# Start interactive mode
praisonai --interactive

# While the agent is processing, type more messages
# They will be queued and processed in order

┌─────────────────────────────────────────────────────────────────┐
│                    Interactive Mode                              │
├─────────────────────────────────────────────────────────────────┤
│  ┌─────────────┐    ┌──────────────┐    ┌─────────────────┐    │
│  │ User Input  │───▶│ MessageQueue │───▶│ Agent Processing│    │
│  │             │    │   (FIFO)     │    │                 │    │
│  └─────────────┘    └──────────────┘    └─────────────────┘    │
│         │                  │                     │              │
│         │                  ▼                     │              │
│         │          ┌──────────────┐              │              │
│         └─────────▶│ Queue Display│◀─────────────┘              │
│                    │  (visual UI) │                             │
│                    └──────────────┘                             │
└─────────────────────────────────────────────────────────────────┘

❯ /queue

Queued Messages (3):
  0. ↳ Refactor the authentication module
  1. ↳ Add unit tests for the new feature
  2. ↳ Update the README with new instructions

Use /queue clear to clear, /queue remove N to remove

❯ /queue clear
✓ Cleared 3 queued message(s)

❯ /queue remove 1
✓ Removed: Add unit tests for the new feature...

❯ Create a Python function to calculate fibonacci
╭──────────────────────────────────────╮
│ ⏳ Calling LLM...                    │
│ 📋 Queued: 2                         │
╰──────────────────────────────────────╯

# Start interactive mode
praisonai --interactive

# Send first task
❯ Create a Python function to calculate fibonacci numbers

# While agent is processing, queue more tasks
❯ Add docstrings to the function
❯ Create unit tests for edge cases
❯ Write a README explaining usage

# Check the queue
❯ /queue
⏳ Processing...
📋 Queued (3)

Queued Messages (3):
  0. ↳ Add docstrings to the function
  1. ↳ Create unit tests for edge cases
  2. ↳ Write a README explaining usage

# Agent will process each task in order automatically

from praisonai.cli.features.message_queue import (
    MessageQueue,
    StateManager,
    QueueDisplay,
    ProcessingState,
    MessageQueueHandler
)

# Create a queue
queue = MessageQueue()

# Add messages
queue.add("First task")
queue.add("Second task")
queue.add("Third task")

# Check queue status
print(f"Queue count: {queue.count}")  # 3
print(f"Is empty: {queue.is_empty}")  # False

# View all messages
messages = queue.get_all()
print(messages)  # ['First task', 'Second task', 'Third task']

# Pop first message (FIFO)
first = queue.pop()
print(first)  # 'First task'

# Peek without removing
next_msg = queue.peek()
print(next_msg)  # 'Second task'

# Remove at specific index
removed = queue.remove_at(1)
print(removed)  # 'Third task'

# Clear all
queue.clear()

from praisonai.cli.features.message_queue import MessageQueueHandler, ProcessingState

# Create handler with a processor function
def my_processor(message):
    # Process the message (e.g., send to LLM)
    return f"Processed: {message}"

handler = MessageQueueHandler(processor=my_processor)

# Submit when idle - processes immediately
handler.submit("First task")

# Simulate processing state
handler.state_manager.set_state(ProcessingState.PROCESSING)

# Submit while processing - queued
handler.submit("Second task")
handler.submit("Third task")

# Check status
status = handler.get_status()
print(status)
# {'queue_count': 2, 'state': 'processing', 'messages': ['Second task', 'Third task']}

# When processing completes, call this to process queue
handler.on_processing_complete()

from praisonai.cli.features.message_queue import (
    MessageQueue, StateManager, QueueDisplay, ProcessingState
)

queue = MessageQueue()
queue.add("Task 1")
queue.add("Task 2")

state = StateManager()
state.set_state(ProcessingState.PROCESSING)

display = QueueDisplay(queue, state_manager=state)

# Format for display
print(display.format_status())      # ⏳ Processing...
print(display.format_queue_count()) # 📋 Queued (2)
print(display.format_queue())       # ↳ Task 1\n↳ Task 2

from praisonai.cli.features.message_queue import AsyncProcessor

processor = AsyncProcessor()

def on_complete(result):
    print(f"Done: {result}")

def on_status(status):
    print(f"Status: {status}")

# Start background processing
processor.start(
    work_fn=lambda: "result",
    on_complete=on_complete,
    on_status=on_status
)

# Check if running
print(processor.is_running)  # True/False

from praisonai.cli.features.message_queue import LiveStatusDisplay

display = LiveStatusDisplay()

# Update status
display.update_status("Thinking...")
display.update_status("Calling LLM...")

# Track tool calls
display.add_tool_call("read_file", {"path": "main.py"})
display.add_command_execution("ls -la")

# Get formatted status
print(display.format_live_status())
# ⏳ Calling LLM...
# 🔧 read_file
# 💻 ls -la

# Clear when done
display.clear()

from praisonai.cli.features.message_queue import NonBlockingInput

input_handler = NonBlockingInput()

# Submit input (from another thread)
input_handler.submit("user message")

# Check for pending input
if input_handler.has_input():
    msg = input_handler.get_input()
    print(f"Got: {msg}")