app_v2_backup.py

import marimo

__generated_with = "0.11.16"
app = marimo.App(width="medium")


@app.cell
def _():
    import marimo as mo
    import os
    return mo, os


@app.cell
def _():
    def get_markdown_content(file_path):
        with open(file_path, 'r', encoding='utf-8') as file:
            content = file.read()
        return content
    return (get_markdown_content,)


@app.cell
def _(get_markdown_content, mo):
    intro_text = get_markdown_content('intro_markdown/intro.md')
    intro_marimo = get_markdown_content('intro_markdown/intro_marimo.md')
    intro_notebook = get_markdown_content('intro_markdown/intro_notebook.md')
    intro_comparison = get_markdown_content('intro_markdown/intro_comparison.md')

    intro = mo.carousel([
        mo.md(f"{intro_text}"),
        mo.md(f"{intro_marimo}"),
        mo.md(f"{intro_notebook}"),
        mo.md(f"{intro_comparison}"),
    ])

    mo.accordion({"## Notebook Introduction":intro})
    return intro, intro_comparison, intro_marimo, intro_notebook, intro_text


@app.cell
def _(os):
    ### Imports
    from typing import (
        Any, Dict, List, Optional, Pattern, Set, Union, Tuple
    )
    from pathlib import Path
    from urllib.request import urlopen
    # from rich.markdown import Markdown as Markd
    from rich.text import Text
    from rich import print
    from tqdm import tqdm
    from enum import Enum
    import pandas as pd
    import tempfile
    import requests
    import getpass
    import urllib3
    import base64
    import time
    import json
    import uuid
    import ssl
    import ast
    import re

    pd.set_option('display.max_columns', None)  
    pd.set_option('display.max_rows', None)     
    pd.set_option('display.max_colwidth', None) 
    pd.set_option('display.width', None)     

    # Set explicit temporary directory
    os.environ['TMPDIR'] = '/tmp'

    # Make sure Python's tempfile module also uses this directory
    tempfile.tempdir = '/tmp'
    return (
        Any,
        Dict,
        Enum,
        List,
        Optional,
        Path,
        Pattern,
        Set,
        Text,
        Tuple,
        Union,
        ast,
        base64,
        getpass,
        json,
        pd,
        print,
        re,
        requests,
        ssl,
        tempfile,
        time,
        tqdm,
        urllib3,
        urlopen,
        uuid,
    )


@app.cell
def _(mo):
    ### Credentials for the watsonx.ai SDK client

    # Endpoints
    wx_platform_url = "https://api.dataplatform.cloud.ibm.com"
    regions = {
        "US": "https://us-south.ml.cloud.ibm.com",
        "EU": "https://eu-de.ml.cloud.ibm.com",
        "GB": "https://eu-gb.ml.cloud.ibm.com",
        "JP": "https://jp-tok.ml.cloud.ibm.com",
        "AU": "https://au-syd.ml.cloud.ibm.com",
        "CA": "https://ca-tor.ml.cloud.ibm.com"
    }

    # Create a form with multiple elements
    client_instantiation_form = (
        mo.md('''
        ###**watsonx.ai credentials:**

        {wx_region}

        {wx_api_key}

        {space_id}
    ''').style(max_height="300px", overflow="auto", border_color="blue")
        .batch(
            wx_region = mo.ui.dropdown(regions, label="Select your watsonx.ai region:", value="US", searchable=True),
            wx_api_key = mo.ui.text(placeholder="Add your IBM Cloud api-key...", label="IBM Cloud Api-key:", kind="password"),
            # project_id = mo.ui.text(placeholder="Add your watsonx.ai project_id...", label="Project_ID:", kind="text"),
            space_id = mo.ui.text(placeholder="Add your watsonx.ai space_id...", label="Space_ID:", kind="text")
        ,)
        .form(show_clear_button=True, bordered=False)
    )


    # client_instantiation_form
    return client_instantiation_form, regions, wx_platform_url


@app.cell
def _(client_instantiation_form, mo):
    from ibm_watsonx_ai import APIClient, Credentials

    def setup_task_credentials(deployment_client):
        # Get existing task credentials
        existing_credentials = deployment_client.task_credentials.get_details()

        # Delete existing credentials if any
        if "resources" in existing_credentials and existing_credentials["resources"]:
            for cred in existing_credentials["resources"]:
                cred_id = deployment_client.task_credentials.get_id(cred)
                deployment_client.task_credentials.delete(cred_id)

        # Store new credentials
        return deployment_client.task_credentials.store()

    if client_instantiation_form.value:
    ### Instantiate the watsonx.ai client
        wx_credentials = Credentials(
            url=client_instantiation_form.value["wx_region"],
            api_key=client_instantiation_form.value["wx_api_key"]
        )

        # project_client = APIClient(credentials=wx_credentials, project_id=client_instantiation_form.value["project_id"])
        deployment_client = APIClient(credentials=wx_credentials, space_id=client_instantiation_form.value["space_id"])

        task_credentials_details = setup_task_credentials(deployment_client)
    else:
        # project_client = None
        deployment_client = None
        task_credentials_details = None

    template_variant = mo.ui.dropdown(["Base","Stream Files to IBM COS [Example]"], label="Code Template:", value="Base")

    if deployment_client is not None:
        client_callout_kind = "success"
    else:
        client_callout_kind = "neutral"

    client_callout = mo.callout(template_variant, kind=client_callout_kind)

    # client_callout
    return (
        APIClient,
        Credentials,
        client_callout,
        client_callout_kind,
        deployment_client,
        setup_task_credentials,
        task_credentials_details,
        template_variant,
        wx_credentials,
    )


@app.cell
def _(
    client_callout,
    client_instantiation_form,
    deploy_fnc,
    deployment_definition,
    fm,
    function_editor,
    hw_selection_table,
    mo,
    purge_tabs,
    sc_m,
    schema_editors,
    selection_table,
    upload_func,
):
    s1 = mo.md(f'''
        ###**Instantiate your watsonx.ai client:**

        1. Select a region from the dropdown menu

        2. Provide an IBM Cloud Apikey and watsonx.ai deployment space id

        3. Once you submit, the area with the code template will turn green if successful

        4. Select a base (provide baseline format) or example code function template

        ---

        {client_instantiation_form}

        ---

        {client_callout}

    ''')

    sc_tabs = mo.ui.tabs(
        {
            "Schema Option Selection": sc_m,
            "Schema Definition": mo.md(f"""
            ####**Edit the schema definitions you selected in the previous tab.**<br>
                                       {schema_editors}"""),
        }
    )

    s2 = mo.md(f'''###**Create your function from the template:**

        1. Use the code editor window to create a function to deploy
        <br>
           The function must:
           <br>
                --- Include a payload and score element
                <br>
                --- Have the same function name in both the score = <name>() segment and the Function Name input field below
                <br>
                --- Additional details can be found here -> [watsonx.ai - Writing deployable Python functions
    ](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/ml-deploy-py-function-write.html?utm_medium=Exinfluencer&utm_source=ibm_developer&utm_content=in_content_link&utm_term=10006555&utm_id=blogs_awb-tekton-optimizations-for-kubeflow-pipelines-2-0&context=wx&audience=wdp)

        3. Click submit, then proceed to select whether you wish to add:
        <br>
                --- An input schema (describing the format of the variables the function takes) **[Optional]**
            <br>
                --- An output schema (describing the format of the output results the function returns) **[Optional]**
            <br>
                --- An sample input example (showing an example of a mapping of the input and output schema to actual values.) **[Optional]**

        4. Fill in the function name field **(must be exactly the same as in the function editor)**

        5. Add a description and metadata tags **[Optional]**

    ---

        {function_editor}

    ---

        {sc_tabs}

    ---

        {fm}

    ''')

    s3 = mo.md(f'''
        ###**Review and Upload your function**

        1. Review the function metadata specs JSON

        2. Select a software specification if necessary (default for python functions is pre-selected), this is the runtime environment of python that your function will run in. Environments on watsonx.ai come pre-packaged with many different libraries, if necessary install new ones by adding them into the function as a `subprocess.check_output('pip install <package_name>', shell=True)` command.

        3. Once your are satisfied, click the upload function button and wait for the response.

        > If you see no table of software specs, you haven't activated your watsonx.ai client.

        ---

        {selection_table}

        ---

        {upload_func}

    ''')

    s4 = mo.md(f'''
        ###**Deploy your function:**

        1. Select a hardware specification (vCPUs/GB) that you want your function deployed on
        <br>
        --- XXS and XS cost the same (0.5 CUH per hour, so XS is the better option
        <br>
        --- Select larger instances for more resource intensive tasks or runnable jobs

        2. Select the type of deployment:
        <br>
        --- Function (Online) for always-on endpoints - Always available and low latency, but consume resources continuously for every hour they are deployed.
        <br>
        --- Batch (Batch) for runnable jobs - Only consume resources during job runs, but aren't as flexible to deploy.

        3. If you've selected Function, pick a completely unique (globally, not just your account) deployment serving name that will be in the endpoint url.

        4. Once your are satisfied, click the deploy function button and wait for the response.

        ---

        {hw_selection_table}

        ---

        {deployment_definition}

        ---

        {deploy_fnc}

    ''')

    s5 = mo.md(f'''
        ###**Helper Purge Functions:**

        These functions help you retrieve and mass delete ***(WARNING: purges all at once)*** deployments, data assets or repository assets (functions, models, etc.) that you have in the deployment space. This is meant to support fast cleanup.

        Select the tab based on what you want to delete, then click each of the buttons one by one after the previous gives a response.
    
        ---

        {purge_tabs}

    ''')

    sections = mo.accordion(
        {
            "Section 1: **watsonx.ai Credentials**": s1,
            "Section 2: **Function Creation**": s2,
            "Section 3: **Function Upload**": s3,
            "Section 4: **Function Deployment**": s4,
            "Section 5: **Helper Functions**": s5,
        }, 
        multiple=True
    )

    sections
    return s1, s2, s3, s4, s5, sc_tabs, sections


@app.cell
def _(mo, template_variant):
    # Template for WatsonX.ai deployable function
    if template_variant.value == "Stream Files to IBM COS [Example]":
        with open("stream_files_to_cos.py", "r") as file:
            template = file.read()
    else:
        template = '''def your_function_name():

        import subprocess
        subprocess.check_output('pip install gensim', shell=True)
        import gensim

        def score(input_data):
            message_from_input_payload = payload.get("input_data")[0].get("values")[0][0]
            response_message = "Received message - {0}".format(message_from_input_payload)

            # Score using the pre-defined model
            score_response = {
                'predictions': [{'fields': ['Response_message_field', 'installed_lib_version'],
                                'values': [[response_message, gensim.__version__]]
                                }]
            }
            return score_response

        return score

        score = your_function_name()
            '''

    function_editor = (
        mo.md('''
        #### **Create your function by editing the template:**

        {editor}

        ''')
        .batch(
            editor = mo.ui.code_editor(value=template, language="python", min_height=50)
        )
        .form(show_clear_button=True, bordered=False)
        )

    # function_editor
    return file, function_editor, template


@app.cell
def _(function_editor, mo, os):
    if function_editor.value:
        # Get the edited code from the function editor
        code = function_editor.value['editor']
        # Create a namespace to execute the code in
        namespace = {}
        # Execute the code
        exec(code, namespace)

        # Find the first function defined in the namespace
        function_name = None
        for name, obj in namespace.items():
            if callable(obj) and name != "__builtins__":
                function_name = name
                break

        if function_name:
            # Instantiate the deployable function
            deployable_function = namespace[function_name]
            # Now deployable_function contains the score function
            mo.md(f"Created deployable function from '{function_name}'")
            # Create the directory if it doesn't exist
            save_dir = "/tmp/notebook_functions"
            os.makedirs(save_dir, exist_ok=True)
            # Save the function code to a file
            file_path = os.path.join(save_dir, f"{function_name}.py")
            with open(file_path, "w") as f:
                f.write(code)
        else:
            mo.md("No function found in the editor code")
    return (
        code,
        deployable_function,
        f,
        file_path,
        function_name,
        name,
        namespace,
        obj,
        save_dir,
    )


@app.cell
def _(deployment_client, mo, pd):
    if deployment_client:
        supported_specs = deployment_client.software_specifications.list()[
            deployment_client.software_specifications.list()['STATE'] == 'supported'
        ]

        # Reset the index to start from 0
        supported_specs = supported_specs.reset_index(drop=True)

        # Create a mapping dictionary for framework names based on software specifications
        framework_mapping = {
            "tensorflow_rt24.1-py3.11": "TensorFlow",
            "pytorch-onnx_rt24.1-py3.11": "PyTorch",
            "onnxruntime_opset_19": "ONNX or ONNXRuntime",
            "runtime-24.1-py3.11": "AI Services/Python Functions/Python Scripts",
            "autoai-ts_rt24.1-py3.11": "AutoAI",
            "autoai-kb_rt24.1-py3.11": "AutoAI",
            "runtime-24.1-py3.11-cuda": "CUDA-enabled (GPU) Python Runtime",
            "runtime-24.1-r4.3": "R Runtime 4.3",
            "spark-mllib_3.4": "Apache Spark 3.4",
            "autoai-rag_rt24.1-py3.11": "AutoAI RAG"
        }

        # Define the preferred order for items to appear at the top
        preferred_order = [
            "runtime-24.1-py3.11",
            "runtime-24.1-py3.11-cuda",
            "runtime-24.1-r4.3",
            "ai-service-v5-software-specification",
            "autoai-rag_rt24.1-py3.11",
            "autoai-ts_rt24.1-py3.11",
            "autoai-kb_rt24.1-py3.11",
            "tensorflow_rt24.1-py3.11",
            "pytorch-onnx_rt24.1-py3.11",
            "onnxruntime_opset_19",
            "spark-mllib_3.4",
        ]

        # Create a new column for sorting
        supported_specs['SORT_ORDER'] = supported_specs['NAME'].apply(
            lambda x: preferred_order.index(x) if x in preferred_order else len(preferred_order)
        )

        # Sort the DataFrame by the new column
        supported_specs = supported_specs.sort_values('SORT_ORDER').reset_index(drop=True)

        # Drop the sorting column as it's no longer needed
        supported_specs = supported_specs.drop(columns=['SORT_ORDER'])

        # Drop the REPLACEMENT column if it exists and add NOTES column
        if 'REPLACEMENT' in supported_specs.columns:
            supported_specs = supported_specs.drop(columns=['REPLACEMENT'])

        # Add NOTES column with framework information
        supported_specs['NOTES'] = supported_specs['NAME'].map(framework_mapping).fillna("Other")

        # Create a table with single-row selection
        selection_table = mo.ui.table(
            supported_specs,
            selection="single",  # Only allow selecting one row
            label="#### **Select a supported software_spec runtime for your function asset** (For Python Functions select - *'runtime-24.1-py3.11'* ):",
            initial_selection=[0],  # Now selecting the first row, which should be runtime-24.1-py3.11
            page_size=6
        )
    else:
        sel_df = pd.DataFrame(
        data=[["ID", "Activate deployment_client."]], 
        columns=["ID", "VALUE"]
        )

        selection_table = mo.ui.table(
            sel_df,
            selection="single",  # Only allow selecting one row
            label="You haven't activated the Deployment_Client",
            initial_selection=[0]
        )

    # # Display the table
    # mo.md(f"""---
    # <br>
    # <br>
    # {selection_table}
    # <br>
    # <br>
    # ---
    # <br>
    # <br>
    # """)
    return (
        framework_mapping,
        preferred_order,
        sel_df,
        selection_table,
        supported_specs,
    )


@app.cell
def _(mo):
    input_schema_checkbox = mo.ui.checkbox(label="Add input schema (optional)")
    output_schema_checkbox = mo.ui.checkbox(label="Add output schema (optional)")
    sample_input_checkbox = mo.ui.checkbox(label="Add sample input example (optional)")
    return input_schema_checkbox, output_schema_checkbox, sample_input_checkbox


@app.cell
def _(
    input_schema_checkbox,
    mo,
    output_schema_checkbox,
    sample_input_checkbox,
    selection_table,
    template_variant,
):
    if selection_table.value['ID'].iloc[0]:
        # Create the input fields
        if template_variant.value == "Stream Files to IBM COS [Example]":
            fnc_nm = "stream_file_to_cos"
        else:
            fnc_nm = "your_function_name"

        uploaded_function_name = mo.ui.text(placeholder="<Must be the same as the name in editor>", label="Function Name:", kind="text", value=f"{fnc_nm}", full_width=False)
        tags_editor = mo.ui.array(
            [mo.ui.text(placeholder="Metadata Tags..."), mo.ui.text(), mo.ui.text()],
            label="Optional Metadata Tags"
        )
        software_spec = selection_table.value['ID'].iloc[0]

        description_input = mo.ui.text_area(
            placeholder="Write a description for your function...)",
            label="Description",
            max_length=256,
            rows=5,
            full_width=True
        )


        func_metadata=mo.hstack([
            description_input,
            mo.hstack([
                uploaded_function_name,
                tags_editor,
            ], justify="start", gap=1, align="start", wrap=True)
        ],
            widths=[0.6,0.4],
            gap=2.75
        )

        schema_metadata=mo.hstack([
            input_schema_checkbox,
            output_schema_checkbox,
            sample_input_checkbox
        ],
            justify="center", gap=1, align="center", wrap=True
        )

    # Display the metadata inputs
    # mo.vstack([
    #     func_metadata,
    #     mo.md("**Make sure to click the checkboxes before filling in descriptions and tags or they will reset.**"),
    #     schema_metadata
    #     ],
    #     align="center",
    #     gap=2
    # )
    fm = mo.vstack([
        func_metadata,
        ],
        align="center",
        gap=2
    )
    sc_m = mo.vstack([
        schema_metadata,
        mo.md("**Make sure to select the checkbox options before filling in descriptions and tags or they will reset.**")
        ],
        align="center",
        gap=2
    )
    return (
        description_input,
        fm,
        fnc_nm,
        func_metadata,
        sc_m,
        schema_metadata,
        software_spec,
        tags_editor,
        uploaded_function_name,
    )


@app.cell
def _(json, mo, template_variant):
    if template_variant.value == "Stream Files to IBM COS [Example]":
        from cos_stream_schema_examples import input_schema, output_schema, sample_input
    else:
        input_schema = [
            {
                'id': '1',
                'type': 'struct',
                'fields': [
                    {
                    'name': '<variable name 1>', 
                    'type': 'string',
                    'nullable': False, 
                    'metadata': {}
                    },
                    {
                    'name': '<variable name 2>', 
                    'type': 'string', 
                    'nullable': False, 
                    'metadata': {}
                    }
                ]
            }
        ]

        output_schema = [
            {
                'id': '1',
                'type': 'struct',
                'fields': [
                    {
                    'name': '<output return name>', 
                    'type': 'string', 
                    'nullable': False, 
                    'metadata': {}
                    }
                ]
            }
        ]

        sample_input = {
            'input_data': [
                {
                    'fields': ['<variable name 1>', '<variable name 2>'],
                    'values': [
                        ['<sample input value for variable 1>', '<sample input value for variable 2>']
                    ]
                }
            ]
        }


    input_schema_editor = mo.ui.code_editor(value=json.dumps(input_schema, indent=4), language="python", min_height=25)
    output_schema_editor = mo.ui.code_editor(value=json.dumps(output_schema, indent=4), language="python", min_height=25)
    sample_input_editor = mo.ui.code_editor(value=json.dumps(sample_input, indent=4), language="python", min_height=25)

    schema_editors = mo.accordion(
        {
            """**Input Schema Metadata Editor**""": input_schema_editor,
            """**Output Schema Metadata Editor**""": output_schema_editor,
            """**Sample Input Metadata Editor**""": sample_input_editor
        }, multiple=True
    )

    # schema_editors
    return (
        input_schema,
        input_schema_editor,
        output_schema,
        output_schema_editor,
        sample_input,
        sample_input_editor,
        schema_editors,
    )


@app.cell
def _(
    ast,
    deployment_client,
    description_input,
    function_editor,
    input_schema_checkbox,
    input_schema_editor,
    json,
    mo,
    os,
    output_schema_checkbox,
    output_schema_editor,
    sample_input_checkbox,
    sample_input_editor,
    selection_table,
    software_spec,
    tags_editor,
    uploaded_function_name,
):
    get_upload_status, set_upload_status = mo.state("No uploads yet")

    function_meta = {}

    if selection_table.value['ID'].iloc[0] and deployment_client is not None:
        # Start with the base required fields
        function_meta = {
            deployment_client.repository.FunctionMetaNames.NAME: f"{uploaded_function_name.value}" or "your_function_name",
            deployment_client.repository.FunctionMetaNames.SOFTWARE_SPEC_ID: software_spec or "45f12dfe-aa78-5b8d-9f38-0ee223c47309"
        }

        # Add optional fields if they exist
        if tags_editor.value:
            # Filter out empty strings from the tags list
            filtered_tags = [tag for tag in tags_editor.value if tag and tag.strip()]
            if filtered_tags:  # Only add if there are non-empty tags
                function_meta[deployment_client.repository.FunctionMetaNames.TAGS] = filtered_tags


        if description_input.value:
            function_meta[deployment_client.repository.FunctionMetaNames.DESCRIPTION] = description_input.value

        # Add input schema if checkbox is checked
        if input_schema_checkbox.value:
            try:
                function_meta[deployment_client.repository.FunctionMetaNames.INPUT_DATA_SCHEMAS] = json.loads(input_schema_editor.value)
            except json.JSONDecodeError:
                # If JSON parsing fails, try Python literal evaluation as fallback
                function_meta[deployment_client.repository.FunctionMetaNames.INPUT_DATA_SCHEMAS] = ast.literal_eval(input_schema_editor.value)

        # Add output schema if checkbox is checked
        if output_schema_checkbox.value:
            try:
                function_meta[deployment_client.repository.FunctionMetaNames.OUTPUT_DATA_SCHEMAS] = json.loads(output_schema_editor.value)
            except json.JSONDecodeError:
                # If JSON parsing fails, try Python literal evaluation as fallback
                function_meta[deployment_client.repository.FunctionMetaNames.OUTPUT_DATA_SCHEMAS] = ast.literal_eval(output_schema_editor.value)

        # Add sample input if checkbox is checked
        if sample_input_checkbox.value:
            try:
                function_meta[deployment_client.repository.FunctionMetaNames.SAMPLE_SCORING_INPUT] = json.loads(sample_input_editor.value)
            except json.JSONDecodeError:
                # If JSON parsing fails, try Python literal evaluation as fallback
                function_meta[deployment_client.repository.FunctionMetaNames.SAMPLE_SCORING_INPUT] = ast.literal_eval(sample_input_editor.value)

    def upload_function(function_meta, use_function_object=True):
        """
        Uploads a Python function to watsonx.ai as a deployable asset.
        Parameters:
        function_meta (dict): Metadata for the function
        use_function_object (bool): Whether to use function object (True) or file path (False)
        Returns:
        dict: Details of the uploaded function
        """
        # Store the original working directory
        original_dir = os.getcwd()

        try:
            # Create temp file from the code in the editor
            code_to_deploy = function_editor.value['editor']
            # This function is defined elsewhere in the notebook
            func_name = uploaded_function_name.value or "your_function_name"
            # Ensure function_meta has the correct function name
            function_meta[deployment_client.repository.FunctionMetaNames.NAME] = func_name
            # Save the file locally first
            save_dir = "/tmp/notebook_functions"
            os.makedirs(save_dir, exist_ok=True)
            file_path = f"{save_dir}/{func_name}.py"
            with open(file_path, "w", encoding="utf-8") as f:
                f.write(code_to_deploy)

            if use_function_object:
                # Import the function from the file
                import sys
                import importlib.util
                # Add the directory to Python's path
                sys.path.append(save_dir)
                # Import the module
                spec = importlib.util.spec_from_file_location(func_name, file_path)
                module = importlib.util.module_from_spec(spec)
                spec.loader.exec_module(module)
                # Get the function object
                function_object = getattr(module, func_name)

                # Change to /tmp directory before calling IBM Watson SDK functions
                os.chdir('/tmp')

                # Upload the function object
                mo.md(f"Uploading function object: {func_name}")
                func_details = deployment_client.repository.store_function(function_object, function_meta)
            else:
                # Change to /tmp directory before calling IBM Watson SDK functions
                os.chdir('/tmp')

                # Upload using the file path approach
                mo.md(f"Uploading function from file: {file_path}")
                func_details = deployment_client.repository.store_function(file_path, function_meta)

            set_upload_status(f"Latest Upload - id - {func_details['metadata']['id']}")
            return func_details
        except Exception as e:
            set_upload_status(f"Error uploading function: {str(e)}")
            mo.md(f"Detailed error: {str(e)}")
            raise
        finally:
            # Always change back to the original directory, even if an exception occurs
            os.chdir(original_dir)

    upload_status =  mo.state("No uploads yet")

    upload_button = mo.ui.button(
        label="Upload Function",
        on_click=lambda _: upload_function(function_meta, use_function_object=True),
        kind="success",
        tooltip="Click to upload function to watsonx.ai"
    )

    # function_meta
    return (
        filtered_tags,
        function_meta,
        get_upload_status,
        set_upload_status,
        upload_button,
        upload_function,
        upload_status,
    )


@app.cell
def _(get_upload_status, mo, upload_button):
    # Upload your function 
    if upload_button.value:
        try:
            upload_result = upload_button.value
            artifact_id = upload_result['metadata']['id']
        except Exception as e:
            mo.md(f"Error: {str(e)}")

    upload_func = mo.vstack([
        upload_button,
        mo.md(f"**Status:** {get_upload_status()}")
    ], justify="space-around", align="center")
    return artifact_id, upload_func, upload_result


@app.cell
def _(deployment_client, mo, pd, upload_button, uuid):
    def reorder_hardware_specifications(df):
        """
        Reorders a hardware specifications dataframe by type and size of environment
        without hardcoding specific hardware types.

        Parameters:
        df (pandas.DataFrame): The hardware specifications dataframe to reorder

        Returns:
        pandas.DataFrame: Reordered dataframe with reset index
        """
        # Create a copy to avoid modifying the original dataframe
        result_df = df.copy()

        # Define a function to extract the base type and size
        def get_sort_key(name):
            # Create a custom ordering list
            custom_order = [
                "XXS", "XS", "S", "M", "L", "XL",
                "XS-Spark", "S-Spark", "M-Spark", "L-Spark", "XL-Spark",
                "K80", "K80x2", "K80x4",
                "V100", "V100x2",
                "WXaaS-XS", "WXaaS-S", "WXaaS-M", "WXaaS-L", "WXaaS-XL",
                "Default Spark", "Notebook Default Spark", "ML"
            ]

            # If name is in the custom order list, use its index
            if name in custom_order:
                return (0, custom_order.index(name))

            # For any name not in the custom order, put it at the end
            return (1, name)

        # Add a temporary column for sorting
        result_df['sort_key'] = result_df['NAME'].apply(get_sort_key)

        # Sort the dataframe and drop the temporary column
        result_df = result_df.sort_values('sort_key').drop('sort_key', axis=1)

        # Reset the index
        result_df = result_df.reset_index(drop=True)

        return result_df

    if deployment_client and upload_button.value:

        hardware_specs = deployment_client.hardware_specifications.list()
        hardware_specs_df = reorder_hardware_specifications(hardware_specs)

        # Create a table with single-row selection
        hw_selection_table = mo.ui.table(
            hardware_specs_df,
            selection="single",  # Only allow selecting one row
            label="#### **Select a supported hardware_specification for your deployment** *(Default: 'XS' - 1vCPU_4GB Ram)*",
            initial_selection=[1],
            page_size=6,
            wrapped_columns=['DESCRIPTION']
        )

        deployment_type = mo.ui.radio(
        options={"Function":"Online (Function Endpoint)","Runnable Job":"Batch (Runnable Jobs)"}, value="Function", label="Select the Type of Deployment:", inline=True
        )
        uuid_suffix = str(uuid.uuid4())[:4]

        deployment_name = mo.ui.text(value=f"deployed_func_{uuid_suffix}", label="Deployment Name:", placeholder="<Must be completely unique>")
    else:
        hw_df = pd.DataFrame(
        data=[["ID", "Activate deployment_client."]], 
        columns=["ID", "VALUE"]
        )

        hw_selection_table = mo.ui.table(
            hw_df,
            selection="single",  # Only allow selecting one row
            label="You haven't activated the Deployment_Client",
            initial_selection=[0]
        )


    # mo.md(f"""
    # <br>
    # <br>
    # {upload_func}
    # <br>
    # <br>
    # ---
    # {hw_selection_table}
    # <br>
    # <br>


    # """)
    return (
        deployment_name,
        deployment_type,
        hardware_specs,
        hardware_specs_df,
        hw_df,
        hw_selection_table,
        reorder_hardware_specifications,
        uuid_suffix,
    )


@app.cell
def _(
    artifact_id,
    deployment_client,
    deployment_details,
    deployment_name,
    deployment_type,
    hw_selection_table,
    mo,
    print,
    upload_button,
):
    def deploy_function(artifact_id, deployment_type):
        """
        Deploys a function asset to watsonx.ai.

        Parameters:
        artifact_id (str): ID of the function artifact to deploy
        deployment_type (object): Type of deployment (online or batch)

        Returns:
        dict: Details of the deployed function
        """
        if not artifact_id:
            print("Error: No artifact ID provided. Please upload a function first.")
            return None

        if deployment_type.value == "Online (Function Endpoint)":  # Changed from "Online (Function Endpoint)"
            deployment_props = {
                deployment_client.deployments.ConfigurationMetaNames.NAME: deployment_name.value,
                deployment_client.deployments.ConfigurationMetaNames.ONLINE: {},
                deployment_client.deployments.ConfigurationMetaNames.HARDWARE_SPEC: {"id": selected_hw_config},
                deployment_client.deployments.ConfigurationMetaNames.SERVING_NAME: deployment_name.value,
            }
        else:  # "Runnable Job" instead of "Batch (Runnable Jobs)"
            deployment_props = {
                deployment_client.deployments.ConfigurationMetaNames.NAME: deployment_name.value,
                deployment_client.deployments.ConfigurationMetaNames.BATCH: {},
                deployment_client.deployments.ConfigurationMetaNames.HARDWARE_SPEC: {"id": selected_hw_config},
                # batch does not use serving names
            }

        try:
            print(deployment_props)
            # First, get the asset details to confirm it exists
            asset_details = deployment_client.repository.get_details(artifact_id)
            print(f"Asset found: {asset_details['metadata']['name']} with ID: {asset_details['metadata']['id']}")

            # Create the deployment
            deployed_function = deployment_client.deployments.create(artifact_id, deployment_props)
            print(f"Creating deployment from Asset: {artifact_id} with deployment properties {str(deployment_props)}")
            return deployed_function
        except Exception as e:
            print(f"Deployment error: {str(e)}")
            return None

    def get_deployment_id(deployed_function):
        deployment_id = deployment_client.deployments.get_uid(deployment_details)
        return deployment_id

    def get_deployment_info(deployment_id):
        deployment_info = deployment_client.deployments.get_details(deployment_id)
        return deployment_info

    deployment_status =  mo.state("No deployments yet")

    if hw_selection_table.value['ID'].iloc[0]:
        selected_hw_config = hw_selection_table.value['ID'].iloc[0]

    deploy_button = mo.ui.button(
        label="Deploy Function",
        on_click=lambda _: deploy_function(artifact_id, deployment_type),
        kind="success",
        tooltip="Click to deploy function to watsonx.ai"
    )

    if deployment_client and upload_button.value:
        deployment_definition = mo.hstack([
            deployment_type,
            deployment_name
        ], justify="space-around")
    else:
        deployment_definition = mo.hstack([
            "No Deployment Type Selected",
            "No Deployment Name Provided"
        ], justify="space-around")

    # deployment_definition
    return (
        deploy_button,
        deploy_function,
        deployment_definition,
        deployment_status,
        get_deployment_id,
        get_deployment_info,
        selected_hw_config,
    )


@app.cell
def _(deploy_button, deployment_definition, mo):
    _ = deployment_definition

    deploy_fnc = mo.vstack([
        deploy_button,
        deploy_button.value
    ], justify="space-around", align="center")

    # mo.md(f"""
    # {deployment_definition}
    # <br>
    # <br>
    # {deploy_fnc}

    # ---
    # """)
    return (deploy_fnc,)


@app.cell(hide_code=True)
def _(deployment_client, mo):
    ### Functions to List , Get ID's as a list and Purge of Assets

    def get_deployment_list():
        deployment_df = deployment_client.deployments.list()
        return deployment_df

    def get_deployment_ids(df):
        dep_list = df['ID'].tolist()
        return dep_list

    def get_data_assets_list():
        data_assets_df = deployment_client.data_assets.list()
        return data_assets_df

    def get_data_asset_ids(df):
        data_asset_list = df['ASSET_ID'].tolist()
        return data_asset_list

    ### List Repository Assets, Get ID's as a list and Purge Repository Assets (AI Services, Functions, Models, etc.)
    def get_repository_list():
        repository_df = deployment_client.repository.list()
        return repository_df

    def get_repository_ids(df):
        repository_list = df['ID'].tolist()
        return repository_list

    def delete_with_progress(ids_list, delete_function, item_type="items"):
        """
        Generic wrapper that adds a progress bar to any deletion function

        Parameters:
            ids_list: List of IDs to delete
            delete_function: Function that deletes a single ID
            item_type: String describing what's being deleted (for display)
        """
        with mo.status.progress_bar(
            total=len(ids_list) or 1,
            title=f"Purging {item_type}",
            subtitle=f"Deleting {item_type}...",
            completion_title="Purge Complete",
            completion_subtitle=f"Successfully deleted {len(ids_list)} {item_type}"
        ) as progress:
            for item_id in ids_list:
                delete_function(item_id)
                progress.update(increment=1)
            return f"Deleted {len(ids_list)} {item_type} successfully"

    # Use with existing deletion functions
    def delete_deployments(deployment_ids):
        return delete_with_progress(
            deployment_ids, 
            lambda id: deployment_client.deployments.delete(id),
            "deployments"
        )

    def delete_data_assets(data_asset_ids):
        return delete_with_progress(
            data_asset_ids, 
            lambda id: deployment_client.data_assets.delete(id),
            "data assets"
        )

    def delete_repository_items(repository_ids):
        return delete_with_progress(
            repository_ids, 
            lambda id: deployment_client.repository.delete(id),
            "repository items"
        )
    return (
        delete_data_assets,
        delete_deployments,
        delete_repository_items,
        delete_with_progress,
        get_data_asset_ids,
        get_data_assets_list,
        get_deployment_ids,
        get_deployment_list,
        get_repository_ids,
        get_repository_list,
    )


@app.cell
def _(get_deployment_id_list, get_deployments_button, mo, purge_deployments):
    deployments_purge_stack = mo.hstack([get_deployments_button, get_deployment_id_list, purge_deployments])
    deployments_purge_stack_results = mo.vstack([get_deployments_button.value, get_deployment_id_list.value, purge_deployments.value])

    deployments_purge_tab = mo.vstack([deployments_purge_stack, deployments_purge_stack_results])
    return (
        deployments_purge_stack,
        deployments_purge_stack_results,
        deployments_purge_tab,
    )


@app.cell
def _(get_repository_button, get_repository_id_list, mo, purge_repository):
    repository_purge_stack = mo.hstack([get_repository_button, get_repository_id_list, purge_repository])

    repository_purge_stack_results = mo.vstack([get_repository_button.value, get_repository_id_list.value, purge_repository.value])

    repository_purge_tab = mo.vstack([repository_purge_stack, repository_purge_stack_results])
    return (
        repository_purge_stack,
        repository_purge_stack_results,
        repository_purge_tab,
    )


@app.cell
def _(get_data_asset_id_list, get_data_assets_button, mo, purge_data_assets):
    data_assets_purge_stack = mo.hstack([get_data_assets_button, get_data_asset_id_list, purge_data_assets])
    data_assets_purge_stack_results = mo.vstack([get_data_assets_button.value, get_data_asset_id_list.value, purge_data_assets.value])

    data_assets_purge_tab = mo.vstack([data_assets_purge_stack, data_assets_purge_stack_results])
    return (
        data_assets_purge_stack,
        data_assets_purge_stack_results,
        data_assets_purge_tab,
    )


@app.cell
def _(data_assets_purge_tab, deployments_purge_tab, mo, repository_purge_tab):
    purge_tabs = mo.ui.tabs(
        {"Purge Deployments": deployments_purge_tab, "Purge Repository Assets": repository_purge_tab,"Purge Data Assets": data_assets_purge_tab }, lazy=False
    )

    # asset_purge = mo.accordion(
    #     {
    #         """<br>
    # #### **Supporting Cleanup Functionality, lists of different assets and purge them if needed** *(purges all detected)*
    # <br>""": purge_tabs,
    #     }
    # )

    # asset_purge
    return (purge_tabs,)


@app.cell(hide_code=True)
def _(
    delete_data_assets,
    delete_deployments,
    delete_repository_items,
    get_data_asset_ids,
    get_data_assets_list,
    get_deployment_ids,
    get_deployment_list,
    get_repository_ids,
    get_repository_list,
    mo,
):
    ### Temporary Function Purge - Assets
    get_data_assets_button = mo.ui.button(
        label="Get Data Assets Dataframe",
        on_click=lambda _: get_data_assets_list(),
        kind="neutral",
    )

    get_data_asset_id_list = mo.ui.button(
        label="Turn Dataframe into List of IDs",
        on_click=lambda _: get_data_asset_ids(get_data_assets_button.value),
        kind="neutral",
    )

    purge_data_assets = mo.ui.button(
        label="Purge Data Assets",
        on_click=lambda _: delete_data_assets(get_data_asset_id_list.value),
        kind="danger",
    )

    ### Temporary Function Purge - Deployments
    get_deployments_button = mo.ui.button(
        label="Get Deployments Dataframe",
        on_click=lambda _: get_deployment_list(),
        kind="neutral",
    )

    get_deployment_id_list = mo.ui.button(
        label="Turn Dataframe into List of IDs",
        on_click=lambda _: get_deployment_ids(get_deployments_button.value),
        kind="neutral",
    )

    purge_deployments = mo.ui.button(
        label="Purge Deployments",
        on_click=lambda _: delete_deployments(get_deployment_id_list.value),
        kind="danger",
    )

    ### Repository Items Purge
    get_repository_button = mo.ui.button(
        label="Get Repository Dataframe",
        on_click=lambda _: get_repository_list(),
        kind="neutral",
    )

    get_repository_id_list = mo.ui.button(
        label="Turn Dataframe into List of IDs",
        on_click=lambda _: get_repository_ids(get_repository_button.value),
        kind="neutral",
    )

    purge_repository = mo.ui.button(
        label="Purge Repository Items",
        on_click=lambda _: delete_repository_items(get_repository_id_list.value),
        kind="danger",
    )
    return (
        get_data_asset_id_list,
        get_data_assets_button,
        get_deployment_id_list,
        get_deployments_button,
        get_repository_button,
        get_repository_id_list,
        purge_data_assets,
        purge_deployments,
        purge_repository,
    )


if __name__ == "__main__":
    app.run()