gr.ChatInterface — a full chat UI in one line

$ import gradio as gr

def echo(message, history):
    return f"You said: {message}"

demo = gr.ChatInterface(fn=echo, title="Echo Bot")
demo.launch()

# fn signature: (message: str, history: list) -> str
# gr.ChatInterface.__init__ has 34 parameters in Gradio 6.28.0

Console:
* Running on local URL:  http://127.0.0.1:7860
* To create a public link, set `share=True` in `launch()`.

Rendered UI (verified by introspecting demo.blocks — 15 components, 5 of them hidden state holders: one BrowserState plus four State objects):
Column
 ├─ Markdown (title "Echo Bot")
 ├─ Chatbot (the message thread, empty at start)
 └─ Group
     ├─ Row
     │   └─ Textbox ("Type a message...", round pill, Enter-to-send)
     │   └─ Form (submit + stop buttons rendered together)
     └─ JSON (hidden internal state holder)

Typing "hi" and hitting Enter appends your message bubble on the right,
then "You said: hi" appears in an assistant bubble on the left.

Bonus — call it without a browser via the REST API it auto-exposes:
POST /gradio_api/call/echo {"data": ["hi", []]}
→ event: complete\ndata: ["You said: hi", null]

The fn gets (message, history) and returns ONE string — that's the whole contract.

$ import gradio as gr

def streamy(message, history):
    for word in ["alpha ", "beta ", "gamma"]:
        yield word

demo = gr.ChatInterface(fn=streamy)
demo.launch()

# Same fn, streaming: yield instead of return

Console:
* Running on local URL:  http://127.0.0.1:7860

Rendered UI: the same tree minus the title Markdown — ChatInterface only adds that
component when you pass title=, so this app has 14 components:
Column > Chatbot > Group > Row > Textbox + Form/Button > JSON, plus the five
hidden state components.

The reply bubble types out word by word: "alpha ", then "beta ", then "gamma".
Gradio auto-detected the generator function and switched to streaming mode —
no queue() call needed.

Same call over the REST API shows the progressive stream:
POST /gradio_api/call/streamy {"data": ["one two three", []]}
→ event: generating\ndata: ["alpha ", null]
→ event: generating\ndata: ["beta ", null]
→ event: generating\ndata: ["gamma", null]
→ event: complete\ndata: ["gamma", null]

Any generator fn streams for free — no extra parameters, no queue config needed.

$ import gradio as gr

def vision_chat(message, history):
    files = [f.split("/")[-1] for f in message.get("files", [])]
    return f"Got text={message.get('text')!r}, files={files}"

demo = gr.ChatInterface(
    fn=vision_chat,
    multimodal=True,
    title="Vision Helper",
)
demo.launch()

# message is a dict: {"text": str, "files": [filepath, ...]}

Console:
* Running on local URL:  http://127.0.0.1:7860

Rendered UI (15 components): same skeleton as the basic app, but the input
Row now holds a MultimodalTextbox — a message field with a 📎 attach button.

Drag an image called photo.webp into the box, type "what is this?", send:
your bubble shows the image preview + text; the assistant bubble replies:
"Got text='what is this?', files=['photo.webp']"

multimodal=False (the default) keeps the plain single-line Textbox.

multimodal=True changes the message type from str to dict — code both branches or pin the format.

$ import gradio as gr

def greet(message, history, system_prompt):
    return f"[{system_prompt}] {message}"

demo = gr.ChatInterface(
    fn=greet,
    additional_inputs=[
        gr.Textbox("You are helpful.", label="System prompt"),
    ],
    examples=[["hello", "You are terse."], ["who are you", "You are HAL."]],
)
demo.launch()

# extra fn args come from additional_inputs, AFTER (message, history)
# with additional_inputs, examples must be lists of lists

Console:
* Running on local URL:  http://127.0.0.1:7860

Rendered UI (18 components — the extra inputs add 4):
Column > Markdown > Chatbot > Group > Row > Textbox + Button > JSON
plus:
 ├─ Accordion ("Additional Inputs", collapsed) > Textbox ("System prompt")
 └─ Dataset (the 2 clickable example chips under the input)

Expand the accordion, set the system prompt to "You are terse.",
type "hello" and send: assistant bubble shows "[You are terse.] hello".
Clicking the "who are you" example chip fills the message AND switches
the system prompt value to "You are HAL." before sending.

Examples become a Dataset of clickable chips; with additional_inputs each example row must include a value for every extra input.