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import gradio as gr
import pandas as pd
import numpy as np
# Global variable to store the DataFrame
df = None
# Global variable to keep track of the current row index
current_row = 0
def load_csv(file):
global df
global current_row
# import the csv and set the data types to be int, string, string, string, string, string, string
df = pd.read_csv(file.name, dtype={'id':int, 'hs': str, 'cs': str, 'topic': str, 'tone': str, 'isCSContextuallyRelevant': str, 'isToneMatch': str})
if 'suggestedTone' not in df.columns:
df['suggestedTone'] = None
current_row = 0
row_dict = df.iloc[current_row].to_dict()
return row_dict['id'], row_dict['hs'], row_dict['cs'], row_dict['topic'], row_dict['tone'], row_dict['isCSContextuallyRelevant'], row_dict['isToneMatch'], row_dict['suggestedTone']
def annotate_row(isCSContextuallyRelevant, isToneMatch, suggestedTone):
global df
global current_row
df.at[current_row, 'isCSContextuallyRelevant'] = isCSContextuallyRelevant
df.at[current_row, 'isToneMatch'] = isToneMatch
df.at[current_row, 'suggestedTone'] = suggestedTone
if current_row < len(df) - 1:
current_row += 1
else:
current_row = 0
df.to_csv('annotated_data.csv', index=False)
row_dict = df.iloc[current_row].to_dict()
return row_dict['id'], row_dict['hs'], row_dict['cs'], row_dict['topic'], row_dict['tone'], row_dict['isCSContextuallyRelevant'], row_dict['isToneMatch'], row_dict['suggestedTone'], 'annotated_data.csv'
def navigate(direction):
global current_row
if direction == "Previous":
current_row = max(0, current_row - 1)
elif direction == "Next":
current_row = min(len(df) - 1, current_row + 1)
elif direction == "First Unlabeled":
unlabeled_row = df[df['isCSContextuallyRelevant'].isna()].index.min()
if not np.isnan(unlabeled_row):
current_row = int(unlabeled_row)
row_dict = df.iloc[current_row].to_dict()
return row_dict['id'], row_dict['hs'], row_dict['cs'], row_dict['topic'], row_dict['tone'], row_dict['isCSContextuallyRelevant'], row_dict['isToneMatch'], row_dict['suggestedTone']
with gr.Blocks(theme=gr.themes.Soft()) as annotator:
gr.Markdown("## Data Annotation")
with gr.Row():
gr.Markdown("### Upload CSV")
file_upload = gr.File()
btn_load = gr.Button("Load CSV")
with gr.Row():
gr.Markdown("### Current Row")
with gr.Row():
idx = gr.Number(label='Index')
hs = gr.Textbox(label='HS')
cs = gr.Textbox(label='CS')
with gr.Row():
topic = gr.Textbox(label='Topic')
tone = gr.Textbox(label='Tone')
with gr.Row():
isCSContextuallyRelevant = gr.Radio(["1", "0"], label="Contextually Relevant?")
isToneMatch = gr.Radio(["1", "0"], label="Tone Match?")
suggestedTone = gr.Dropdown(['', 'empathy', 'refutal', 'first_person', 'warn_of_conseq', 'humor', 'other'], label='Suggested Tone', interactive=True)
btn_annotate = gr.Button("Annotate")
with gr.Row():
btn_previous = gr.Button("Previous")
btn_next = gr.Button("Next")
btn_first_unlabeled = gr.Button("First Unlabeled")
with gr.Row():
gr.Markdown("### Annotated Data File Download")
file_download = gr.File()
btn_load.click(load_csv, inputs=[file_upload], outputs=[idx, hs, cs, topic, tone, isCSContextuallyRelevant, isToneMatch, suggestedTone])
btn_annotate.click(annotate_row, inputs=[isCSContextuallyRelevant, isToneMatch, suggestedTone], outputs=[idx, hs, cs, topic, tone, isCSContextuallyRelevant, isToneMatch, suggestedTone, file_download])
btn_previous.click(navigate, inputs=gr.Textbox("Previous", visible=False), outputs=[idx, hs, cs, topic, tone, isCSContextuallyRelevant, isToneMatch, suggestedTone])
btn_next.click(navigate, inputs=gr.Textbox("Next", visible=False), outputs=[idx, hs, cs, topic, tone, isCSContextuallyRelevant, isToneMatch, suggestedTone])
btn_first_unlabeled.click(navigate, inputs=gr.Textbox("First Unlabeled", visible=False), outputs=[idx, hs, cs, topic, tone, isCSContextuallyRelevant, isToneMatch, suggestedTone])
annotator.launch() |