User: With streamlit, how can I parse and manage Markdown text? Say I have an LLM responding to me and giving markdown, such as:
### Title
*bold text*
```bash
# This is some text
```User: How can I do that?
Streamlit provides built-in support for rendering Markdown, which makes it easy to parse and display Markdown text returned from a language model like an LLM. You can use the `st.markdown()` function to render the Markdown content directly on your Streamlit app. Here's a step-by-step guide on how you can manage and display Markdown text using Streamlit:

1. **Install Streamlit**: If you haven't already, you need to install Streamlit. You can do this using pip:

   ```bash
   pip install streamlit
   ```

2. **Create a Streamlit App**: Create a new Python file, say `app.py`.

3. **Write the Streamlit Code**: Use the `st.markdown()` function to render the Markdown content returned by the LLM. For example:

   ```python
   import streamlit as st

   # Sample Markdown text from an LLM
   markdown_text = """
   ### Title
   **bold text**

   ```bash
   # This is some code in a bash script
   ```
   """

   # Display the Markdown text
   st.markdown(markdown_text)
   ```

4. **Run your Streamlit App**: Open a terminal, navigate to the directory containing your `app.py`, and run:

   ```bash
   streamlit run app.py
   ```

   This will start a local server and open your Streamlit app in a web browser.

Streamlit automatically parses and renders the Markdown content, including headings, bold text, and code blocks, in a visually appealing format. This makes it a great tool for displaying dynamic content generated by LLMs or other sources.

Additionally, you can use Markdown specific syntax within the `st.markdown()` function, such as tables, lists, images, and more to enhance the display of your content. If there are additional specific needs for your Markdown display, Streamlit offers other features and widgets to further customize the user interface.
User: And how can I prevent pressing return from sending the submit? to the message?
Assistant: When implementing a text input field in a Streamlit app, particularly when using a multi-line text input (like a chat interface with an LLM returning Markdown responses), it's common to want to prevent the 'Enter' or 'Return' key from automatically submitting the input. As of my last update, Streamlit doesn’t provide built-in support for intercepting keypress events like this directly in the simple text input widgets. However, you can manage this behavior using a workaround by implementing the following setup:

### Using Session State

1. **Install Streamlit** if you haven't already done so:

   ```bash
   pip install streamlit
   ```

2. **Create a new Streamlit app (`app.py`)**:

```python
import streamlit as st

# Define or load previous user input or app state
if 'user_input' not in st.session_state:
    st.session_state.user_input = ""

def handle_submit():
    """Handle the input when the form is submitted."""
    # You can add logic here, like sending the input to an LLM
    st.write("You submitted:", st.session_state.user_input)

# Create a form-like structure with a callback
with st.form(key='input_form'):
    # Multi-line text input (textarea)
    st.session_state.user_input = st.text_area("Enter Markdown text here:", value=st.session_state.user_input, height=100)
    
    # Submit button with a callback
    submitted = st.form_submit_button("Submit", on_click=handle_submit)

# Display the current content of the "user_input"
st.markdown("### You entered:")
st.markdown(st.session_state.user_input)
```

3. **Run the Streamlit App**:

   ```bash
   streamlit run app.py
   ```

### Explanation:

1. **Session State**: We use `st.session_state` to hold the current state of the text input. This allows us to manage the text input state across interactions, as Streamlit re-runs the script from top to bottom each time the user interacts with the app.

2. **Text Area**: The `text_area` widget allows for multi-line input without auto-submission on pressing 'Enter'. This is crucial for allowing users to enter complex markdown without submitting each time they press return for a new line.

3. **Form**: By wrapping the input in a form (`st.form()`), you explicitly define a section of the app that behaves like a form, only submitting when the `form_submit_button` is pressed. This setup prevents Enter from submitting the form and instead only submits when the "Submit" button is clicked.

With this structure, you have control over when the input is considered complete and can be submitted, making the user experience more intuitive for submitting Markdown text especially when interacting with an LLM or similar applications.
User: Here is the code for my current application. Change it. Also, I would like it to scroll down to the history automatically. Code: ```py import streamlit as st from gradio_client import Client import os import json  # Set page configuration st.set_page_config(     page_title="LLM Chat Application",     layout="wide",     initial_sidebar_state="auto" )  # Constants HISTORY_FILE = 'history.txt'  # This file stores the conversation history MODEL_CLIENT_NAME = 'yuntian-deng/ChatGPT4'  # Replace with your actual model identifier CONFIG_FILE = 'config.json'  # Config file to store top_p and temperature  # Function to load history from the file def load_history():     if os.path.exists(HISTORY_FILE):         with open(HISTORY_FILE, 'r', encoding='utf-8') as f:             return f.read()     return ""  # Function to save history to the file def save_history(history):     with open(HISTORY_FILE, 'w', encoding='utf-8') as f:         f.write(history)  # Function to load config from the file def load_config():     if os.path.exists(CONFIG_FILE):         with open(CONFIG_FILE, 'r', encoding='utf-8') as f:             return json.load(f)     else:         return {"top_p": 1.0, "temperature": 1.0}  # Function to save config to the file def save_config(config):     with open(CONFIG_FILE, 'w', encoding='utf-8') as f:         json.dump(config, f)  # Initialize session state for history if 'history' not in st.session_state:     st.session_state.history = load_history()  # Initialize session state for config if 'config' not in st.session_state:     st.session_state.config = load_config()  # We use a different key for the text area to avoid conflicts history_input = st.text_area(     "Conversation",     value=st.session_state.history,     height=400,     key='history_text_area',     placeholder="Your conversation so far...", )  # Handle manual edits to the history if history_input != st.session_state.history:     # Update the history with the user edits     st.session_state.history = history_input     save_history(st.session_state.history)  # Create a form for new input with st.form("chat_form", clear_on_submit=True):     user_input = st.text_input("Your Message", key='user_input', placeholder="Type your message here...")      # Generate Response button     generate = st.form_submit_button("Send Message")      # Sliders for Temperature and Top-P sampling     col1, col2 = st.columns(2)     with col1:         temperature = st.slider(             "Temperature",             min_value=0.0,             max_value=5.0,             value=st.session_state.config.get('temperature', 1.0),             step=0.1,             help="Controls the randomness of the model's output."         )     with col2:         top_p = st.slider(             "Top-P Sampling",             min_value=0.0,             max_value=1.0,             value=st.session_state.config.get('top_p', 1.0),             step=0.05,             help="Nucleus sampling."         )      # Update the config in session_state and save to file when sliders change     if temperature != st.session_state.config.get('temperature', 1.0) or top_p != st.session_state.config.get('top_p', 1.0):         st.session_state.config['temperature'] = temperature         st.session_state.config['top_p'] = top_p         save_config(st.session_state.config)  # Handle form submission if generate:     if user_input.strip():         # Append user's message to the history         st.session_state.history += f"User: {user_input.strip()}\n"         save_history(st.session_state.history)         try:             # Initialize the client             client = Client(MODEL_CLIENT_NAME)              # Prepare the prompt             prompt = st.session_state.history              # Call the model to get a response             result = client.predict(                 inputs=prompt,                 top_p=st.session_state.config['top_p'],                 temperature=st.session_state.config['temperature'],                 chat_counter=0,                 chatbot=[],                 api_name='/predict'             )             # Adjust based on actual API response structure             # Assuming 'result' is a list of lists where result[0][0][1] is the response             output = result[0][0][1].strip()              # Append model response to history             st.session_state.history += f"Assistant: {output}\n"              st.markdown(st.session_state.history)                          # Save updated history             save_history(st.session_state.history)              # Re-render the page to show updated history             st.rerun()          except Exception as e:             st.error(f"Error communicating with the model: {e}")     else:         st.warning("Please enter a message.") ```