Support additional_outputs in gr.ChatInterface (#10071)

* artifacts

* add changeset

* chatinterface

* guide fixes

* example

* zerogpu

* Revert "zerogpu"

This reverts commit 4d7b5882ae.

* changes

* submit fn

* chat interface

* changes

* add changeset

* notebook

* lint

* type

* regex

* add changeset

* fixes

* line

* fixes

* formatting

* change

* change

* add guard

* link to playground demo

* add changeset

---------

Co-authored-by: gradio-pr-bot <gradio-pr-bot@users.noreply.github.com>
Co-authored-by: aliabd <ali.si3luwa@gmail.com>

.changeset/lazy-symbols-behave.md

@@ -0,0 +1,6 @@
+---
+"gradio": minor
+"website": minor
+---
+
+feat:Support `additional_outputs` in `gr.ChatInterface`

demo/chatinterface_artifacts/run.ipynb

@@ -0,0 +1 @@
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: chatinterface_artifacts"]}, {"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": ["import gradio as gr\n", "\n", "python_code = \"\"\"\n", "def fib(n):\n", "    if n <= 0:\n", "        return 0\n", "    elif n == 1:\n", "        return 1\n", "    else:\n", "        return fib(n-1) + fib(n-2)\n", "\"\"\"\n", "\n", "js_code = \"\"\"\n", "function fib(n) {\n", "    if (n <= 0) return 0;\n", "    if (n === 1) return 1;\n", "    return fib(n - 1) + fib(n - 2);\n", "}\n", "\"\"\"\n", "\n", "def chat(message, history):\n", "    if \"python\" in message.lower():\n", "        return \"Type Python or JavaScript to see the code.\", gr.Code(language=\"python\", value=python_code)\n", "    elif \"javascript\" in message.lower():\n", "        return \"Type Python or JavaScript to see the code.\", gr.Code(language=\"javascript\", value=js_code)\n", "    else:\n", "        return \"Please ask about Python or JavaScript.\", None\n", "\n", "with gr.Blocks() as demo:\n", "    code = gr.Code(render=False)\n", "    with gr.Row():\n", "        with gr.Column():\n", "            gr.Markdown(\"<center><h1>Write Python or JavaScript</h1></center>\")\n", "            gr.ChatInterface(\n", "                chat,\n", "                examples=[\"Python\", \"JavaScript\"],\n", "                additional_outputs=[code],\n", "                type=\"messages\"\n", "            )\n", "        with gr.Column():\n", "            gr.Markdown(\"<center><h1>Code Artifacts</h1></center>\")\n", "            code.render()\n", "\n", "demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/chatinterface_artifacts/run.py

@@ -0,0 +1,44 @@
+import gradio as gr
+
+python_code = """
+def fib(n):
+    if n <= 0:
+        return 0
+    elif n == 1:
+        return 1
+    else:
+        return fib(n-1) + fib(n-2)
+"""
+
+js_code = """
+function fib(n) {
+    if (n <= 0) return 0;
+    if (n === 1) return 1;
+    return fib(n - 1) + fib(n - 2);
+}
+"""
+
+def chat(message, history):
+    if "python" in message.lower():
+        return "Type Python or JavaScript to see the code.", gr.Code(language="python", value=python_code)
+    elif "javascript" in message.lower():
+        return "Type Python or JavaScript to see the code.", gr.Code(language="javascript", value=js_code)
+    else:
+        return "Please ask about Python or JavaScript.", None
+
+with gr.Blocks() as demo:
+    code = gr.Code(render=False)
+    with gr.Row():
+        with gr.Column():
+            gr.Markdown("<center><h1>Write Python or JavaScript</h1></center>")
+            gr.ChatInterface(
+                chat,
+                examples=["Python", "JavaScript"],
+                additional_outputs=[code],
+                type="messages"
+            )
+        with gr.Column():
+            gr.Markdown("<center><h1>Code Artifacts</h1></center>")
+            code.render()
+
+demo.launch()

gradio/chat_interface.py

@@ -39,7 +39,7 @@ from gradio.context import get_blocks_context
 from gradio.events import Dependency, SelectData
 from gradio.helpers import create_examples as Examples  # noqa: N812
 from gradio.helpers import special_args, update
-from gradio.layouts import Accordion, Group, Row
+from gradio.layouts import Accordion, Column, Group, Row
 from gradio.routes import Request
 from gradio.themes import ThemeClass as Theme
 
@@ -74,6 +74,7 @@ class ChatInterface(Blocks):
         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,
         examples: list[str] | list[MultimodalValue] | list[list] | None = None,
         example_labels: list[str] | None = None,
         example_icons: list[str] | None = None,
@@ -108,6 +109,7 @@ class ChatInterface(Blocks):
             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.
             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).
@@ -123,7 +125,7 @@ class ChatInterface(Blocks):
             head_paths: Custom html code as a pathlib.Path to a html file or a list of such paths. This html files will be read, concatenated, and included in the head of the demo webpage. If the `head` parameter is also set, the html from `head` will be included first.
             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 textbox when the value changes, unless the user scrolls up. If False, will not scroll to the bottom of the textbox when the value changes.
+            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).
@@ -169,6 +171,7 @@ class ChatInterface(Blocks):
             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": "Additional Inputs",
@@ -203,114 +206,116 @@ class ChatInterface(Blocks):
                     break
 
         with self:
-            if title:
-                Markdown(
-                    f"<h1 style='text-align: center; margin-bottom: 1rem'>{self.title}</h1>"
-                )
-            if description:
-                Markdown(description)
-            if chatbot:
-                if self.type:
-                    if self.type != chatbot.type:
-                        warnings.warn(
-                            "The type of the gr.Chatbot does not match the type of the gr.ChatInterface."
-                            f"The type of the gr.ChatInterface, '{self.type}', will be used."
-                        )
-                        chatbot.type = self.type
-                        chatbot._setup_data_model()
-                else:
-                    warnings.warn(
-                        f"The gr.ChatInterface was not provided with a type, so the type of the gr.Chatbot, '{chatbot.type}', will be used."
+            with Column():
+                if title:
+                    Markdown(
+                        f"<h1 style='text-align: center; margin-bottom: 1rem'>{self.title}</h1>"
                     )
-                    self.type = chatbot.type
-                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.type = self.type or "tuples"
-                self.chatbot = Chatbot(
-                    label="Chatbot",
-                    scale=1,
-                    height=200 if fill_height else None,
-                    type=self.type,
-                    autoscroll=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_)}"
+                if description:
+                    Markdown(description)
+                if chatbot:
+                    if self.type:
+                        if self.type != chatbot.type:
+                            warnings.warn(
+                                "The type of the gr.Chatbot does not match the type of the gr.ChatInterface."
+                                f"The type of the gr.ChatInterface, '{self.type}', will be used."
                             )
-                        self.textbox = textbox_
+                            chatbot.type = self.type
+                            chatbot._setup_data_model()
                     else:
-                        textbox_component = (
-                            MultimodalTextbox if self.multimodal else Textbox
+                        warnings.warn(
+                            f"The gr.ChatInterface was not provided with a type, so the type of the gr.Chatbot, '{chatbot.type}', will be used."
                         )
-                        self.textbox = textbox_component(
-                            show_label=False,
-                            label="Message",
-                            placeholder="Type a message...",
-                            scale=7,
-                            autofocus=autofocus,
-                            submit_btn=submit_btn,
-                            stop_btn=stop_btn,
+                        self.type = chatbot.type
+                    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.type = self.type or "tuples"
+                    self.chatbot = Chatbot(
+                        label="Chatbot",
+                        scale=1,
+                        height=200 if fill_height else None,
+                        type=self.type,
+                        autoscroll=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="Message",
+                                placeholder="Type a message...",
+                                scale=7,
+                                autofocus=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
+                        # 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.fake_response_textbox = Textbox(label="Response", visible=False)
+                    self.fake_api_btn = Button("Fake API", visible=False)
+                    self.fake_response_textbox = Textbox(
+                        label="Response", visible=False
+                    )
 
-            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=self.cache_mode,
-                    visible=self._additional_inputs_in_examples,
-                    preprocess=self._additional_inputs_in_examples,
+                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=self.cache_mode,
+                        visible=self._additional_inputs_in_examples,
+                        preprocess=self._additional_inputs_in_examples,
+                    )
+
+                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()
 
-            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()
-
-            self.saved_input = State()
-            self.chatbot_state = (
-                State(self.chatbot.value) if self.chatbot.value else State([])
-            )
-            self.previous_input = State(value=[])
-            self.show_progress = show_progress
-            self._setup_events()
-            self._setup_api()
+                self.saved_input = State()
+                self.chatbot_state = (
+                    State(self.chatbot.value) if self.chatbot.value else State([])
+                )
+                self.previous_input = State(value=[])
+                self.show_progress = show_progress
+                self._setup_events()
+                self._setup_api()
 
     @staticmethod
     def _setup_example_messages(
@@ -360,7 +365,7 @@ class ChatInterface(Blocks):
             .then(
                 submit_fn,
                 [self.saved_input, self.chatbot] + self.additional_inputs,
-                [self.chatbot],
+                [self.chatbot] + self.additional_outputs,
                 show_api=False,
                 concurrency_limit=cast(
                     Union[int, Literal["default"], None], self.concurrency_limit
@@ -398,7 +403,7 @@ class ChatInterface(Blocks):
                 ).then(
                     submit_fn,
                     [self.saved_input, self.chatbot],
-                    [self.chatbot],
+                    [self.chatbot] + self.additional_outputs,
                     show_api=False,
                     concurrency_limit=cast(
                         Union[int, Literal["default"], None], self.concurrency_limit
@@ -431,7 +436,7 @@ class ChatInterface(Blocks):
             .then(
                 submit_fn,
                 [self.saved_input, self.chatbot] + self.additional_inputs,
-                [self.chatbot],
+                [self.chatbot] + self.additional_outputs,
                 show_api=False,
                 concurrency_limit=cast(
                     Union[int, Literal["default"], None], self.concurrency_limit
@@ -465,7 +470,7 @@ class ChatInterface(Blocks):
         ).then(
             submit_fn,
             [self.saved_input, self.chatbot],
-            [self.chatbot],
+            [self.chatbot] + self.additional_outputs,
             show_api=False,
             concurrency_limit=cast(
                 Union[int, Literal["default"], None], self.concurrency_limit
@@ -668,7 +673,7 @@ class ChatInterface(Blocks):
         history_with_input: TupleFormat | list[MessageDict],
         request: Request,
         *args,
-    ) -> tuple[TupleFormat, TupleFormat] | tuple[list[MessageDict], list[MessageDict]]:
+    ) -> TupleFormat | list[MessageDict] | tuple[TupleFormat | list[MessageDict], ...]:
         message_serialized, history = self._process_msg_and_trim_history(
             message, history_with_input
         )
@@ -682,9 +687,15 @@ class ChatInterface(Blocks):
             response = await anyio.to_thread.run_sync(
                 self.fn, *inputs, limiter=self.limiter
             )
+        additional_outputs = None
+        if isinstance(response, tuple):
+            response, *additional_outputs = response
+
         self._append_history(history_with_input, response)
 
-        return history_with_input  # type: ignore
+        if additional_outputs:
+            return history_with_input, *additional_outputs
+        return history_with_input
 
     async def _stream_fn(
         self,
@@ -692,7 +703,10 @@ class ChatInterface(Blocks):
         history_with_input: TupleFormat | list[MessageDict],
         request: Request,
         *args,
-    ) -> AsyncGenerator:
+    ) -> AsyncGenerator[
+        TupleFormat | list[MessageDict] | tuple[TupleFormat | list[MessageDict], ...],
+        None,
+    ]:
         message_serialized, history = self._process_msg_and_trim_history(
             message, history_with_input
         )
@@ -707,15 +721,29 @@ class ChatInterface(Blocks):
                 self.fn, *inputs, limiter=self.limiter
             )
             generator = utils.SyncToAsyncIterator(generator, self.limiter)
+
+        additional_outputs = None
         try:
             first_response = await utils.async_iteration(generator)
+            if isinstance(first_response, tuple):
+                first_response, *additional_outputs = first_response
             self._append_history(history_with_input, first_response)
-            yield history_with_input
+            yield (
+                history_with_input
+                if not additional_outputs
+                else (history_with_input, *additional_outputs)
+            )
         except StopIteration:
             yield history_with_input
         async for response in generator:
+            if isinstance(response, tuple):
+                response, *additional_outputs = response
             self._append_history(history_with_input, response, first_response=False)
-            yield history_with_input
+            yield (
+                history_with_input
+                if not additional_outputs
+                else (history_with_input, *additional_outputs)
+            )
 
     def option_clicked(
         self, history: list[MessageDict], option: SelectData

guides/05_chatbots/01_creating-a-chatbot-fast.md

@@ -1,6 +1,6 @@
 # How to Create a Chatbot with Gradio
 
-Tags: NLP, LLM, CHATBOT
+Tags: LLM, CHATBOT, NLP
 
 ## Introduction
 

guides/05_chatbots/02_chatinterface-examples.md

@@ -1,5 +1,7 @@
 # Using Popular LLM libraries and APIs
 
+Tags: LLM, CHATBOT, API
+
 In this Guide, we go through several examples of how to use `gr.ChatInterface` with popular LLM libraries and API providers.
 
 We will cover the following libraries and API providers:
@@ -37,7 +39,7 @@ Tip: For quick prototyping, the  <a href='https://github.com/gradio-app/openai-g
 
 ## Hugging Face `transformers`
 
-Of course, in many cases you want to run a chatbot locally. Here's the equivalent example using Together's RedPajama model, from Hugging Face (this requires you to have a GPU with CUDA).
+Of course, in many cases you want to run a chatbot locally. Here's the equivalent example using the SmolLM2-135M-Instruct model using the Hugging Face `transformers` library.
 
 $code_llm_hf_transformers
 
@@ -47,7 +49,7 @@ The SambaNova Cloud API provides access to full-precision open-source models, su
 
 $code_llm_sambanova
 
-Tip: For quick prototyping, the  <a href='https://github.com/gradio-app/sambanova-gradio'>sambanova-gradio library</a> makes it even easier to build chatbots on top of OpenAI models.
+Tip: For quick prototyping, the  <a href='https://github.com/gradio-app/sambanova-gradio'>sambanova-gradio library</a> makes it even easier to build chatbots on top of SambaNova models.
 
 ## Hyperbolic
 
@@ -55,7 +57,7 @@ The Hyperbolic AI API provides access to many open-source models, such as the Ll
 
 $code_llm_hyperbolic
 
-Tip: For quick prototyping, the  <a href='https://github.com/HyperbolicLabs/hyperbolic-gradio'>hyperbolic-gradio library</a> makes it even easier to build chatbots on top of OpenAI models.
+Tip: For quick prototyping, the  <a href='https://github.com/HyperbolicLabs/hyperbolic-gradio'>hyperbolic-gradio library</a> makes it even easier to build chatbots on top of Hyperbolic models.
 
 
 ## Anthropic's Claude 

js/_website/generate_jsons/src/docs/__init__.py

@@ -3,6 +3,9 @@ import json
 import os
 import re
 import requests
+import base64
+import urllib.parse
+
 
 
 from gradio_client.documentation import document_cls, generate_documentation
@@ -97,11 +100,34 @@ def add_guides():
 add_guides()
 
 
+def generate_playground_link(demo_name):
+    playground_url = "https://gradio.app/playground?demo=Blank"
+    with open(os.path.join(DEMOS_DIR, demo_name, "run.py")) as f:
+        demo_code = f.read()
+        encoded_code = base64.b64encode(demo_code.encode('utf-8')).decode('utf-8')
+        encoded_code_url = urllib.parse.quote(encoded_code, safe='')
+        playground_url += "&code=" + encoded_code_url
+    if "requirements.txt" in os.listdir(os.path.join(DEMOS_DIR, demo_name)):
+        with open(os.path.join(DEMOS_DIR, demo_name, "requirements.txt")) as f:
+            requirements = f.read()
+            if requirements:
+                encoded_reqs = base64.b64encode(requirements.encode('utf-8')).decode('utf-8')
+                encoded_reqs_url = urllib.parse.quote(encoded_reqs, safe='')
+                playground_url += "&reqs=" + encoded_reqs_url
+    return f"<a href='{playground_url}' target='_blank'>demo/{demo_name}</a>"
+
+
 def escape_parameters(parameters):
     new_parameters = []
     for param in parameters:
         param = param.copy()  # Manipulating the list item directly causes issues, so copy it first
         param["doc"] = html.escape(param["doc"]) if param["doc"] else param["doc"]
+        if param["doc"] and "$demo/" in param["doc"]:
+            param["doc"] = re.sub(
+                    r"\$demo/(\w+)",
+                    lambda m: generate_playground_link(m.group(1)),
+                    param["doc"],
+                )
         new_parameters.append(param)
     assert len(new_parameters) == len(parameters)
     return new_parameters