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""" |
|
|
simple_demo.py |
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|
""" |
|
|
|
|
|
import os |
|
|
import gradio as gr |
|
|
from safetensors.torch import load_file |
|
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from huggingface_hub import hf_hub_download |
|
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import gspread |
|
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from google.oauth2 import service_account |
|
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import json |
|
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from datetime import datetime |
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import uuid |
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|
|
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|
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from lionguard2 import LionGuard2, CATEGORIES |
|
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from utils import get_embeddings |
|
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|
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|
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GOOGLE_SHEET_URL = os.environ.get("GOOGLE_SHEET_URL") |
|
|
GOOGLE_CREDENTIALS = os.environ.get("GCP_SERVICE_ACCOUNT") |
|
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RESULTS_SHEET_NAME = "results" |
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VOTES_SHEET_NAME = "votes" |
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|
|
|
|
|
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def save_results_data(row): |
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try: |
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|
|
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|
credentials = service_account.Credentials.from_service_account_info( |
|
|
json.loads(GOOGLE_CREDENTIALS), |
|
|
scopes=[ |
|
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"https://www.googleapis.com/auth/spreadsheets", |
|
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"https://www.googleapis.com/auth/drive", |
|
|
], |
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) |
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gc = gspread.authorize(credentials) |
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sheet = gc.open_by_url(GOOGLE_SHEET_URL) |
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ws = sheet.worksheet(RESULTS_SHEET_NAME) |
|
|
ws.append_row(list(row.values())) |
|
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print(f"Saved results data for text_id: {row['text_id']}") |
|
|
except Exception as e: |
|
|
print(f"Error saving results data: {e}") |
|
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|
|
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|
|
|
def save_vote_data(text_id, agree): |
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|
try: |
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|
|
|
credentials = service_account.Credentials.from_service_account_info( |
|
|
json.loads(GOOGLE_CREDENTIALS), |
|
|
scopes=[ |
|
|
"https://www.googleapis.com/auth/spreadsheets", |
|
|
"https://www.googleapis.com/auth/drive", |
|
|
], |
|
|
) |
|
|
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|
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gc = gspread.authorize(credentials) |
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sheet = gc.open_by_url(GOOGLE_SHEET_URL) |
|
|
ws = sheet.worksheet(VOTES_SHEET_NAME) |
|
|
|
|
|
vote_row = { |
|
|
"datetime": datetime.now().isoformat(), |
|
|
"text_id": text_id, |
|
|
"agree": agree |
|
|
} |
|
|
ws.append_row(list(vote_row.values())) |
|
|
print(f"Saved vote data for text_id: {text_id}, agree: {agree}") |
|
|
except Exception as e: |
|
|
print(f"Error saving vote data: {e}") |
|
|
|
|
|
|
|
|
def download_model(repo_id, filename="LionGuard2.safetensors", token=None): |
|
|
""" |
|
|
Download the LionGuard2 model from a Hugging Face private repository. |
|
|
|
|
|
Args: |
|
|
repo_id: The Hugging Face repository ID (e.g., "username/repo-name") |
|
|
filename: The filename to download (default: "LionGuard2.safetensors") |
|
|
token: Hugging Face access token for private repositories |
|
|
|
|
|
Returns: |
|
|
Path to the downloaded file |
|
|
""" |
|
|
if token is None: |
|
|
token = os.environ.get("HF_API_KEY") |
|
|
|
|
|
|
|
|
model_path = hf_hub_download( |
|
|
repo_id=repo_id, |
|
|
filename=filename, |
|
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token=token, |
|
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cache_dir="./cache" |
|
|
) |
|
|
return model_path |
|
|
|
|
|
|
|
|
def load_model(repo_id=None, use_local=True): |
|
|
""" |
|
|
Load the LionGuard2 model from either local file or Hugging Face repository. |
|
|
|
|
|
Args: |
|
|
repo_id: The Hugging Face repository ID (optional) |
|
|
use_local: Whether to use local file first (default: True) |
|
|
""" |
|
|
model = LionGuard2() |
|
|
model.eval() |
|
|
|
|
|
model_path = "LionGuard2.safetensors" |
|
|
|
|
|
|
|
|
if repo_id and (not use_local or not os.path.exists(model_path)): |
|
|
try: |
|
|
print(f"Downloading LionGuard2.safetensors from {repo_id}...") |
|
|
model_path = download_model(repo_id) |
|
|
print(f"Model downloaded to: {model_path}") |
|
|
except Exception as e: |
|
|
print(f"Failed to download from HF repo: {e}") |
|
|
if not os.path.exists("LionGuard2.safetensors"): |
|
|
raise Exception("No local model file found and failed to download from HF repo") |
|
|
print("Falling back to local file...") |
|
|
|
|
|
state_dict = load_file(model_path) |
|
|
model.load_state_dict(state_dict) |
|
|
return model |
|
|
|
|
|
|
|
|
|
|
|
HF_REPO_ID = "gabrielchua/refactored-guacamole" |
|
|
model = load_model(repo_id=HF_REPO_ID) |
|
|
|
|
|
|
|
|
def format_score_with_style(score_str, compact=False): |
|
|
""" |
|
|
Format score with color and emoji based on value. |
|
|
|
|
|
Args: |
|
|
score_str: Score as string (could be "-" for missing values) |
|
|
compact: Whether to use compact styling |
|
|
|
|
|
Returns: |
|
|
HTML formatted string with styling |
|
|
""" |
|
|
if score_str == "-": |
|
|
return '<span style="color: #9ca3af;">-</span>' |
|
|
|
|
|
try: |
|
|
score = float(score_str) |
|
|
percentage = int(score * 100) |
|
|
padding = "6px 12px" if compact else "8px 16px" |
|
|
|
|
|
if score < 0.4: |
|
|
|
|
|
return f'<span style="background: linear-gradient(135deg, #065f46 0%, #047857 100%); color: #34d399; padding: {padding}; border-radius: 8px; font-size: 13px; font-weight: 600; border: 1px solid #10b981; box-shadow: 0 2px 4px rgba(0,0,0,0.2);">👌 {percentage}%</span>' |
|
|
elif 0.4 <= score < 0.7: |
|
|
|
|
|
return f'<span style="background: linear-gradient(135deg, #92400e 0%, #b45309 100%); color: #fbbf24; padding: {padding}; border-radius: 8px; font-size: 13px; font-weight: 600; border: 1px solid #f59e0b; box-shadow: 0 2px 4px rgba(0,0,0,0.2);">⚠️ {percentage}%</span>' |
|
|
else: |
|
|
|
|
|
return f'<span style="background: linear-gradient(135deg, #991b1b 0%, #b91c1c 100%); color: #fca5a5; padding: {padding}; border-radius: 8px; font-size: 13px; font-weight: 600; border: 1px solid #ef4444; box-shadow: 0 2px 4px rgba(0,0,0,0.2);">🚨 {percentage}%</span>' |
|
|
except: |
|
|
return score_str |
|
|
|
|
|
|
|
|
def format_binary_score(score): |
|
|
"""Format the binary score with appropriate styling for dark mode.""" |
|
|
percentage = int(score * 100) |
|
|
|
|
|
if score < 0.4: |
|
|
return f'<div style="background: linear-gradient(135deg, #065f46 0%, #047857 100%); color: #34d399; padding: 24px; border-radius: 12px; text-align: center; font-weight: 700; border: 2px solid #10b981; font-size: 20px; box-shadow: 0 4px 12px rgba(0,0,0,0.3); margin: 16px 0;">✅ Pass ({percentage}/100)</div>' |
|
|
elif 0.4 <= score < 0.7: |
|
|
return f'<div style="background: linear-gradient(135deg, #92400e 0%, #b45309 100%); color: #fbbf24; padding: 24px; border-radius: 12px; text-align: center; font-weight: 700; border: 2px solid #f59e0b; font-size: 20px; box-shadow: 0 4px 12px rgba(0,0,0,0.3); margin: 16px 0;">⚠️ Warning ({percentage}/100)</div>' |
|
|
else: |
|
|
return f'<div style="background: linear-gradient(135deg, #991b1b 0%, #b91c1c 100%); color: #fca5a5; padding: 24px; border-radius: 12px; text-align: center; font-weight: 700; border: 2px solid #ef4444; font-size: 20px; box-shadow: 0 4px 12px rgba(0,0,0,0.3); margin: 16px 0;">🚨 Fail ({percentage}/100)</div>' |
|
|
|
|
|
|
|
|
def analyze_text(text): |
|
|
""" |
|
|
Analyze text for content moderation violations. |
|
|
|
|
|
Args: |
|
|
text: Input text to analyze |
|
|
|
|
|
Returns: |
|
|
binary_score: Overall safety score with styling |
|
|
category_table: HTML table with category-specific scores and styling |
|
|
text_id: Unique identifier for this analysis |
|
|
voting_section: HTML for voting buttons |
|
|
""" |
|
|
if not text.strip(): |
|
|
empty_html = '<div style="text-align: center; color: #9ca3af; padding: 30px; font-style: italic;">Enter text to analyze</div>' |
|
|
return '<div style="text-align: center; color: #9ca3af; padding: 30px; font-style: italic;">Enter text to analyze</div>', empty_html, "", "" |
|
|
|
|
|
try: |
|
|
|
|
|
text_id = str(uuid.uuid4()) |
|
|
|
|
|
|
|
|
embeddings = get_embeddings([text]) |
|
|
|
|
|
|
|
|
results = model.predict(embeddings) |
|
|
|
|
|
|
|
|
binary_score = results.get('binary', [0.0])[0] |
|
|
|
|
|
|
|
|
hateful_scores = CATEGORIES['hateful'] |
|
|
hateful_l1_score = results.get(hateful_scores[0], [0.0])[0] if len(hateful_scores) > 0 else 0.0 |
|
|
hateful_l2_score = results.get(hateful_scores[1], [0.0])[0] if len(hateful_scores) > 1 else 0.0 |
|
|
|
|
|
insults_scores = CATEGORIES['insults'] |
|
|
insults_score = results.get(insults_scores[0], [0.0])[0] if len(insults_scores) > 0 else 0.0 |
|
|
|
|
|
sexual_scores = CATEGORIES['sexual'] |
|
|
sexual_l1_score = results.get(sexual_scores[0], [0.0])[0] if len(sexual_scores) > 0 else 0.0 |
|
|
sexual_l2_score = results.get(sexual_scores[1], [0.0])[0] if len(sexual_scores) > 1 else 0.0 |
|
|
|
|
|
physical_violence_scores = CATEGORIES['physical_violence'] |
|
|
physical_violence_score = results.get(physical_violence_scores[0], [0.0])[0] if len(physical_violence_scores) > 0 else 0.0 |
|
|
|
|
|
self_harm_scores = CATEGORIES['self_harm'] |
|
|
self_harm_l1_score = results.get(self_harm_scores[0], [0.0])[0] if len(self_harm_scores) > 0 else 0.0 |
|
|
self_harm_l2_score = results.get(self_harm_scores[1], [0.0])[0] if len(self_harm_scores) > 1 else 0.0 |
|
|
|
|
|
aom_scores = CATEGORIES['all_other_misconduct'] |
|
|
aom_l1_score = results.get(aom_scores[0], [0.0])[0] if len(aom_scores) > 0 else 0.0 |
|
|
aom_l2_score = results.get(aom_scores[1], [0.0])[0] if len(aom_scores) > 1 else 0.0 |
|
|
|
|
|
|
|
|
if GOOGLE_SHEET_URL and GOOGLE_CREDENTIALS: |
|
|
results_row = { |
|
|
"datetime": datetime.now().isoformat(), |
|
|
"text_id": text_id, |
|
|
"text": text, |
|
|
"binary_score": binary_score, |
|
|
"hateful_l1_score": hateful_l1_score, |
|
|
"hateful_l2_score": hateful_l2_score, |
|
|
"insults_score": insults_score, |
|
|
"sexual_l1_score": sexual_l1_score, |
|
|
"sexual_l2_score": sexual_l2_score, |
|
|
"physical_violence_score": physical_violence_score, |
|
|
"self_harm_l1_score": self_harm_l1_score, |
|
|
"self_harm_l2_score": self_harm_l2_score, |
|
|
"aom_l1_score": aom_l1_score, |
|
|
"aom_l2_score": aom_l2_score |
|
|
} |
|
|
save_results_data(results_row) |
|
|
|
|
|
|
|
|
categories_html = [] |
|
|
|
|
|
|
|
|
main_categories = ['hateful', 'insults', 'sexual', 'physical_violence', 'self_harm', 'all_other_misconduct'] |
|
|
|
|
|
for category in main_categories: |
|
|
subcategories = CATEGORIES[category] |
|
|
category_name = category.replace('_', ' ').title() |
|
|
|
|
|
|
|
|
category_emojis = { |
|
|
'Hateful': '🤬', |
|
|
'Insults': '💢', |
|
|
'Sexual': '🔞', |
|
|
'Physical Violence': '⚔️', |
|
|
'Self Harm': '☹️', |
|
|
'All Other Misconduct': '🙅♀️' |
|
|
} |
|
|
category_display = f"{category_emojis.get(category_name, '📝')} {category_name}" |
|
|
|
|
|
|
|
|
level_scores = [] |
|
|
for i, subcategory_key in enumerate(subcategories): |
|
|
score = results.get(subcategory_key, [0.0])[0] |
|
|
level_scores.append((f"Level {i+1}", score)) |
|
|
|
|
|
|
|
|
max_score = max([score for _, score in level_scores]) if level_scores else 0.0 |
|
|
|
|
|
|
|
|
categories_html.append(f''' |
|
|
<tr style="border-bottom: 1px solid #374151; transition: background-color 0.2s ease;"> |
|
|
<td style="padding: 16px; font-weight: 500; color: #f9fafb; font-size: 15px;">{category_display}</td> |
|
|
<td style="padding: 16px; text-align: center;">{format_score_with_style(f"{max_score:.4f}")}</td> |
|
|
</tr> |
|
|
''') |
|
|
|
|
|
|
|
|
html_table = f''' |
|
|
<div style="margin: 24px 0;"> |
|
|
<div style="margin-bottom: 20px; text-align: center;"> |
|
|
<h2 style="color: #f9fafb; font-size: 20px; font-weight: 600; margin-bottom: 6px;">📊 Category-Specific Scores</h2> |
|
|
</div> |
|
|
<div style="background: #1f2937; border-radius: 12px; overflow: hidden; box-shadow: 0 4px 12px rgba(0,0,0,0.3); border: 1px solid #374151;"> |
|
|
<table style="width: 100%; border-collapse: collapse;"> |
|
|
<thead> |
|
|
<tr style="background: linear-gradient(135deg, #374151 0%, #4b5563 100%);"> |
|
|
<th style="padding: 16px; text-align: left; font-weight: 600; font-size: 15px; color: #f9fafb;">Category</th> |
|
|
<th style="padding: 16px; text-align: center; font-weight: 600; font-size: 15px; color: #f9fafb;">Score</th> |
|
|
</tr> |
|
|
</thead> |
|
|
<tbody> |
|
|
{"".join(categories_html)} |
|
|
</tbody> |
|
|
</table> |
|
|
</div> |
|
|
</div> |
|
|
''' |
|
|
|
|
|
|
|
|
voting_html = f''' |
|
|
<div style="margin: 24px 0; text-align: center;"> |
|
|
<div style="background: #1f2937; border-radius: 12px; padding: 20px; box-shadow: 0 4px 12px rgba(0,0,0,0.3); border: 1px solid #374151;"> |
|
|
<h3 style="color: #f9fafb; font-size: 18px; font-weight: 600; margin-bottom: 12px;">📊 How accurate are these results?</h3> |
|
|
<p style="color: #d1d5db; font-size: 14px; margin-bottom: 16px;">Your feedback helps improve the model</p> |
|
|
</div> |
|
|
</div> |
|
|
''' |
|
|
|
|
|
return format_binary_score(binary_score), html_table, text_id, voting_html |
|
|
|
|
|
except Exception as e: |
|
|
error_msg = f"Error analyzing text: {str(e)}" |
|
|
error_html = f'<div style="background: linear-gradient(135deg, #991b1b 0%, #b91c1c 100%); color: #fca5a5; padding: 20px; border-radius: 12px; text-align: center; border: 2px solid #ef4444; box-shadow: 0 4px 12px rgba(0,0,0,0.3);">❌ {error_msg}</div>' |
|
|
return f'<div style="background: linear-gradient(135deg, #991b1b 0%, #b91c1c 100%); color: #fca5a5; padding: 16px; border-radius: 8px; text-align: center; border: 1px solid #ef4444;">❌ {error_msg}</div>', error_html, "", "" |
|
|
|
|
|
|
|
|
|
|
|
def vote_thumbs_up(text_id): |
|
|
"""Handle thumbs up vote""" |
|
|
if text_id and GOOGLE_SHEET_URL and GOOGLE_CREDENTIALS: |
|
|
save_vote_data(text_id, True) |
|
|
return ''' |
|
|
<div style="background: linear-gradient(135deg, #065f46 0%, #047857 100%); color: #34d399; padding: 24px; border-radius: 16px; text-align: center; font-weight: 700; border: 2px solid #10b981; margin: 16px 0; box-shadow: 0 8px 25px rgba(16, 185, 129, 0.4); animation: bounceIn 0.6s ease-out;"> |
|
|
<div style="font-size: 48px; margin-bottom: 12px;">🎉</div> |
|
|
<h3 style="color: #ffffff; font-size: 20px; margin-bottom: 8px; text-shadow: 0 2px 4px rgba(0,0,0,0.3);"> |
|
|
Awesome! Thank you! |
|
|
</h3> |
|
|
<p style="color: #a7f3d0; font-size: 14px; margin: 0;"> |
|
|
Your positive feedback helps improve our AI safety models |
|
|
</p> |
|
|
</div> |
|
|
''' |
|
|
return '<div style="color: #9ca3af; text-align: center; padding: 16px;">Voting not available</div>' |
|
|
|
|
|
def vote_thumbs_down(text_id): |
|
|
"""Handle thumbs down vote""" |
|
|
if text_id and GOOGLE_SHEET_URL and GOOGLE_CREDENTIALS: |
|
|
save_vote_data(text_id, False) |
|
|
return ''' |
|
|
<div style="background: linear-gradient(135deg, #991b1b 0%, #b91c1c 100%); color: #fca5a5; padding: 24px; border-radius: 16px; text-align: center; font-weight: 700; border: 2px solid #ef4444; margin: 16px 0; box-shadow: 0 8px 25px rgba(239, 68, 68, 0.4); animation: bounceIn 0.6s ease-out;"> |
|
|
<div style="font-size: 48px; margin-bottom: 12px;">📝</div> |
|
|
<h3 style="color: #ffffff; font-size: 20px; margin-bottom: 8px; text-shadow: 0 2px 4px rgba(0,0,0,0.3);"> |
|
|
Thanks for the feedback! |
|
|
</h3> |
|
|
<p style="color: #fecaca; font-size: 14px; margin: 0;"> |
|
|
Critical feedback helps us identify and fix model weaknesses |
|
|
</p> |
|
|
</div> |
|
|
''' |
|
|
return '<div style="color: #9ca3af; text-align: center; padding: 16px;">Voting not available</div>' |
|
|
|
|
|
|
|
|
with gr.Blocks(title="LionGuard2", theme=gr.themes.Base().set( |
|
|
body_background_fill="*neutral_950", |
|
|
background_fill_primary="*neutral_900", |
|
|
background_fill_secondary="*neutral_800", |
|
|
border_color_primary="*neutral_700", |
|
|
color_accent_soft="*blue_500" |
|
|
)) as demo: |
|
|
gr.HTML(""" |
|
|
<div style="text-align: center; margin-bottom: 40px; padding: 20px;"> |
|
|
|
|
|
</div> |
|
|
""") |
|
|
|
|
|
with gr.Row(): |
|
|
with gr.Column(scale=1, min_width=400): |
|
|
text_input = gr.Textbox( |
|
|
label="Enter text to analyze:", |
|
|
placeholder="Type your text here...", |
|
|
lines=12, |
|
|
max_lines=20, |
|
|
container=True |
|
|
) |
|
|
|
|
|
analyze_btn = gr.Button("🔍 Analyze Text", variant="primary") |
|
|
|
|
|
with gr.Column(scale=1, min_width=400): |
|
|
gr.HTML(""" |
|
|
<div style="margin-bottom: 24px; text-align: center;"> |
|
|
<h2 style="color: #f9fafb; font-size: 22px; font-weight: 600; margin-bottom: 8px;">Overall Safety Score</h2> |
|
|
<p style="color: #d1d5db; font-size: 14px; margin: 0; opacity: 0.8;">Higher percentages indicate higher likelihood of harmful content</p> |
|
|
</div> |
|
|
""") |
|
|
|
|
|
binary_output = gr.HTML( |
|
|
value='<div style="text-align: center; color: #9ca3af; padding: 30px; font-style: italic;">Enter text to analyze</div>' |
|
|
) |
|
|
|
|
|
category_table = gr.HTML( |
|
|
value='<div style="text-align: center; color: #9ca3af; padding: 30px; font-style: italic;">Category scores will appear here after analysis</div>' |
|
|
) |
|
|
|
|
|
|
|
|
with gr.Row(): |
|
|
with gr.Column(): |
|
|
gr.HTML(""" |
|
|
<div style="background: linear-gradient(135deg, #1e40af 0%, #3b82f6 100%); border-radius: 16px; padding: 24px; margin: 20px 0; text-align: center; border: 2px solid #60a5fa; box-shadow: 0 8px 25px rgba(59, 130, 246, 0.3); position: relative; overflow: hidden;"> |
|
|
<div style="position: absolute; top: -50%; left: -50%; width: 200%; height: 200%; background: radial-gradient(circle, rgba(255,255,255,0.1) 0%, transparent 70%); animation: pulse 3s ease-in-out infinite;"></div> |
|
|
<div style="position: relative; z-index: 1;"> |
|
|
<h3 style="color: #ffffff; font-size: 22px; font-weight: 700; margin-bottom: 12px; text-shadow: 0 2px 4px rgba(0,0,0,0.3);"> |
|
|
🎯 Help Us Improve! Rate the Analysis |
|
|
</h3> |
|
|
<p style="color: #dbeafe; font-size: 16px; margin-bottom: 0; font-weight: 500;"> |
|
|
Your feedback trains better AI safety models |
|
|
</p> |
|
|
</div> |
|
|
</div> |
|
|
""") |
|
|
|
|
|
voting_instruction = gr.HTML(value=""" |
|
|
<div id="voting-instructions" style="background: #374151; border-radius: 12px; padding: 20px; margin: 16px 0; text-align: center; border: 2px dashed #6b7280; box-shadow: 0 4px 12px rgba(0,0,0,0.2);"> |
|
|
<p style="color: #d1d5db; font-size: 16px; margin: 0; font-weight: 500;"> |
|
|
⬆️ Analyze some text above to unlock voting ⬆️ |
|
|
</p> |
|
|
</div> |
|
|
""") |
|
|
|
|
|
with gr.Row(visible=False) as voting_buttons_row: |
|
|
with gr.Column(scale=1): |
|
|
thumbs_up_btn = gr.Button( |
|
|
"👍 Results Look Accurate", |
|
|
variant="primary", |
|
|
size="lg", |
|
|
elem_classes=["voting-btn-positive"] |
|
|
) |
|
|
with gr.Column(scale=1): |
|
|
thumbs_down_btn = gr.Button( |
|
|
"👎 Results Look Wrong", |
|
|
variant="secondary", |
|
|
size="lg", |
|
|
elem_classes=["voting-btn-negative"] |
|
|
) |
|
|
|
|
|
vote_feedback = gr.HTML(value="") |
|
|
|
|
|
|
|
|
current_text_id = gr.Textbox(value="", visible=False) |
|
|
|
|
|
|
|
|
with gr.Row(): |
|
|
with gr.Accordion("ℹ️ About the Scoring System", open=False): |
|
|
gr.HTML(""" |
|
|
<div style="font-size: 14px; line-height: 1.6; color: #f3f4f6; padding: 10px;"> |
|
|
<h3 style="color: #f9fafb; margin-bottom: 16px;">How Scoring Works:</h3> |
|
|
<ul style="color: #d1d5db; margin-bottom: 24px;"> |
|
|
<li><b>Percentages represent likelihood of harmful content</b> - Higher % = More likely to be harmful</li> |
|
|
<li><b>0-40%:</b> Content appears safe</li> |
|
|
<li><b>40-70%:</b> Potentially concerning content that warrants review</li> |
|
|
<li><b>70-100%:</b> High likelihood of policy violation</li> |
|
|
</ul> |
|
|
<h3 style="color: #f9fafb; margin-bottom: 16px;">Content Categories (Singapore Context):</h3> |
|
|
<ul style="color: #d1d5db;"> |
|
|
<li><b>🤬 Hateful:</b> Content targeting Singapore's protected traits (e.g., race, religion), including discriminatory remarks and explicit calls for harm/violence.</li> |
|
|
<li><b>💢 Insults:</b> Personal attacks on non-protected attributes (e.g., appearance). Note: Sexuality attacks are classified as insults, not hateful, in Singapore.</li> |
|
|
<li><b>🔞 Sexual:</b> Sexual content or adult themes, ranging from mild content inappropriate for minors to explicit content inappropriate for general audiences.</li> |
|
|
<li><b>⚔️ Physical Violence:</b> Threats, descriptions, or glorification of physical harm against individuals or groups (not property damage).</li> |
|
|
<li><b>☹️ Self Harm:</b> Content about self-harm or suicide, including ideation, encouragement, or descriptions of ongoing actions.</li> |
|
|
<li><b>🙅♀️ All Other Misconduct:</b> Unethical/criminal conduct not covered above, from socially condemned behavior to clearly illegal activities under Singapore law.</li> |
|
|
</ul> |
|
|
</div> |
|
|
""") |
|
|
|
|
|
|
|
|
def analyze_and_show_voting(text): |
|
|
binary_score, category_table, text_id, voting_html = analyze_text(text) |
|
|
|
|
|
if text_id: |
|
|
voting_buttons_update = gr.update(visible=True) |
|
|
voting_instruction_update = gr.update(value=""" |
|
|
<div style="background: linear-gradient(135deg, #065f46 0%, #047857 100%); border-radius: 12px; padding: 20px; margin: 16px 0; text-align: center; border: 2px solid #10b981; box-shadow: 0 4px 12px rgba(16, 185, 129, 0.3);"> |
|
|
<p style="color: #34d399; font-size: 16px; margin: 0; font-weight: 600;"> |
|
|
✅ Analysis Complete! Please rate the accuracy below 👇 |
|
|
</p> |
|
|
</div> |
|
|
""") |
|
|
else: |
|
|
voting_buttons_update = gr.update(visible=False) |
|
|
voting_instruction_update = gr.update(value=""" |
|
|
<div style="background: #374151; border-radius: 12px; padding: 20px; margin: 16px 0; text-align: center; border: 2px dashed #6b7280; box-shadow: 0 4px 12px rgba(0,0,0,0.2);"> |
|
|
<p style="color: #d1d5db; font-size: 16px; margin: 0; font-weight: 500;"> |
|
|
⬆️ Analyze some text above to unlock voting ⬆️ |
|
|
</p> |
|
|
</div> |
|
|
""") |
|
|
return binary_score, category_table, text_id, voting_buttons_update, voting_instruction_update, "" |
|
|
|
|
|
|
|
|
analyze_btn.click( |
|
|
fn=analyze_and_show_voting, |
|
|
inputs=[text_input], |
|
|
outputs=[binary_output, category_table, current_text_id, voting_buttons_row, voting_instruction, vote_feedback] |
|
|
) |
|
|
|
|
|
|
|
|
text_input.submit( |
|
|
fn=analyze_and_show_voting, |
|
|
inputs=[text_input], |
|
|
outputs=[binary_output, category_table, current_text_id, voting_buttons_row, voting_instruction, vote_feedback] |
|
|
) |
|
|
|
|
|
|
|
|
thumbs_up_btn.click( |
|
|
fn=vote_thumbs_up, |
|
|
inputs=[current_text_id], |
|
|
outputs=[vote_feedback] |
|
|
) |
|
|
|
|
|
thumbs_down_btn.click( |
|
|
fn=vote_thumbs_down, |
|
|
inputs=[current_text_id], |
|
|
outputs=[vote_feedback] |
|
|
) |
|
|
|
|
|
|
|
|
if __name__ == "__main__": |
|
|
demo.launch(share=True, server_name="0.0.0.0", server_port=7860) |
|
|
|
