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("No-code Data Automation Studio<br><br>")
    
#     with gr.Tab("Source"):
        
#         gr.Markdown("## Data Sources")
#         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.Accordion("Syntax"):
#             gr.Markdown("<br>data_source my-ds-name1 my-ds-desc1 my-jira-endpoint1 my-jira-creds1")
    
#     with gr.Tab("Set"):
#         gr.Markdown("## Data Sets")
#         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 Transforms")
#         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 Analyses")
#         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 Visualizations")
#         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
operand = gr.Interface(fn=process_command,
                    inputs=[textbox],
                    outputs="text",
                    title="operand",
                    description="Data Workbench CLI",
                    theme=gr.themes.Soft())

operand.queue()
operand.launch()