Utilities and Helpers

class Progress:
    def __init__(self, track_tqdm=False):
        """
        Progress tracking for long-running iterators.

        Parameters:
        - track_tqdm: Whether to automatically track tqdm progress bars
        """

    def __call__(self, progress, desc=None, total=None, unit="steps"):
        """
        Update progress indicator.

        Parameters:
        - progress: Progress value (0-1 for percentage, or current/total)
        - desc: Description of current operation
        - total: Total number of steps (if not using 0-1 scale)
        - unit: Unit name for progress display
        """

    def tqdm(self, iterable, *args, **kwargs):
        """
        Wrap iterable with progress tracking.

        Parameters:
        - iterable: Iterable to track progress for
        - Additional tqdm arguments for customization
        
        Returns:
        - Progress-wrapped iterable
        """

    def update(self, n=1):
        """Increment progress by n steps."""

    def close(self):
        """Close progress indicator."""

import gradio as gr
import time

def long_task(progress=gr.Progress()):
    progress(0, desc="Starting...")
    
    for i in progress.tqdm(range(100), desc="Processing"):
        time.sleep(0.1)  # Simulate work
        
    progress(1.0, desc="Complete!")
    return "Task finished!"

def manual_progress(progress=gr.Progress()):
    total_steps = 50
    
    for i in range(total_steps):
        # Update with fraction
        progress(i / total_steps, desc=f"Step {i+1}/{total_steps}")
        time.sleep(0.1)
        
    return "Manual progress complete!"

# Use in interface
gr.Interface(
    fn=long_task,
    inputs=None,
    outputs="text"
)

def Info(message):
    """
    Display information notification.

    Parameters:
    - message: Information message to display
    """

def Warning(message):
    """
    Display warning notification.

    Parameters:
    - message: Warning message to display
    """

def Success(message):
    """
    Display success notification.

    Parameters:
    - message: Success message to display
    """

class Error(Exception):
    """
    Gradio-specific error exception for user-facing error messages.

    Parameters:
    - message: Error message to display to user
    """
    
    def __init__(self, message):
        self.message = message
        super().__init__(message)

import gradio as gr

def process_data(data):
    if not data:
        gr.Warning("No data provided, using defaults")
        data = "default_value"
    
    try:
        result = complex_operation(data)
        gr.Success("Data processed successfully!")
        return result
    except ValueError as e:
        gr.Error(f"Invalid data format: {e}")
        return None
    except Exception as e:
        gr.Info("Processing failed, but this is recoverable")
        return fallback_processing(data)

def validate_input(text):
    if len(text) < 5:
        raise gr.Error("Input must be at least 5 characters long")
    
    gr.Info("Input validation passed")
    return text.upper()

class Examples:
    def __init__(
        self,
        examples,
        inputs,
        outputs=None,
        fn=None,
        cache_examples=None,
        examples_per_page=10,
        label="Examples",
        elem_id=None,
        run_on_click=False,
        preprocess=True,
        postprocess=True,
        api_name="load_example",
        batch=False,
        **kwargs
    ):
        """
        Example data management and display system.

        Parameters:
        - examples: List of example inputs (one per row)
        - inputs: List of input components that examples correspond to
        - outputs: List of output components to populate (optional)
        - fn: Function to run when example is selected
        - cache_examples: Whether to cache example outputs
        - examples_per_page: Number of examples to show per page
        - label: Label for examples section
        - run_on_click: Whether to run function when example clicked
        - preprocess: Whether to preprocess example data
        - postprocess: Whether to postprocess example outputs
        - api_name: API endpoint name for examples
        - batch: Whether to process examples in batches
        """

    def create(self):
        """Create examples component."""

    def load_from_cache(self, example_id):
        """Load cached example result."""

    def cache(self):
        """Cache all example results."""

import gradio as gr

def text_classifier(text):
    # Simple sentiment classification
    if "good" in text.lower() or "great" in text.lower():
        return "Positive"
    elif "bad" in text.lower() or "terrible" in text.lower():
        return "Negative"
    else:
        return "Neutral"

with gr.Blocks() as demo:
    text_input = gr.Textbox(label="Text to classify")
    classification_output = gr.Textbox(label="Classification")
    submit_btn = gr.Button("Classify")
    
    # Add examples
    examples = gr.Examples(
        examples=[
            ["This movie is really great!"],
            ["I had a terrible experience."],
            ["The weather is okay today."],
            ["Absolutely fantastic service!"],
            ["Not good at all, very disappointing."]
        ],
        inputs=text_input,
        outputs=classification_output,
        fn=text_classifier,
        cache_examples=True,
        run_on_click=True
    )
    
    submit_btn.click(text_classifier, text_input, classification_output)

def update(
    value=None,
    visible=None,
    interactive=None,
    label=None,
    info=None,
    elem_id=None,
    elem_classes=None,
    **kwargs
):
    """
    Component property update utility for dynamic interface changes.

    Parameters:
    - value: New component value
    - visible: Whether component should be visible
    - interactive: Whether component should be interactive
    - label: New component label
    - info: New info text
    - elem_id: New element ID
    - elem_classes: New CSS classes
    - Additional component-specific properties
    
    Returns:
    - Update dictionary for component modification
    """

def skip():
    """
    Processing skip flag for conditional execution.
    
    Returns:
    - Special value indicating to skip this output
    """

import gradio as gr

def toggle_component(visible):
    if visible:
        return gr.update(visible=False, value="Hidden!")
    else:
        return gr.update(visible=True, value="Visible!")

def conditional_processing(input_text, should_process):
    if not should_process:
        return gr.skip()  # Don't update output
    
    return input_text.upper()

def dynamic_update(choice):
    if choice == "text":
        return gr.update(
            label="Text Input",
            placeholder="Enter text here...",
            visible=True,
            interactive=True
        )
    elif choice == "number":
        return gr.update(
            label="Number Input", 
            placeholder="Enter number...",
            visible=True,
            interactive=True
        )
    else:
        return gr.update(visible=False)

with gr.Blocks() as demo:
    choice = gr.Radio(["text", "number", "hidden"], label="Input Type")
    dynamic_input = gr.Textbox(label="Dynamic Input")
    toggle_btn = gr.Button("Toggle Visibility")
    
    choice.change(dynamic_update, choice, dynamic_input)
    toggle_btn.click(toggle_component, dynamic_input, dynamic_input)

def render(component):
    """
    Component rendering and display function.

    Parameters:
    - component: Component to render
    
    Returns:
    - Rendered component
    """

class FileSize:
    def __init__(self, size_bytes):
        """
        File size utility class with formatting.

        Parameters:
        - size_bytes: File size in bytes
        """

    def human_readable(self):
        """Convert to human-readable format (KB, MB, GB)."""

    def to_mb(self):
        """Convert to megabytes."""

    def to_gb(self):
        """Convert to gigabytes."""

# Configuration constants
NO_RELOAD = object()  # Flag to disable automatic reload functionality
IS_WASM = False  # WebAssembly environment detection flag

def get_package_version():
    """
    Package version retrieval utility.
    
    Returns:
    - Current Gradio version string
    """

def set_static_paths(paths):
    """
    Static file path configuration for custom assets.

    Parameters:
    - paths: List of paths to serve as static files
    """

import gradio as gr

def file_info(file):
    if file is None:
        return "No file uploaded"
    
    # Get file size
    size = gr.FileSize(file.size)
    
    return f"""
    File: {file.orig_name}
    Size: {size.human_readable()}
    Path: {file.path}
    """

def check_environment():
    version = gr.get_package_version()
    is_wasm = gr.IS_WASM
    
    return f"Gradio version: {version}, WASM: {is_wasm}"

# Configure static file serving
gr.set_static_paths(["/path/to/custom/assets"])

with gr.Blocks() as demo:
    file_input = gr.File(label="Upload File")
    file_info_output = gr.Textbox(label="File Information")
    env_info_output = gr.Textbox(label="Environment")
    
    file_input.change(file_info, file_input, file_info_output)
    
    # Show environment info on load
    demo.load(check_environment, outputs=env_info_output)

class BrowserState:
    def __init__(self, value=None, **kwargs):
        """
        Browser-specific state management for client-side persistence.

        Parameters:
        - value: Initial state value
        """

def close_all():
    """Close all running Gradio interfaces and clean up resources."""

import gradio as gr

def increment_session_counter(count):
    return count + 1

def reset_session_counter():
    return 0

def save_to_browser(data):
    # Browser state persists across page reloads
    return data

with gr.Blocks() as demo:
    # Session state (resets on page reload)
    session_count = gr.State(0)
    
    # Browser state (persists across reloads)
    persistent_data = gr.BrowserState("")
    
    count_display = gr.Number(label="Session Count")
    increment_btn = gr.Button("Increment")
    reset_btn = gr.Button("Reset")
    
    persistent_input = gr.Textbox(label="Persistent Data")
    save_btn = gr.Button("Save to Browser")
    
    increment_btn.click(
        increment_session_counter,
        session_count,
        [session_count, count_display]
    )
    
    reset_btn.click(
        reset_session_counter,
        outputs=[session_count, count_display]
    )
    
    save_btn.click(
        save_to_browser,
        persistent_input,
        persistent_data
    )

class I18n:
    def __init__(self, locale="en", translations=None):
        """
        Internationalization and localization support.

        Parameters:
        - locale: Default locale code (e.g., "en", "es", "fr")
        - translations: Dictionary of translations
        """

    def t(self, key, **kwargs):
        """
        Translate text key with optional parameters.

        Parameters:
        - key: Translation key
        - kwargs: Variables for string formatting
        
        Returns:
        - Translated string
        """

    def set_locale(self, locale):
        """Change current locale."""

    def load_translations(self, translations):
        """Load translation dictionary."""

import gradio as gr

# Set up internationalization
i18n = gr.I18n("en", {
    "en": {
        "welcome": "Welcome to the app!",
        "submit": "Submit",
        "result": "Result: {value}"
    },
    "es": {
        "welcome": "¡Bienvenido a la aplicación!",
        "submit": "Enviar", 
        "result": "Resultado: {value}"
    }
})

def process_with_i18n(text, locale):
    i18n.set_locale(locale)
    
    result = text.upper()
    return i18n.t("result", value=result)

with gr.Blocks() as demo:
    gr.Markdown(i18n.t("welcome"))
    
    text_input = gr.Textbox()
    locale_choice = gr.Radio(["en", "es"], value="en", label="Language")
    submit_btn = gr.Button(i18n.t("submit"))
    output = gr.Textbox()
    
    submit_btn.click(process_with_i18n, [text_input, locale_choice], output)

def load_ipython_extension(ipython):
    """
    Jupyter notebook integration for IPython environments.

    Parameters:
    - ipython: IPython instance for magic command registration
    """

# Load Gradio IPython extension
%load_ext gradio

# Use Gradio magic commands
%%gradio
def my_function(x):
    return x * 2

gr.Interface(my_function, "number", "number")

# Disable auto-reload for production
demo = gr.Blocks()
demo.queue(default_concurrency_limit=10)

# Configure static file caching
gr.set_static_paths(["/static"], cache_headers=True)

# Use NO_RELOAD flag
if gr.NO_RELOAD:
    # Production configuration
    demo.launch(server_name="0.0.0.0", server_port=7860)
else:
    # Development configuration
    demo.launch(share=True, debug=True)