Spaces:
Running
Running
File size: 13,359 Bytes
f21a673 10f3d6f f21a673 f842376 f21a673 f842376 f21a673 282a0cf 7df1b03 4364c1e 7df1b03 4364c1e 7df1b03 f21a673 7df1b03 f21a673 7df1b03 f21a673 7df1b03 f21a673 7df1b03 f21a673 7df1b03 f21a673 7df1b03 f21a673 7df1b03 f21a673 7df1b03 f21a673 7df1b03 f21a673 7df1b03 f21a673 7df1b03 f21a673 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 |
import gradio as gr
from transformers import DebertaTokenizer, DebertaForSequenceClassification
from transformers import pipeline
import json
save_path_abstract = './fine-tuned-deberta'
model_abstract = DebertaForSequenceClassification.from_pretrained(save_path_abstract)
tokenizer_abstract = DebertaTokenizer.from_pretrained(save_path_abstract)
classifier_abstract = pipeline('text-classification', model=model_abstract, tokenizer=tokenizer_abstract)
save_path_essay = './fine-tuned-deberta'
model_essay = DebertaForSequenceClassification.from_pretrained(save_path_essay)
tokenizer_essay = DebertaTokenizer.from_pretrained(save_path_essay)
classifier_essay = pipeline('text-classification', model=model_essay, tokenizer=tokenizer_essay)
demo_essays = json.load(open('samples.json'))
index = None
################# HELPER FUNCTIONS (DETECTION TAB) ####################
def process_result_detection_tab(text):
'''
Classify the text into one of the four categories by averaging the soft predictions of the two models.
Args:
text: str: the text to be classified
Returns:
dict: a dictionary with the following keys:
'Machine Generated': float: the probability that the text is machine generated
'Human Written': float: the probability that the text is human written
'Machine Written, Machine Humanized': float: the probability that the text is machine written and machine humanized
'Human Written, Machine Polished': float: the probability that the text is human written and machine polished
'''
mapping = {'llm': 'Machine Generated', 'human':'Human Written', 'machine-humanized': 'Machine Written, Machine Humanized', 'machine-polished': 'Human Written, Machine Polished'}
# Initialize scores for all classes
final_results = {label: 0.0 for label in mapping.values()}
# Add scores from classifier_abstract
if result['label'] in mapping:
final_results[mapping[result['label']]] += 0.5 * result['score']
# Add scores from classifier_essay
if result_r['label'] in mapping:
final_results[mapping[result_r['label']]] += 0.5 * result_r['score']
print(final_results)
return final_results
def update_detection_tab(name, uploaded_file, radio_input):
'''
Callback function to update the result of the classification based on the input text or uploaded file.
Args:
name: str: the input text from the Textbox
uploaded_file: file: the uploaded file from the file input
Returns:
dict: the result of the classification including labels and scores
'''
if name == '' and uploaded_file is None:
return ""
if uploaded_file is not None:
return f"Work in progress"
else:
return process_result_detection_tab(name)
def active_button_detection_tab(input_text, file_input):
'''
Callback function to activate the 'Check Origin' button when the input text or file input
is not empty. For text input, the button can be clickde only when the word count is between
50 and 500.
Args:
input_text: str: the input text from the textbox
file_input: file: the uploaded file from the file input
Returns:
gr.Button: The 'Check Origin' button with the appropriate interactivity.
'''
if (input_text == "" and file_input is None) or (file_input is None and not (50 <= len(input_text.split()) <= 500)):
return gr.Button("Check Origin", variant="primary", interactive=False)
return gr.Button("Check Origin", variant="primary", interactive=True)
def clear_detection_tab():
'''
Callback function to clear the input text and file input in the 'Try it!' tab.
The interactivity of the 'Check Origin' button is set to False to prevent user click when the Textbox is empty.
Args:
None
Returns:
str: An empty string to clear the Textbox.
None: None to clear the file input.
gr.Button: The 'Check Origin' button with no interactivity.
'''
return "", None, gr.Button("Check Origin", variant="primary", interactive=False)
def count_words_detection_tab(text):
'''
Callback function called when the input text is changed to update the word count.
Args:
text: str: the input text from the Textbox
Returns:
str: the word count of the input text for the Markdown widget
'''
return (f'{len(text.split())}/500 words (Minimum 50 words)')
################# HELPER FUNCTIONS (CHALLENGE TAB) ####################
def clear_challenge_tab():
'''
Callback function to clear the text and result in the 'Challenge Yourself' tab.
The interactivity of the buttons is set to False to prevent user click when the Textbox is empty.
Args:
None
Returns:
gr.Button: The 'Machine-Generated' button with no interactivity.
gr.Button: The 'Human-Written' button with no interactivity.
gr.Button: The 'Machine-Humanized' button with no interactivity.
gr.Button: The 'Machine-Polished' button with no interactivity.
str: An empty string to clear the Textbox.
'''
mg = gr.Button("Machine-Generated", variant="secondary", interactive=False)
hw = gr.Button("Human-Written", variant="secondary", interactive=False)
mh = gr.Button("Machine-Humanized", variant="secondary", interactive=False)
mp = gr.Button("Machine-Polished", variant="secondary", interactive=False)
return mg, hw, mh, mp, ''
def generate_text_challenge_tab():
'''
Callback function to randomly sample an essay from the dataset and set the interactivity of the buttons to True.
Args:
None
Returns:
str: A sample text from the dataset
gr.Button: The 'Machine-Generated' button with interactivity.
gr.Button: The 'Human-Written' button with interactivity.
gr.Button: The 'Machine-Humanized' button with interactivity.
gr.Button: The 'Machine-Polished' button with interactivity.
str: An empty string to clear the Result.
'''
global index # to access the index of the sample text for the show_result function
mg = gr.Button("Machine-Generated", variant="secondary", interactive=True)
hw = gr.Button("Human-Written", variant="secondary", interactive=True)
mh = gr.Button("Machine-Humanized", variant="secondary", interactive=True)
mp = gr.Button("Machine-Polished", variant="secondary", interactive=True)
index = random.choice(range(80))
essay = demo_essays[index][0]
return essay, mg, hw, mh, mp, ''
def correct_label_challenge_tab():
'''
Function to return the correct label of the sample text based on the index (global variable).
Args:
None
Returns:
str: The correct label of the sample text
'''
if 0 <= index < 20 :
return 'Human-Written'
elif 20 <= index < 40:
return 'Machine-Generated'
elif 40 <= index < 60:
return 'Machine-Polished'
elif 60 <= index < 80:
return 'Machine-Humanized'
def show_result_challenge_tab(button):
'''
Callback function to show the result of the classification based on the button clicked by the user.
The correct label of the sample text is displayed in the primary variant.
The chosen label by the user is displayed in the stop variant if it is incorrect.
Args:
button: str: the label of the button clicked by the user
Returns:
str: the outcome of the classification
gr.Button: The 'Machine-Generated' button with the appropriate variant.
gr.Button: The 'Human-Written' button with the appropriate variant.
gr.Button: The 'Machine-Humanized' button with the appropriate variant.
gr.Button: The 'Machine-Polished' button with the appropriate variant.
'''
correct_btn = correct_label_challenge_tab()
mg = gr.Button("Machine-Generated", variant="secondary")
hw = gr.Button("Human-Written", variant="secondary")
mh = gr.Button("Machine-Humanized", variant="secondary")
mp = gr.Button("Machine-Polished", variant="secondary")
if button == 'Machine-Generated':
mg = gr.Button("Machine-Generated", variant="stop")
elif button == 'Human-Written':
hw = gr.Button("Human-Written", variant="stop")
elif button == 'Machine-Humanized':
mh = gr.Button("Machine-Humanized", variant="stop")
elif button == 'Machine-Polished':
mp = gr.Button("Machine-Polished", variant="stop")
if correct_btn == 'Machine-Generated':
mg = gr.Button("Machine-Generated", variant="primary")
elif correct_btn == 'Human-Written':
hw = gr.Button("Human-Written", variant="primary")
elif correct_btn == 'Machine-Humanized':
mh = gr.Button("Machine-Humanized", variant="primary")
elif correct_btn == 'Machine-Polished':
mp = gr.Button("Machine-Polished", variant="primary")
outcome = ''
if button == correct_btn:
outcome = 'Correct'
else:
outcome = 'Incorrect'
return outcome, mg, hw, mh, mp
############################## GRADIO UI ##############################
with gr.Blocks() as demo:
gr.Markdown("""<h1><centre>Machine Generated Text (MGT) Detection</center></h1>""")
with gr.Tab('Try it!'):
with gr.Row():
radio_button = gr.Dropdown(['Student Essay', 'Scientific Abstract'], label = 'Text Type', info = 'We have specialized models that work on domain-specific text.', value='Student Essay')
with gr.Row():
input_text = gr.Textbox(placeholder="Paste your text here...", label="Text", lines=10, max_lines=15)
file_input = gr.File(label="Upload File", file_types=[".txt", ".pdf"])
with gr.Row():
wc = gr.Markdown("0/500 words (Minimum 50 words)")
with gr.Row():
check_button = gr.Button("Check Origin", variant="primary", interactive=False)
clear_button = gr.ClearButton([input_text, file_input], variant="stop")
out = gr.Label(label='Result')
clear_button.add(out)
check_button.click(fn=update_detection_tab, inputs=[input_text, file_input, radio_button], outputs=out)
input_text.change(count_words_detection_tab, input_text, wc, show_progress=False)
input_text.input(
active_button_detection_tab,
[input_text, file_input],
[check_button],
)
file_input.upload(
active_button_detection_tab,
[input_text, file_input],
[check_button],
)
clear_button.click(
clear_detection_tab,
inputs=[],
outputs=[input_text, file_input, check_button],
)
# Adding JavaScript to simulate file input click
gr.Markdown(
"""
<script>
document.addEventListener("DOMContentLoaded", function() {
const uploadButton = Array.from(document.getElementsByTagName('button')).find(el => el.innerText === "Upload File");
if (uploadButton) {
uploadButton.onclick = function() {
document.querySelector('input[type="file"]').click();
};
}
});
</script>
"""
)
with gr.Tab('Challenge Yourself!'):
gr.Markdown(
"""
<style>
.gr-button-secondary {
width: 100px;
height: 30px;
padding: 5px;
}
.gr-row {
display: flex;
align-items: center;
gap: 10px;
}
.gr-block {
padding: 20px;
}
.gr-markdown p {
font-size: 16px;
}
</style>
<span style='font-family: Arial, sans-serif; font-size: 20px;'>Was this text written by <strong>human</strong> or <strong>AI</strong>?</span>
<p style='font-family: Arial, sans-serif;'>Try detecting one of our sample texts:</p>
"""
)
with gr.Row():
generate = gr.Button("Generate Sample Text", variant="primary")
clear = gr.ClearButton([], variant="stop")
with gr.Row():
text = gr.Textbox(value="", label="Text", lines=20, interactive=False)
with gr.Row():
mg = gr.Button("Machine-Generated", variant="secondary", interactive=False)
hw = gr.Button("Human-Written", variant="secondary", interactive=False)
mh = gr.Button("Machine-Humanized", variant="secondary", interactive=False)
mp = gr.Button("Machine-Polished", variant="secondary", interactive=False)
with gr.Row():
result = gr.Label(label="Result", value="")
clear.add([result, text])
generate.click(generate_text_challenge_tab, [], [text, mg, hw, mh, mp, result])
for button in [mg, hw, mh, mp]:
button.click(show_result_challenge_tab, [button], [result, mg, hw, mh, mp])
clear.click(clear_challenge_tab, [], [mg, hw, mh, mp, result])
demo.launch(share=False)
|