the chat interface will use a `gr.MultimodalTextbox` component for the input, which allows for the uploading of multimedia files. If False, the chat interface will use a gr.Textbox component for the input. If this is True, the first argument of `fn` should accept not a `str` message but a `dict` message with keys "text" and "files" chatbot: an instance of the gr.Chatbot component to use for the chat interface, if you would like to customize the chatbot properties. If not provided, a default gr.Chatbot component will be created. textbox: an instance of the gr.Textbox or gr.MultimodalTextbox component to use for the chat interface, if you would like to customize the textbox properties. If not provided, a default gr.Textbox or gr.MultimodalTextbox component will be created. editable: if True, users can edit past messages to regenerate responses. additional_inputs: an instance or list of instances of gradio components (or their string shortcuts) to use as additional inputs to the chatbot. If the components are not already rendered in a surrounding Blocks, then the components will be displayed under the chatbot, in an accordion. The values of these components will be passed into `fn` as arguments in order after the chat history. additional_inputs_accordion: if a string is provided, this is the label of the `gr.Accordion` to use to contain additional inputs. A `gr.Accordion` object can be provided as well to configure other properties of the container holding the additional inputs. Defaults to a `gr.Accordion(label="Additional Inputs", open=False)`. This parameter is only used if `additional_inputs` is provided. additional_outputs: an instance or list of instances of gradio components to use as additional outputs from the chat function. These must be components that are already defined in the same Blocks scope. If provided, the chat function should return additional values for these components. See $demo/chatinterface_artifacts. examples: sample inputs for the function; if provided, appear within the chatbot and can be clicked to populate the chatbot input. Should be a list of strings representing text-only examples, or a list of dictionaries (with keys `text` and `files`) representing multimodal examples. If `additional_inputs` are provided, the examples must be a list of lists, where the first element of each inner list is the string or dictionary example message and the remaining elements are the example values for the additional inputs -- in this case, the examples will appear under the chatbot. example_labels: labels for the examples, to be displayed instead of the examples themselves. If provided, should be a list of strings with the same length as the examples list. Only applies when examples are displayed within the chatbot (i.e. when `additional_inputs` is not provided). example_icons: icons for the examples, to be displayed above the examples. If provided, should be a list of string URLs or local paths with the same length as the examples list. Only applies when examples are displayed within the chatbot (i.e. when `additional_inputs` is not provided). cache_examples: if True, caches examples in the server for fast runtime in examples. The default option in HuggingFace Spaces is True. The default option elsewhere is False. Note that examples are cached separately from Gradio's queue() so certain features, such as gr.Progress(), gr.Info(), gr.Warning(), etc. will not be displayed in Gradio's UI for cached examples. cache_mode: if "eager", all examples are cached at app launch. If "lazy", examples are cached for all users after the first use by any user of the app. If None, will use the GRADIO_CACHE_MODE environment variable if defined, or default to "eager". run_examples_on_click: if True, clicking on an example will run the example through the chatbot fn and the response will be displayed in the chatbot. If False, clicking on an example will only populate the chatbot input with the example message. Has no effect if `cache_examples` is True title: a title for the interface; if provided, appears above chatbot in large font. Also used as the tab title when opened in a browser window. description: a description for the interface; if provided, appears above the chatbot and beneath the title in regular font. Accepts Markdown and HTML content. flagging_mode: one of "never", "manual". If "never", users will not see a button to flag an input and output. If "manual", users will see a button to flag. flagging_options: a list of strings representing the options that users can choose from when flagging a message. Defaults to ["Like", "Dislike"]. These two case-sensitive strings will render as "thumbs up" and "thumbs down" icon respectively next to each bot message, but any other strings appear under a separate flag icon. flagging_dir: path to the the directory where flagged data is stored. If the directory does not exist, it will be created. analytics_enabled: whether to allow basic telemetry. If None, will use GRADIO_ANALYTICS_ENABLED environment variable if defined, or default to True. autofocus: if True, autofocuses to the textbox when the page loads. autoscroll: If True, will automatically scroll to the bottom of the chatbot when a new message appears, unless the user scrolls up. If False, will not scroll to the bottom of the chatbot automatically. submit_btn: If True, will show a submit button with a submit icon within the textbox. If a string, will use that string as the submit button text in place of the icon. If False, will not show a submit button. stop_btn: If True, will show a button with a stop icon during generator executions, to stop generating. If a string, will use that string as the submit button text in place of the stop icon. If False, will not show a stop button. concurrency_limit: if set, this is the maximum number of chatbot submissions that can be running simultaneously. Can be set to None to mean no limit (any number of chatbot submissions can be running simultaneously). Set to "default" to use the default concurrency limit (defined by the `default_concurrency_limit` parameter in `.queue()`, which is 1 by default). delete_cache: a tuple corresponding [frequency, age] both expressed in number of seconds. Every `frequency` seconds, the temporary files created by this Blocks instance will be deleted if more than `age` seconds have passed since the file was created. For example, setting this to (86400, 86400) will delete temporary files every day. The cache will be deleted entirely when the server restarts. If None, no cache deletion will occur. show_progress: how to show the progress animation while event is running: "full" shows a spinner which covers the output component area as well as a runtime display in the upper right corner, "minimal" only shows the runtime display, "hidden" shows no progress animation at all fill_height: if True, the chat interface will expand to the height of window. fill_width: Whether to horizontally expand to fill container fully. If False, centers and constrains app to a maximum width. api_name: defines how the chat endpoint appears in the API docs. Can be a string or None. If set to a string, the endpoint will be exposed in the API docs with the given name. If None, the name of the function will be used. api_description: Description of the API endpoint. Can be a string, None, or False. If set to a string, the endpoint will be exposed in the API docs with the given description. If None, the function's docstring will be used as the API endpoint description. If False, then no description will be displayed in the API docs. api_visibility: Controls the visibility of the chat endpoint. Can be "public" (shown in API docs and callable), "private" (hidden from API docs and not callable), or "undocumented" (hidden from API docs but callable). save_history: if True, will save the chat history to the browser's local storage and display previous conversations in a side panel. validator: a function that takes in the inputs and can optionally return a gr.validate() object for each input. """ super().__init__( analytics_enabled=analytics_enabled, mode="chat_interface", title=title or "Gradio", fill_height=fill_height, fill_width=fill_width, delete_cache=delete_cache, ) self.api_name: str | None = api_name self.api_description: str | None | Literal[False] = api_description self.api_visibility = api_visibility self.multimodal = multimodal self.concurrency_limit = concurrency_limit if isinstance(fn, ChatInterface): self.fn = fn.fn else: self.fn = fn self.is_async = inspect.iscoroutinefunction( self.fn ) or inspect.isasyncgenfunction(self.fn) self.is_generator = inspect.isgeneratorfunction( self.fn ) or inspect.isasyncgenfunction(self.fn) self.validator = validator self.provided_chatbot = chatbot is not None self.examples = examples self.examples_messages = self._setup_example_messages( examples, example_labels, example_icons ) self.run_examples_on_click = run_examples_on_click self.cache_examples = cache_examples self.cache_mode = cache_mode self.editable = editable self.fill_height = fill_height self.autoscroll = autoscroll self.autofocus = autofocus self.title = title self.description = description self.show_progress = show_progress self.save_history = save_history self.additional_inputs = [ get_component_instance(i) for i in utils.none_or_singleton_to_list(additional_inputs) ] self.additional_outputs = utils.none_or_singleton_to_list(additional_outputs) if additional_inputs_accordion is None: self.additional_inputs_accordion_params = { "label": I18nData("chat_interface.additional_inputs"), "open": False, } elif isinstance(additional_inputs_accordion, str): self.additional_inputs_accordion_params = { "label": additional_inputs_accordion } elif isinstance(additional_inputs_accordion, Accordion): self.additional_inputs_accordion_params = ( additional_inputs_accordion.recover_kwargs( additional_inputs_accordion.get_config() ) ) else: raise ValueError( f"The `additional_inputs_accordion` parameter must be a string or gr.Accordion, not {builtins.type(additional_inputs_accordion)}" ) self._additional_inputs_in_examples = False if self.additional_inputs and self.examples is not None: for example in self.examples: if not isinstance(example, list): raise ValueError( "Examples must be a list of lists when additional inputs are provided." ) for idx, example_for_input in enumerate(example): if example_for_input is not None and idx > 0: self._additional_inputs_in_examples = True break if self._additional_inputs_in_examples: break if flagging_mode is None: flagging_mode = os.getenv("GRADIO_CHAT_FLAGGING_MODE", "never") # type: ignore if flagging_mode in ["manual", "never"]: self.flagging_mode = flagging_mode else: raise ValueError( "Invalid value for `flagging_mode` parameter." "Must be: 'manual' or 'never'." ) self.flagging_options = flagging_options self.flagging_dir = flagging_dir if isinstance(textbox, (Textbox, MultimodalTextbox)): textbox.unrender() if isinstance(chatbot, Chatbot): chatbot.unrender() with self: self.saved_conversations = BrowserState( [], storage_key=f"_saved_conversations_{self._id}" ) self.conversation_id = State(None) self.saved_input = State() # Stores the most recent user message self.null_component = State() # Used to discard unneeded values with Column(): self._render_header() if self.save_history: with Row(scale=1): self._render_history_area() with Column(scale=6): self._render_chatbot_area( chatbot, textbox, submit_btn, stop_btn ) self._render_footer() else: self._render_chatbot_area(chatbot, textbox, submit_btn, stop_btn) self._render_footer() self._setup_events() if textbox is not None: conflicting_params = [] if isinstance(textbox, (Textbox, MultimodalTextbox)): if submit_btn is not True and submit_btn != textbox.submit_btn: conflicting_params.append("submit_btn") if stop_btn is not True and stop_btn != textbox.stop_btn: conflicting_params.append("stop_btn") if autofocus is not True and autofocus != textbox.autofocus: conflicting_params.append("autofocus") if conflicting_params: warnings.warn( f"You provided a custom `textbox` component, but also specified " f"{', '.join(f'`{p}`' for p in conflicting_params)} parameter(s) on `gr.ChatInterface`. " f"These ChatInterface parameters will be ignored. To customize these settings, " f"pass them directly to your `gr.Textbox` or `gr.MultimodalTextbox` component instead. " f"For example: textbox=gr.Textbox(..., submit_btn='submit')", UserWarning, stacklevel=2, ) def _render_header(self): if self.title: Markdown( f"

{self.title}

" ) if self.description: Markdown(self.description) def _render_history_area(self): with Column(scale=1, min_width=100): self.new_chat_button = Button( I18nData("chat_interface.new_chat"), variant="primary", size="md", icon=utils.get_icon_path("plus.svg"), # scale=0, ) self.chat_history_dataset = Dataset( components=[Textbox(visible=False)], show_label=False, layout="table", type="index", ) def _render_chatbot_area( self, chatbot: Chatbot | None, textbox: Textbox | MultimodalTextbox | None, submit_btn: str | bool | None, stop_btn: str | bool | None, ): if chatbot: self.chatbot = cast(Chatbot, get_component_instance(chatbot, render=True)) if self.chatbot.examples and self.examples_messages: warnings.warn( "The ChatInterface already has examples set. The examples provided in the chatbot will be ignored." ) self.chatbot.examples = ( self.examples_messages if not self._additional_inputs_in_examples else None ) self.chatbot._setup_examples() else: self.chatbot = Chatbot( label=I18nData("chat_interface.chatbot"), scale=1, height=400 if self.fill_height else None, autoscroll=self.autoscroll, examples=( self.examples_messages if not self._additional_inputs_in_examples else None ), ) with Group(): with Row(): if textbox: textbox.show_label = False textbox_ = get_component_instance(textbox, render=True) if not isinstance(textbox_, (Textbox, MultimodalTextbox)): raise TypeError( f"Expected a gr.Textbox or gr.MultimodalTextbox component, but got {builtins.type(textbox_)}" ) self.textbox = textbox_ else: textbox_component = ( MultimodalTextbox if self.multimodal else Textbox ) self.textbox = textbox_component( show_label=False, label="", placeholder=I18nData("chat_interface.message_placeholder"), scale=7, autofocus=self.autofocus, submit_btn=submit_btn, stop_btn=stop_btn, ) # Hide the stop button at the beginning, and show it with the given value during the generator execution. self.original_stop_btn = self.textbox.stop_btn self.textbox.stop_btn = False self.fake_api_btn = Button("Fake API", visible=False) self.api_response = JSON( label="Response", visible=False ) # Used to store the response from the API call # Used internally to store the chatbot value when it differs from the value displayed in the chatbot UI. # For example, when a user submits a message, the chatbot UI is immediately updated with the user message, # but the chatbot_state value is not updated until the submit_fn is called. self.chatbot_state = State(self.chatbot.value if self.chatbot.value else []) # Provided so that developers can update the chatbot value from other events outside of `gr.ChatInterface`. self.chatbot_value = State(self.chatbot.value if self.chatbot.value else []) def _render_footer(self): if self.examples: self.examples_handler = Examples( examples=self.examples, inputs=[self.textbox] + self.additional_inputs, outputs=self.chatbot, fn=self._examples_stream_fn if self.is_generator else self._examples_fn, cache_examples=self.cache_examples, cache_mode=cast(Literal["eager", "lazy"], self.cache_mode), visible=self._additional_inputs_in_examples, preprocess=self._additional_inputs_in_examples, preload=False, ) any_unrendered_inputs = any( not inp.is_rendered for inp in self.additional_inputs ) if self.additional_inputs and any_unrendered_inputs: with Accordion(**self.additional_inputs_accordion_params): # type: ignore for input_component in self.additional_inputs: if not input_component.is_rendered: input_component.render() def _setup_example_messages( self, examples: list[str] | list[MultimodalValue] | list[list] | None, example_labels: list[str] | None = None, example_icons: list[str] | None = None, ) -> list[ExampleMessage]: examples_messages = [] if examples: for index, example in enumerate(examples): if isinstance(example, list): example = example[0] example_message: ExampleMessage = {} if isinstance(example, str): example_message["text"] = example elif isinstance(example, dict): example_message["text"] = example.get("text", "") example_message["files"] = example.get("files", []) if example_labels: example_message["display_text"] = example_labels[index] if self.multimodal: example_files = example_message.get("files") if not example_files: if example_icons: example_message["icon"] = example_icons[index] else: example_message["icon"] = { "path": "", "url": None, "orig_name": None, "mime_type": "text", # for internal use, not a valid mime type "meta": {"_type": "gradio.FileData"}, } elif example_icons: example_message["icon"] = example_icons[index] examples_messages.append(example_message) return examples_messages def _generate_chat_title(self, conversation: list[NormalizedMessageDict]) -> str: """ Generate a title for a conversation by taking the first user message that is a string and truncating it to 40 characters. If files are present, add a 📎 to the title. """ title = "" for message in conversation: if message["role"] == "user": for content in message["content"]: if content["type"] == "text": title += content["text"] # type: ignore break else: title += "📎 " if len(title) > 40: title = title[:40] + "..." return title or str(I18nData("chat_interface.conversation")) @staticmethod def serialize_components( conversation: list[NormalizedMessageDict], ) -> list[NormalizedMessageDict]: def inner(obj: Any) -> Any: if isinstance(obj, list): return [inner(item) for item in obj] elif isinstance(obj, dict): return {k: inner(v) for k, v in obj.items()} elif isinstance(obj, Component): return obj.value return obj return inner(conversation) def _save_conversation( self, index: int | None, conversation: list[NormalizedMessageDict], saved_conversations: list[list[NormalizedMessageDict]], ): if self.save_history: serialized_conversation = self.serialize_components(conversation) if index is not None: saved_conversations[index] = serialized_conversation else: saved_conversations = saved_conversations or [] saved_conversations.insert(0, serialized_conversation) index = 0 return index, saved_conversations def _delete_conversation( self, index: int | None, saved_conversations: list[list[NormalizedMessageDict]], ): if index is not None: saved_conversations.pop(index) return None, saved_conversations def _load_chat_history(self, conversations): return Dataset( samples=[ [self._generate_chat_title(conv)] for conv in conversations or [] if conv ] ) def _load_conversation( self, index: int, conversations: list[list[NormalizedMessageDict]], ): return ( index, Chatbot( value=conversations[index], # type: ignore ), ) def _api_wrapper(self, fn, submit_fn): """Wrap the submit_fn in a way that preserves the signature of the original function. That way, the API page shows the same parameters as the original function. """ # Need two separate functions here because a `return` # statement can't be placed in an async generator function. # using different names because otherwise type checking complains if self.is_generator: @wraps(fn) async def _wrapper(*args, **kwargs): async for chunk in submit_fn(*args, **kwargs): yield chunk return _wrapper else: @wraps(fn) async def __wrapper(*args, **kwargs): return await submit_fn(*args, **kwargs) return __wrapper def _setup_events(self) -> None: from gradio import on submit_fn = self._stream_fn if self.is_generator else self._submit_fn submit_wrapped = self._api_wrapper(self.fn, submit_fn) # To not conflict with the api_name submit_wrapped.__name__ = "_submit_fn" api_fn = self._api_wrapper(self.fn, submit_fn) synchronize_chat_state_kwargs = { "fn": lambda x: (x, x), "inputs": [self.chatbot], "outputs": [self.chatbot_state, self.chatbot_value], "api_visibility": "undocumented", "queue": False, } submit_fn_kwargs = { "fn": submit_wrapped, "inputs": [self.saved_input, self.chatbot_state] + self.additional_inputs, "outputs": [self.null_component, self.chatbot] + self.additional_outputs, "api_visibility": "undocumented", "concurrency_limit": cast( Union[int, Literal["default"], None], self.concurrency_limit ), "show_progress": cast( Literal["full", "minimal", "hidden"], self.show_progress ), } save_fn_kwargs = { "fn": self._save_conversation, "inputs": [ self.conversation_id, self.chatbot_state, self.saved_conversations, ], "outputs": [self.conversation_id, self.saved_conversations], "api_visibility": "undocumented", "queue": False, } user_submit = self.textbox.submit( self._clear_and_save_textbox, [self.textbox], [self.textbox, self.saved_input], api_visibility="undocumented", queue=bool(self.validator), validator=self.validator, ) submit_event = user_submit.then( # The reason we do this outside of the submit_fn is that we want to update the chatbot UI with the user message immediately, before the submit_fn is called self._append_message_to_history, [self.saved_input, self.chatbot], [self.chatbot], api_visibility="undocumented", queue=False, ).then( **submit_fn_kwargs, ) submit_event.then(**synchronize_chat_state_kwargs).then( lambda: update(value=None, interactive=True), None, self.textbox, api_visibility="undocumented", ).then(**save_fn_kwargs) # Creates the "/chat" API endpoint self.fake_api_btn.click( api_fn, [self.textbox, self.chatbot_state] + self.additional_inputs, [self.api_response, self.chatbot_state] + self.additional_outputs, api_name=self.api_name, api_description=self.api_description, api_visibility=cast(Literal["public", "private"], self.api_visibility), concurrency_limit=cast( Union[int, Literal["default"], None], self.concurrency_limit ), postprocess=False, ) example_select_event = None if ( isinstance(self.chatbot, Chatbot) and self.examples and not self._additional_inputs_in_examples ): if self.cache_examples or self.run_examples_on_click: example_select_event = self.chatbot.example_select( self.example_clicked, None, [self.chatbot, self.saved_input], api_visibility="undocumented", ) if not self.cache_examples: example_select_event = example_select_event.then(**submit_fn_kwargs) example_select_event.then(**synchronize_chat_state_kwargs) else: example_select_event = self.chatbot.example_select( self.example_populated, None, [self.textbox], api_visibility="undocumented", ) retry_event = ( self.chatbot.retry( self._pop_last_user_message, [self.chatbot_state], [self.chatbot_state, self.saved_input], api_visibility="undocumented", queue=False, ) .then( self._append_message_to_history, [self.saved_input, self.chatbot_state], [self.chatbot], api_visibility="undocumented", queue=False, ) .then( lambda: update(interactive=False, placeholder=""), outputs=[self.textbox], api_visibility="undocumented", ) .then(**submit_fn_kwargs) ) retry_event.then(**synchronize_chat_state_kwargs).then( lambda: update(interactive=True), outputs=[self.textbox], api_visibility="undocumented", ).then(**save_fn_kwargs) events_to_cancel = [submit_event, retry_event] if example_select_event is not None: events_to_cancel.append(example_select_event) self._setup_stop_events( event_triggers=[ self.chatbot.retry, self.chatbot.example_select, ], events_to_cancel=events_to_cancel, after_success=user_submit, ) self.chatbot.undo( self._pop_last_user_message, [self.chatbot], [self.chatbot, self.textbox], api_visibility="undocumented", queue=False, ).then(**synchronize_chat_state_kwargs).then(**save_fn_kwargs) self.chatbot.option_select( self.option_clicked, [self.chatbot], [self.chatbot, self.saved_input], api_visibility="undocumented", ).then(**submit_fn_kwargs).then(**synchronize_chat_state_kwargs).then( **save_fn_kwargs ) self.chatbot.clear(**synchronize_chat_state_kwargs).then( # type: ignore self._delete_conversation, [self.conversation_id, self.saved_conversations], [self.conversation_id, self.saved_conversations], api_visibility="undocumented", queue=False, ) if self.editable: self.chatbot.edit( self._edit_message, [self.chatbot], [self.chatbot, self.chatbot_state, self.saved_input], api_visibility="undocumented", ).success(**submit_fn_kwargs).success(**synchronize_chat_state_kwargs).then( **save_fn_kwargs ) if self.save_history: self.new_chat_button.click( lambda: (None, []), None, [self.conversation_id, self.chatbot], api_visibility="undocumented", queue=False, ).then( lambda x: x, [self.chatbot], [self.chatbot_state], api_visibility="undocumented", queue=False, ) on( triggers=[self.load, self.saved_conversations.change], fn=self._load_chat_history, inputs=[self.saved_conversations], outputs=[self.chat_history_dataset], api_visibility="undocumented", queue=False, ) self.chat_history_dataset.click( lambda: [], None, [self.chatbot], api_visibility="undocumented", queue=False, show_progress="hidden", ).then( self._load_conversation, [self.chat_history_dataset, self.saved_conversations], [self.conversation_id, self.chatbot], api_visibility="undocumented", queue=False, show_progress="hidden", ).then(**synchronize_chat_state_kwargs) if self.flagging_mode != "never": flagging_callback = ChatCSVLogger() flagging_callback.setup(self.flagging_dir) self.chatbot.feedback_options = self.flagging_options self.chatbot.like(flagging_callback.flag, self.chatbot) self.chatbot_value.change( lambda x: x, [self.chatbot_value], [self.chatbot], api_visibility="undocumented", ).then(**synchronize_chat_state_kwargs) def _setup_stop_events( self, event_triggers: list[Callable], events_to_cancel: list[Dependency], after_success: Dependency, ) -> None: textbox_component = MultimodalTextbox if self.multimodal else Textbox original_submit_btn = self.textbox.submit_btn after_success.success( utils.async_lambda( lambda: textbox_component( submit_btn=False, stop_btn=self.original_stop_btn, ) ), None, [self.textbox], api_visibility="undocumented", queue=False, ) for event_trigger in event_triggers: event_trigger( utils.async_lambda( lambda: textbox_component( submit_btn=False, stop_btn=self.original_stop_btn, ) ), None, [self.textbox], api_visibility="undocumented", queue=False, ) for event_to_cancel in events_to_cancel: event_to_cancel.then( utils.async_lambda( lambda: textbox_component( submit_btn=original_submit_btn, stop_btn=False ) ), None, [self.textbox], api_visibility="undocumented", queue=False, ) self.textbox.stop( None, None, None, cancels=events_to_cancel, # type: ignore api_visibility="undocumented", ) def _clear_and_save_textbox( self, message: str | MultimodalPostprocess, ) -> tuple[ Textbox | MultimodalTextbox, str | MultimodalPostprocess, ]: return ( type(self.textbox)("", interactive=False, placeholder=""), message, ) def _append_message_to_history( self, message: MessageDict | Message | str | Component | MultimodalPostprocess | list, history: list[MessageDict], role: Literal["user", "assistant"] = "user", ) -> list[MessageDict]: message_dicts = self._message_as_message_dict(message, role) history = copy.deepcopy(history) history.extend(message_dicts) # type: ignore return history def _message_as_message_dict( self, message: MessageDict | Message | str | Component | MultimodalPostprocess | list, role: Literal["user", "assistant"], ) -> list[NormalizedMessageDict]: """ Converts a user message, example message, or response from the chat function to a list of MessageDict objects that can be appended to the chat history. """ message_dicts = [] if not isinstance(message, list): message = [message] for msg in message: if isinstance(msg, Message): message_dicts.append(msg.model_dump()) elif isinstance(msg, ChatMessage): msg.role = role message_dicts.append( dataclasses.asdict(msg, dict_factory=utils.dict_factory) ) elif isinstance(msg, (str, Component)): message_dicts.append({"role": role, "content": msg}) elif ( isinstance(msg, dict) and "content" in msg ): # in MessageDict format already msg["role"] = role # type: ignore message_dicts.append(msg) else: # in MultimodalPostprocess format multimodal_message = {"role": role, "content": []} for x in msg.get("files", []): # type: ignore if isinstance(x, dict): x = x.get("path") multimodal_message["content"].append({"path": x}) # type: ignore if msg["text"]: # type: ignore multimodal_message["content"].append(msg["text"]) # type: ignore message_dicts.append(multimodal_message) return message_dicts async def _submit_fn( self, message: str | MultimodalPostprocess, history: list[MessageDict], *args, ) -> tuple: inputs = [message, history] + list(args) if self.is_async: response = await self.fn(*inputs) else: response = await run_sync(self.fn, *inputs, limiter=self.limiter) # type: ignore if self.additional_outputs: response, *additional_outputs = response else: additional_outputs = None history = self._append_message_to_history(message, history, "user") history = self._append_message_to_history(response, history, "assistant") if additional_outputs: return response, history, *additional_outputs return response, history async def _stream_fn( self, message: str | MultimodalPostprocess, history: list[MessageDict], *args, ) -> AsyncGenerator[ tuple, None, ]: inputs = [message, history] + list(args) if self.is_async: generator = self.fn(*inputs) else: generator = await run_sync(self.fn, *inputs, limiter=self.limiter) # type: ignore generator = utils.SyncToAsyncIterator(generator, self.limiter) history = self._append_message_to_history(message, history, "user") additional_outputs = None try: first_response = await utils.async_iteration(generator) if self.additional_outputs: first_response, *additional_outputs = first_response history_ = self._append_message_to_history( first_response, history, "assistant" ) if not additional_outputs: yield first_response, history_ else: yield first_response, history_, *additional_outputs except StopIteration: yield None, history async for response in generator: if self.additional_outputs: response, *additional_outputs = response history_ = self._append_message_to_history(response, history, "assistant") if not additional_outputs: yield response, history_ else: yield response, history_, *additional_outputs def option_clicked( self, history: list[MessageDict], option: SelectData ) -> tuple[list[MessageDict], str | MultimodalPostprocess]: """ When an option is clicked, the chat history is appended with the option value. The saved input value is also set to option value. """ history.append({"role": "user", "content": option.value}) return history, option.value def _flatten_example_files(self, example: SelectData): """ Returns an example with the files flattened to just the file path. Also ensures that the `files` key is always present in the example. """ example.value["files"] = [f["path"] for f in example.value.get("files", [])] return example def example_populated(self, example: SelectData): if self.multimodal: example = self._flatten_example_files(example) return example.value else: return example.value["text"] def _edit_message( self, history: list[MessageDict], edit_data: EditData ) -> tuple[ list[MessageDict], list[MessageDict], str | MultimodalPostprocess, ]: if isinstance(edit_data.index, (list, tuple)): history = history[: edit_data.index[0]] else: history = history[: edit_data.index] return history, history, edit_data.value def example_clicked( self, example: SelectData ) -> Generator[tuple[list[MessageDict], str | MultimodalPostprocess], None, None]: """ When an example is clicked, the chat history (and saved input) is initially set only to the example message. Then, if example caching is enabled, the cached response is loaded and added to the chat history as well. """ history = self._append_message_to_history(example.value, [], "user") example = self._flatten_example_files(example) message = example.value if self.multimodal else example.value["text"] yield history, message if self.cache_examples: history = self.examples_handler.load_from_cache(example.index)[0].root yield history, message def _process_example( self, message: ExampleMessage | str, response: MessageDict | str | None ): result = [] if self.multimodal: message = cast(ExampleMessage, message) for file in message.get("files", []): if isinstance(file, dict): file = file.get("path") result.append({"role": "user", "content": (file,)}) if "text" in message: result.append({"role": "user", "content": message["text"]}) result.append({"role": "assistant", "content": response}) else: message = cast(str, message) result = [ {"role": "user", "content": message}, {"role": "assistant", "content": response}, ] return result async def _examples_fn( self, message: ExampleMessage | str, *args ) -> list[MessageDict]: inputs, _, _, _ = special_args( self.fn, inputs=[message, [], *args], request=None ) if self.is_async: response = await self.fn(*inputs) else: response = await run_sync(self.fn, *inputs, limiter=self.limiter) # type: ignore return self._process_example(message, response) # type: ignore async def _examples_stream_fn( self, message: str, *args, ) -> AsyncGenerator: inputs, _, _, _ = special_args( self.fn, inputs=[message, [], *args], request=None ) if self.is_async: generator = self.fn(*inputs) else: generator = await run_sync(self.fn, *inputs, limiter=self.limiter) # type: ignore generator = utils.SyncToAsyncIterator(generator, self.limiter) async for response in generator: yield self._process_example(message, response) def _pop_last_user_message( self, history: list[NormalizedMessageDict], ) -> tuple[list[NormalizedMessageDict], str | MultimodalPostprocess]: """ Removes the message (or set of messages) that the user last sent from the chat history and returns them. If self.multimodal is True, returns a MultimodalPostprocess (dict) object with text and files. If self.multimodal is False, returns just the message text as a string. """ if not history: return history, "" if not self.multimodal else {"text": "", "files": []} i = len(history) - 1 while i >= 0 and history[i]["role"] == "assistant": # type: ignore i -= 1 while i >= 0 and history[i]["role"] == "user": # type: ignore i -= 1 last_messages = history[i + 1 :] last_user_message = "" files = [] for msg in last_messages: assert isinstance(msg, dict) # noqa: S101 if msg["role"] == "user": content = msg["content"] for item in content: if item["type"] == "file": files.append(item["file"]) elif item["type"] == "text": last_user_message += item["text"] return_message = ( {"text": last_user_message, "files": files} if self.multimodal else last_user_message ) history_ = history[: i + 1] return history_, return_message # type: ignore