File size: 4,336 Bytes
e75a48e 7ba7074 d52d7a5 7ba7074 dfa8bc0 83eb289 7ba7074 dfa8bc0 7ba7074 dfa8bc0 f961b92 7ba7074 48ad9ce 998ddcc d52d7a5 7ba7074 ee053e6 515fbbc ee053e6 2ceea22 18945cc 6f14589 4f46b1a 2ceea22 7af5cb9 6f14589 ece39d2 7af5cb9 ece39d2 2ceea22 7af5cb9 ece39d2 7af5cb9 ece39d2 2ceea22 7af5cb9 ece39d2 7af5cb9 ece39d2 2ceea22 7af5cb9 b7f426c 7af5cb9 2ceea22 7af5cb9 2ceea22 b7f426c 2ceea22 7af5cb9 2ceea22 b7f426c 2ceea22 7af5cb9 2ceea22 b7f426c 2ceea22 7ba7074 ece39d2 d52d7a5 477fa16 ee053e6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 |
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 dprocess(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
# Set-up the Gradio UI
# Create Gradio interface with tabs
with gr.Blocks(theme=gr.themes.Soft()) as operand:
gr.Markdown("# operand")
gr.Markdown("Data Studio<br><br>")
with gr.Tab("Data Source"):
gr.Markdown("## Data Sources")
gr.Markdown("Instances of data sources e.g., Jira Cloud endpoint")
with gr.Accordion("See Details"):
gr.Markdown("lorem ipsum")
textbox_a = gr.Textbox(label='Command A')
output_a = gr.Textbox(label='Output A')
button_a = gr.Button("Submit")
button_a.click(dprocess, inputs=[textbox_a], outputs=output_a)
with gr.Tab("Data Set"):
gr.Markdown("## Data Set")
gr.Markdown("A data set from a data source.")
textbox_b = gr.Textbox(label='Command B')
output_b = gr.Textbox(label='Output B')
button_b = gr.Button("Submit")
button_b.click(dprocess, inputs=[textbox_b], outputs=output_b)
with gr.Tab("Data Transform"):
gr.Markdown("## Data Transform")
gr.Markdown("A transformation of a data set into a new data set.")
textbox_c = gr.Textbox(label='Command C')
output_c = gr.Textbox(label='Output C')
button_c = gr.Button("Submit")
button_c.click(dprocess, inputs=[textbox_c], outputs=output_c)
with gr.Tab("Data 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 C')
output_d = gr.Textbox(label='Output C')
button_d = gr.Button("Submit")
button_d.click(dprocess, inputs=[textbox_d], outputs=output_d)
with gr.Tab("Data Visualization"):
gr.Markdown("## Data Visualization")
gr.Markdown("A visual insight from a data set or data analysis results e.g., matplotlib, sns, plotly")
textbox_c = gr.Textbox(label='Command C')
output_c = gr.Textbox(label='Output C')
button_c = gr.Button("Submit")
button_c.click(dprocess, inputs=[textbox_c], outputs=output_c)
with gr.Tab("Notification"):
gr.Markdown("## Notifications")
gr.Markdown("Scheduled transmission of data set, data analysis or data visualization direct to user device")
textbox_c = gr.Textbox(label='Command C')
output_c = gr.Textbox(label='Output C')
button_c = gr.Button("Submit")
button_c.click(dprocess, inputs=[textbox_c], outputs=output_c)
with gr.Tab("Automation"):
gr.Markdown("## Automation")
gr.Markdown("Multistep composition of functional elements")
textbox_c = gr.Textbox(label='Command C')
output_c = gr.Textbox(label='Output C')
button_c = gr.Button("Submit")
button_c.click(dprocess, inputs=[textbox_c], outputs=output_c)
# 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() |