dash-chat-custom 1.0.1
A chat component for Dash
License: MIT

# dash-chat

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dash-chat is a Dash component library chat interface. It provides a customizable and responsive chat UI with support for markdown, chat persistence, typing indicators, themes, and state management.

## Installation
```
$ pip install dash-chat
```

## Basic Usage
The simplest way to use the `dash_chat.ChatComponent` is to initialize the `messages` prop as an empty list. This is a list of messages that initialize the chat UI. Each message is an OpenAI-style dictionary that must have the following key-value pairs:
- `role`: The message sender, either `"user"` or `"assistant"`.
- `content`: The content of the message.

A dash callback chat function is also required to handle how the messages are updated

### Example 1
Using **OpenAI** with dash-chat (requires the `openai` package - install it by running `pip install openai`)

![dash-chat-demo](https://github.com/gbolly/dash-chat/blob/main/dash-chat-demo.gif?raw=true)

```python
import os
import dash
from dash import callback, html, Input, Output, State
from dash_chat import ChatComponent
from openai import OpenAI


api_key = os.environ.get("OPENAI_API_KEY")
client = OpenAI(api_key=api_key)

app = dash.Dash(__name__)

app.layout = html.Div([
    ChatComponent(
        id="chat-component",
        messages=[],
    )
])

@callback(
    Output("chat-component", "messages"),
    Input("chat-component", "new_message"),
    State("chat-component", "messages"),
    prevent_initial_call=True,
)
def handle_chat(new_message, messages):
    if not new_message:
        return messages

    updated_messages = messages + [new_message]

    if new_message["role"] == "user":
        response = client.chat.completions.create(
            model="gpt-3.5-turbo",
            messages=updated_messages,
            temperature=1.0,
            max_tokens=150,
        )

        bot_response = {"role": "assistant", "content": response.choices[0].message.content.strip()}
        return updated_messages + [bot_response]

    return updated_messages

if __name__ == "__main__":
    app.run(debug=True)
```

### Example 2
To send local images and files along with a message to the AI assistant, the structure of `content` in the `messages` prop becomes a list of dictionary. The `content` takes the structure;

```python
    [
        {"type": "text", "text": "Analyze image"},
        {
            "type": "attachment",
            "file": <base64File>,
            "fileName": <file.name>,
            "fileType": <file.type>
        },
    ]
```
In your dash callback, follow the OpenAI-style for uploading images with text.

![dash-chat-demo](https://github.com/gbolly/dash-chat/blob/main/dash-chat-demo.gif?raw=true)

```python
import base64
import dash
import os
import re
from io import BytesIO
from dash import callback, html, Input, Output, State
from dash_chat import ChatComponent
from openai import OpenAI


api_key = os.environ.get("OPENAI_API_KEY")
client = OpenAI(api_key=api_key)

app = dash.Dash(__name__)

app.layout = html.Div([
    ChatComponent(
        id="chat-component",
        messages=[],
        supported_input_file_types=[".png", ".jpg", ".pdf", ".doc"]
    )
])


def decode_base64(data):
    match = re.match(r"data:(.*?);base64,(.*)", data)
    if match:
        _, base64_data = match.groups()
    else:
        base64_data = data

    missing_padding = len(base64_data) % 4
    if missing_padding:
        base64_data += "=" * (4 - missing_padding)

    return base64.b64decode(base64_data)


@callback(
    Output("chat-component", "messages"),
    Input("chat-component", "new_message"),
    State("chat-component", "messages"),
    prevent_initial_call=True,
)
def handle_chat(new_message, messages):
    if not new_message:
        return messages

    if isinstance(new_message["content"], list):
        user_content = []
        for item in new_message["content"]:
            if item["type"] == "text":
                user_content.append({"type": "text", "text": item["text"]})
            elif item["type"] == "attachment":
                file_type = item["fileType"]
                file_path = item["file"]
                file_name = item["fileName"]

                if file_type.startswith("image/"):
                    # https://github.com/openai/openai-python#vision
                    user_content.append(
                        {"type": "image_url", "image_url": {"url": file_path}}
                    )
                else:
                    # other file types (PDF, DOCX, etc.)
                    # https://github.com/openai/openai-python?tab=readme-ov-file#file-uploads
                    decoded_bytes = decode_base64(file_path)
                    uploaded_file = client.files.create(
                        file=(file_name, BytesIO(decoded_bytes), file_type),
                        purpose="user_data"
                    )
                    user_content.append({
                        "type": "text",
                        "text": f"File '{file_name}' uploaded. ID: {uploaded_file.id}",
                    })
        updated_messages = messages + [{"role": "user", "content": user_content}]
    else:
        updated_messages = messages + [new_message]

    if new_message["role"] == "user":
        response = client.chat.completions.create(
            model="gpt-4o-mini",
            messages=updated_messages,
            temperature=1.0,
            max_tokens=150,
        )

        bot_response = {
            "role": "assistant",
            "content": response.choices[0].message.content.strip(),
        }

        return updated_messages + [bot_response]

    return updated_messages


if __name__ == "__main__":
    app.run(debug=True)
```

### Example 3
`ChatComponent` is agnostic about which chatbot or AI assistant technology you're interacting with, so here's an example not using OpenAI

```python
import time
import dash
from dash import callback, html, Input, Output, State
from dash_chat import ChatComponent


app = dash.Dash(__name__)

app.layout = html.Div([
    ChatComponent(
        id="chat-component",
        messages=[],
    )
])

@callback(
    Output("chat-component", "messages"),
    Input("chat-component", "new_message"),
    State("chat-component", "messages"),
    prevent_initial_call=True,
)
def handle_chat(new_message, messages):
    if not new_message:
        return messages

    updated_messages = messages + [new_message]

    if new_message["role"] == "user":
        time.sleep(2)
        bot_response = {"role": "assistant", "content": "Hello John Doe."}
        return updated_messages + [bot_response]

    return updated_messages

if __name__ == "__main__":
    app.run(debug=True)
```

### **Persistence Functionality**
The ChatComponent supports persistence, allowing messages to be stored and retrieved across page reloads. When persistence=True, messages are saved in the specified storage (localStorage or sessionStorage).

On initialization, the component checks for stored messages.
If stored messages exist, they are loaded; otherwise, an empty message list is used.
New messages are automatically saved to storage.
When the page is refreshed, stored messages are restored to maintain chat history.
To enable persistence, set:

```python
ChatComponent(
    id="chat-component",
    persistence=True,
    persistence_type="local"  # or "session"
)
```

### **Renderers (Graphs, Tables, Attachments & Text)**
`dash-chat` supports rich content rendering by allowing messages to contain structured content types like graphs, tables, and images. You can render custom content by passing a structured list to the content field of a message.

#### Text
```python
{
    "role": "assistant",
    "content": {
        "type": "text",
        "text": "This will be rendered as a markdown message"
    },
}
```

#### Attachments (Images & Files)
```python
{
    "role": "assistant",
    "content": {
        "type": "attachment",
        "file": "data:image/png;base64,...",
        "fileName": "example.png",
        "fileType": "image/png"
    }
}
```
Renders an image or a downloadable file preview.

#### Graph
```python
{
    "role": "assistant",
    "content": {
        "type": "graph",
        "props": {
            "figure": {
                "data": [
                    {
                        "x": [1, 2, 3],
                        "y": [4, 1, 2],
                        "type": "bar", "name": "Demo"
                    }
                ],
                "layout": {"title": "Bar Chart"},
            },
            "config": {"displaylogo": True},
            "responsive": True
        }
    }
}
```
Renders an interactive Plotly graph equivalent to [`dcc.Graph`](https://dash.plotly.com/dash-core-components/graph). The props object supports most of the arguments you would pass to a [`dcc.Graph`](https://dash.plotly.com/dash-core-components/graph).

#### Table
```python
{
    "role": "assistant",
    "content": {
        "type": "table",
        "header": ["Order ID", "Item", "Quantity", "Total"],
        "data": [
            ["#1021", "Apple iPhone", 1, "$799"],
            ["#1022", "Samsung Galaxy", 2, "$1398"]
        ],
        "props": {
            "striped": True,
            "bordered": True,
            "hover": True,
            "responsive": True,
            "size": "lg"
        }
    }
}
```
Renders an HTML table. You provide the table by setting:

- header: a list of strings representing the column names.
    > Example: ["Order ID", "Item", "Quantity", "Total"]

- data: a list of rows, where each row is a list of strings (or values) for the cells.
    > Example: [["#1021", "Apple iPhone", 1, "$799"], ["#1022", "Samsung Galaxy", 2, "$1398"]]

The props object supports all the arguments you would pass to [`dbc.Table`](https://dash-bootstrap-components.opensource.faculty.ai/docs/components/table/) in dash-bootstrap-components.

#### Multiple renderers as a list at `"content"`
Multiple supported renderers can also be provided as the assistants' content:
```python
{
    "role": "assistant",
    "content": [
        {"type": "text", "text": "Here's a bar chart of your data."},
        {
            "type": "graph",
            "props": {
                "figure": {
                    "data": [{"x": [1, 2, 3], "y": [4, 1, 2], "type": "bar", "name": "Demo"}],
                    "layout": {"title": "Bar Chart"},
                }
                "config": {},
                "responsive": True
            },
        },
        {
            "type": "table",
            "header": ["Order ID", "Item", "Quantity", "Total"],
            "data": [
                ["#1021", "iPhone 14", 1, "$799"],
                ["#1022", "Galaxy S22", 2, "$1398"],
                ["#1023", "Pixel 7", 1, "$599"],
            ],
            "props": {
                "striped": True
            },
        },
    ]
}
```
For a complete example of how to setup dash apps and how to uses renderers see the `usage` folder.

### **Props**

`ChatComponent` can be configured with the following properties:

| Prop Name                     | Type                       | Default Value                 | Description                                                                                   |
|-------------------------------|----------------------------|-------------------------------|-----------------------------------------------------------------------------------------------|
| **id**                        | `string`                  | `None`                         | Unique identifier for the component, required for Dash callbacks.                             |
| **container_style**           | `dict`                    | `None`                         | Inline css styles to customize the chat container.                                            |
| **fill_height**               | `boolean`                 | `True`                         | Whether to vertically fill the screen with the chat container. If `False`, constrains height. |
| **fill_width**                | `boolean`                 | `True`                         | Whether to horizontally fill the screen with the chat container. If `False`, constrains width.|
| **input_container_style**     | `dict`                    | `None`                         | Inline css styles for the container holding the message input field.                          |
| **input_text_style**          | `dict`                    | `None`                         | Inline css styles for the message input field itself.                                         |
| **messages**                  | `list of dicts`           | `None`                         | List of chat messages. Each message object must include: `role` and `content`. Initialize as an empty list if no on first load.                  |
| **theme**                     | `string`                  | `"light"`                      | Theme for the chat interface. Options: `"light"` or `"dark"`.                                 |
| **typing_indicator**          | `string`                  | `"dots"`                       | Type of typing indicator. Options: `"dots"` (animated dots) or `"spinner"` (spinner).         |
| **user_bubble_style**         | `dict`                    | `{"backgroundColor": "#007bff", "color": "white", "marginLeft": "auto", "textAlign": "right"}`                                   | Inline css styles to customize the message bubble for user.            |
| **assistant_bubble_style**    | `dict`                    | `{"backgroundColor": "#f1f0f0", "color": "black", "marginRight": "auto", "textAlign": "left"}`                                   | Inline css styles to customize the message bubble for assistant.       |
| **input_placeholder**         | `string`                  | `None`                         | Placeholder text to be used in the input box.                                                 |
| **class_name**                | `string`                  | `None`                         | Name to use as class attribute on the main chat container.                                    |
| **persistence**               | `boolean`                 | `False`                        | Whether to store chat messages so that it can be persisted.                                   |
| **persistence_type**          | `string`                  | `"local"`                      | Where chat messages will be stored for persistence. Options: `"local"` or `"session"`         |
| **supported_input_file_types**          | `string`                  | `"*/*"`                | String or list of file types to support in the file input         |

## License

This project is licensed under the MIT License. See the LICENSE file for details.