
# Streamlit Chat Prompt

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A Streamlit component that provides a modern chat-style prompt with image attachment and paste support. This component was built to mimic the style of [streamlit.chat_input](https://docs.streamlit.io/develop/api-reference/chat/st.chat_input) while expanding functionality with images. Future work may include addition of speech-to-text input.

**Author:** Tyler House ([@tahouse](https://github.com/tahouse))

![Demo](https://raw.githubusercontent.com/tahouse/streamlit-chat-prompt/main/docs/demo.gif)

## Features

- 📝 Chat-style text input with multi-line support
- 📎 Image attachment support via button or drag-and-drop
- 📋 Paste image support (paste images directly from clipboard)
- 🖼️ Image preview with ability to remove attached images
- ⌨️ Submit with Enter key (Shift+Enter for new line)
- 🎨 Automatic theme integration with Streamlit
- 📱 Responsive design that works well on mobile and desktop
- 🗜️ Automatic image compression/scaling to stay under size limits (customizable, default 5MB)
- 📌 Optional pinned-to-bottom placement for main chat interface (one per app)
- 🔄 Flexible positioning for use in dialogs, sidebars, or anywhere in the app flow
- ✏️ Support for default/editable content - perfect for message editing workflows
- 🔤 Smart focus management - automatically returns to text input after interactions

## Installation

```bash
pip install streamlit-chat-prompt
```

## Usage

```python
import streamlit as st
from streamlit_chat_prompt import prompt

# Create a chat prompt
response = prompt(
    name="chat",  # Unique name for the prompt
    key="chat",   # Unique key for the component instance
    placeholder="Hi there! What should we talk about?",  # Optional placeholder text
    main_bottom=True,  # Pin prompt to bottom of main area
    max_image_size=5 * 1024 * 1024,  # Maximum image size (5MB default)
    disabled=False,  # Optionally disable the prompt
)

# Handle the response
if response:
    if response.text:
        st.write(f"Message: {response.text}")
    
    if response.images:
        for i, img in enumerate(response.images):
            st.write(f"Image {i+1}: {img.type} ({img.format})")
```

## Examples

Here are some usage patterns, or check out [rocktalk](https://github.com/tahouse/rocktalk) for a full working example.

1. Main Chat Interface ![Main Chat Interface](https://raw.githubusercontent.com/tahouse/streamlit-chat-prompt/main/docs/main-chat.png)

    ```python
    import base64
    from io import BytesIO
    import streamlit as st
    from streamlit_chat_prompt import PromptReturn, prompt, ImageData
    from PIL import Image


    st.chat_message("assistant").write("Hi there! What should we chat about?")

    prompt_return: PromptReturn | None = prompt(
        name="foo",
        key="chat_prompt",
        placeholder="Hi there! What should we chat about?",
        main_bottom=True,
    )

    if prompt_return:
        with st.chat_message("user"):
            st.write(prompt_return.text)
            if prompt_return.images:
                for image in prompt_return.images:
                    st.divider()
                    image_data: bytes = base64.b64decode(image.data)
                    st.markdown("Using `st.image`")
                    st.image(Image.open(BytesIO(image_data)))

                    # or use markdown
                    st.divider()
                    st.markdown("Using `st.markdown`")
                    st.markdown(f"![Image example](data:image/png;base64,{image.data})")

    ```

2. Dialog Usage and Starting From Existing Message ![Dialog Interface](https://raw.githubusercontent.com/tahouse/streamlit-chat-prompt/main/docs/dialog.png)

    ```python
    if st.button(
        "Dialog Prompt with Default Value", key=f"dialog_prompt_with_default_button"
    ):
        with open("example_images/vangogh.png", "rb") as f:
            image_data = f.read()
            image = Image.open(BytesIO(image_data))
            base64_image = base64.b64encode(image_data).decode("utf-8")
            test_dg(
                default_input=PromptReturn(
                    text="This is a test message with an image",
                    images=[
                        ImageData(data=base64_image, type="image/png", format="base64")
                    ],
                ),
                key="dialog_with_default",
            )
    ```

## Component API

### prompt()

Main function to create a chat prompt.

Parameters:

- `name` (str): Unique name for this prompt instance
- `key` (str): Unique key for the component instance
- `placeholder` (str, optional): Placeholder text shown in input field
- `default` (Union[str, PromptReturn], optional): Default value for the prompt. Can include text and images using the `PromptReturn` object type.
- `main_bottom` (bool, optional): Pin prompt to bottom of main area (default: True)
- `max_image_size` (int, optional): Maximum image size in bytes (default: 5MB)
- `disabled` (bool, optional): Disable the prompt (default: False)

Returns:

`Optional[PromptReturn]`: Object containing message and images if submitted, None otherwise

### PromptReturn

Object returned when user submits the prompt.

Properties:

- `text` (Optional[str]): Text message entered by user
- `images` (Optional[List[ImageData]]): List of attached images

### ImageData

Object representing an attached image.

Properties:

- `type` (str): Image MIME type (e.g. "image/jpeg")
- `format` (str): Image format (e.g. "base64")
- `data` (str): Image data as base64 string

## Development

This repository is based on the [Streamlit Component template system](https://github.com/streamlit/component-template). If you want to modify or develop the component:

1. Clone the repository
2. Install development dependencies:

    ```sh
    pip install -e ".[devel]"
    ```

3. Start the frontend development server:

    ```sh
    cd streamlit_chat_prompt/frontend
    npm install
    npm run start
    ```

4. In a separate terminal, run your Streamlit app:

    ```sh
    streamlit run your_app.py
    ```

## License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.
