from warnings import filterwarnings
filterwarnings('ignore')

import os
import uuid
import json
import gradio as gr
import pandas as pd
from huggingface_hub import CommitScheduler
from pathlib import Path

# Configure the logging functionality
log_file = Path("logs/") / f"data_{uuid.uuid4()}.json"
log_folder = log_file.parent

repo_id = "operand-logs"

# Create a commit scheduler
scheduler = CommitScheduler(
    repo_id=repo_id,
    repo_type="dataset",
    folder_path=log_folder,
    path_in_repo="data",
    every=2
)

def process_command(command, ddddd):
    print('foo...')
    with scheduler.lock:
        with log_file.open("a") as f:
            f.write(json.dumps(
                {
                    'p1': 'foo',
                    'p2': 100
                }
            ))
            f.write("\n")
    return 42

# Set-up the Gradio UI
#textbox = gr.Textbox(label='Command')
# company = gr.Radio(label='Company:',
#                    choices=["aws", "google", "IBM", "Meta", "msft"],
#                    value="aws")

# Create Gradio interface with tabs
with gr.Blocks(theme=gr.themes.Soft()) as operand:
    
    gr.Markdown("# operand")
    gr.Markdown("Data Studio<br><br>")
    gr.Image("img.png", label="Sample Image")
    
    with gr.Tab("Source"):
        
        gr.Markdown("## Source")
        
        with gr.Accordion("Syntax"):
            gr.Markdown("<br>data_source my-ds-name1 my-ds-desc1 my-jira-endpoint1 my-jira-creds1")

        gr.Markdown("Instances of data sources e.g., Jira Cloud endpoint, Trello endpoint, Github endpoint")

        textbox_a = gr.Textbox(label='Command')
        output_a = gr.Textbox(label='Output')
        button_a = gr.Button("Submit")
        button_a.click(process_command, inputs=[textbox_a], outputs=output_a)
    
    with gr.Tab("Set"):
        gr.Markdown("## Data Set")
        gr.Markdown("A data set from a data source.")
        textbox_b = gr.Textbox(label='Command')
        output_b = gr.Textbox(label='Output')
        button_b = gr.Button("Submit")
        button_b.click(process_command, inputs=[textbox_b], outputs=output_b)
    
    with gr.Tab("Transform"):
        gr.Markdown("## Data Transform")
        gr.Markdown("A transformation of a data set into a new data set.")
        textbox_c = gr.Textbox(label='Command')
        output_c = gr.Textbox(label='Output')
        button_c = gr.Button("Submit")
        button_c.click(process_command, inputs=[textbox_c], outputs=output_c)

    with gr.Tab("Analysis"):
        gr.Markdown("## Data Analysis")
        gr.Markdown("Statistical analysis of a data set e.g., slope calculation on feature")
        textbox_d = gr.Textbox(label='Command')
        output_d = gr.Textbox(label='Output')
        button_d = gr.Button("Submit")
        button_d.click(process_command, inputs=[textbox_d], outputs=output_d)

    with gr.Tab("Visualization"):
        gr.Markdown("## Data Visualization")
        gr.Markdown("A visual insight from a data set or data analysis results e.g., matplotlib, sns, plotly")
        textbox_e = gr.Textbox(label='Command')
        output_e = gr.Textbox(label='Output')
        button_e = gr.Button("Submit")
        button_e.click(process_command, inputs=[textbox_e], outputs=output_e)

    with gr.Tab("Notification"):
        gr.Markdown("## Notifications")
        gr.Markdown("Scheduled transmission of data set, data analysis or data visualization direct to user device")
        textbox_f = gr.Textbox(label='Command')
        output_f = gr.Textbox(label='Output')
        button_f = gr.Button("Submit")
        button_f.click(process_command, inputs=[textbox_f], outputs=output_f)

    with gr.Tab("Automation"):
        gr.Markdown("## Automation")
        gr.Markdown("Multistep composition of functional elements")
        textbox_g = gr.Textbox(label='Command')
        output_g = gr.Textbox(label='Output')
        button_g = gr.Button("Submit")
        button_g.click(process_command, inputs=[textbox_g], outputs=output_g)
        
# For the inputs parameter of Interface provide [textbox,company] with outputs parameter of Interface provide prediction
# demo = gr.Interface(fn=dprocess,
#                     inputs=[textbox],
#                     outputs="text",
#                     title="operand",
#                     description="Data Workbench CLI",
#                     theme=gr.themes.Soft())

operand.queue()
operand.launch()