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Upload app.py
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app.py
CHANGED
@@ -6,6 +6,7 @@ from typing import List, Dict, Tuple, Union
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import json
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import os
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from collections import OrderedDict
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@dataclass
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class ScorecardCategory:
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@@ -13,17 +14,82 @@ class ScorecardCategory:
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questions: List[Dict[str, Union[str, List[str]]]]
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scores: Dict[str, int] = field(default_factory=dict)
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def load_models_from_json(directory):
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models = {}
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@@ -37,7 +103,7 @@ def load_models_from_json(directory):
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return OrderedDict(sorted(models.items(), key=lambda x: x[0].lower()))
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# Load templates and models
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scorecard_template = load_scorecard_templates('scorecard_templates')
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models = load_models_from_json('model_data')
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def create_source_html(sources):
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@@ -92,6 +158,9 @@ def create_category_chart(selected_models, selected_categories):
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if not selected_models:
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return px.bar(title='Please select at least one model for comparison')
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data = []
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for model in selected_models:
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for category in selected_categories:
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@@ -129,10 +198,18 @@ def update_detailed_scorecard(model, selected_categories):
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gr.update(visible=False),
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gr.update(visible=False)
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]
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total_yes = 0
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total_no = 0
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@@ -144,21 +221,27 @@ def update_detailed_scorecard(model, selected_categories):
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category_data = models[model]['scores'][category_name]
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card_content = f"<div class='card'><div class='card-title'>{category_name}</div>"
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category_yes = 0
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category_no = 0
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category_na = 0
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for section, details in
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status = details['status']
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sources = details.get('sources', [])
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questions = details.get('questions', {})
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# Determine section class based on status
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section_class = "section-na" if status == "N/A" else "section-active"
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card_content += f"<div class='section {section_class}'>"
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card_content += f"<h3>{section}</h3>"
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# Add sources if they exist
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if sources:
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card_content += "<div class='sources-list'>"
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for source in sources:
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@@ -174,13 +257,21 @@ def update_detailed_scorecard(model, selected_categories):
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card_content += "</div>"
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card_content += "</div>"
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# Process questions
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if questions:
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card_content += "<div class='questions'>"
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for question, is_checked in questions.items():
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if status == "N/A":
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style_class = "na"
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icon = "○"
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category_na += 1
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total_na += 1
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else:
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@@ -196,23 +287,21 @@ def update_detailed_scorecard(model, selected_categories):
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total_no += 1
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card_content += f"<div class='question-item {style_class}'>{icon} {question}</div>"
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card_content += "</div>"
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card_content += "</div>"
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# Calculate category score (excluding N/A items)
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if category_yes + category_no > 0:
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category_score = category_yes / (category_yes + category_no) * 100
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card_content += f"<div class='category-score'>Category Score: {category_score:.2f}% (Yes: {category_yes}, No: {category_no}, N/A: {category_na})</div>"
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elif category_na > 0:
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card_content += f"<div class='category-score'>Category Score: N/A (All {category_na} items not applicable)</div>"
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card_content += "</div>"
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all_cards_content += card_content
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all_cards_content += "</div>"
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# Calculate total score (excluding N/A items)
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if total_yes + total_no > 0:
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total_score = total_yes / (total_yes + total_no) * 100
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total_score_md = f"<div class='total-score'>Total Score: {total_score:.2f}% (Yes: {total_yes}, No: {total_no}, N/A: {total_na})</div>"
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@@ -220,7 +309,7 @@ def update_detailed_scorecard(model, selected_categories):
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total_score_md = "<div class='total-score'>No applicable scores (all items N/A)</div>"
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return [
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gr.update(value=
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gr.update(value=all_cards_content, visible=True),
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gr.update(value=total_score_md, visible=True)
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]
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@@ -365,8 +454,152 @@ css = """
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background-color: #2d2d2d;
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color: #999;
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}
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"""
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with gr.Blocks(css=css) as demo:
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gr.Markdown("# AI Model Social Impact Scorecard Dashboard")
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model_multi_chooser = gr.Dropdown(choices=list(models.keys()),
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label="Select Models for Comparison",
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multiselect=True, interactive=True, visible=False)
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category_filter = gr.CheckboxGroup(choices=
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label="Filter Categories",
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value=
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visible=False)
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with gr.Column(visible=True) as leaderboard_tab:
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category_chart = gr.Plot()
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with gr.Column(visible=False) as detailed_scorecard_tab:
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model_metadata = gr.
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all_category_cards = gr.HTML()
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total_score = gr.Markdown()
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import json
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import os
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from collections import OrderedDict
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import re
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@dataclass
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class ScorecardCategory:
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questions: List[Dict[str, Union[str, List[str]]]]
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scores: Dict[str, int] = field(default_factory=dict)
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def extract_category_number(category_name: str) -> int:
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"""Extract the category number from the category name."""
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match = re.match(r'^(\d+)\.?\s*.*$', category_name)
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return int(match.group(1)) if match else float('inf')
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def sort_categories(categories):
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"""Sort categories by their numeric prefix."""
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return sorted(categories, key=extract_category_number)
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# def load_scorecard_templates(directory):
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# templates = []
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# for filename in os.listdir(directory):
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# if filename.endswith('.json'):
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# with open(os.path.join(directory, filename), 'r') as file:
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# data = json.load(file)
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# templates.append(ScorecardCategory(
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# name=data['name'],
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# questions=data['questions']
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# ))
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# return templates
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def get_modality_icon(modality):
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"""Return an emoji icon for each modality type."""
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icons = {
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"Text-to-Text": "📝", # Memo icon for text-to-text
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"Text-to-Image": "🎨", # Artist palette for text-to-image
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"Image-to-Text": "🔍", # Magnifying glass for image-to-text
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"Image-to-Image": "🖼️", # Frame for image-to-image
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"Audio": "🎵", # Musical note for audio
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"Video": "🎬", # Clapper board for video
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"Multimodal": "🔄" # Cycle arrows for multimodal
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}
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return icons.get(modality, "💫") # Default icon if modality not found
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def create_metadata_card(metadata):
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"""Create a formatted HTML card for metadata."""
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html = "<div class='card metadata-card'>"
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html += "<div class='card-title'>Model Information</div>"
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html += "<div class='metadata-content'>"
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# Handle special formatting for modalities
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modalities = metadata.get("Modalities", [])
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formatted_modalities = ""
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if modalities:
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formatted_modalities = " ".join(
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f"<span class='modality-badge'>{get_modality_icon(m)} {m}</span>"
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for m in modalities
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)
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# Order of metadata display (customize as needed)
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display_order = ["Name", "Provider", "Type", "URL"]
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# Display ordered metadata first
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for key in display_order:
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if key in metadata:
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value = metadata[key]
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if key == "URL":
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html += f"<div class='metadata-row'><span class='metadata-label'>{key}:</span> "
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html += f"<a href='{value}' target='_blank' class='metadata-link'>{value}</a></div>"
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else:
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html += f"<div class='metadata-row'><span class='metadata-label'>{key}:</span> <span class='metadata-value'>{value}</span></div>"
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# Add modalities if present
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if formatted_modalities:
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html += f"<div class='metadata-row'><span class='metadata-label'>Modalities:</span> <div class='modality-container'>{formatted_modalities}</div></div>"
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# Add any remaining metadata not in display_order
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for key, value in metadata.items():
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if key not in display_order and key != "Modalities":
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html += f"<div class='metadata-row'><span class='metadata-label'>{key}:</span> <span class='metadata-value'>{value}</span></div>"
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html += "</div></div>"
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return html
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def load_models_from_json(directory):
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models = {}
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return OrderedDict(sorted(models.items(), key=lambda x: x[0].lower()))
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# Load templates and models
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# scorecard_template = load_scorecard_templates('scorecard_templates')
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models = load_models_from_json('model_data')
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def create_source_html(sources):
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if not selected_models:
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return px.bar(title='Please select at least one model for comparison')
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# Sort categories before processing
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selected_categories = sort_categories(selected_categories)
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data = []
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for model in selected_models:
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for category in selected_categories:
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gr.update(visible=False),
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gr.update(visible=False)
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]
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print("Selected categories:", selected_categories)
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print("Available categories in model:", list(models[model]['scores'].keys()))
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# Sort categories before processing
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selected_categories = sort_categories(selected_categories)
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metadata_html = create_metadata_card(models[model]['metadata'])
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# metadata_md = f"## Model Metadata for {model}\n\n"
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# for key, value in models[model]['metadata'].items():
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# metadata_md += f"**{key}:** {value}\n\n"
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total_yes = 0
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total_no = 0
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category_data = models[model]['scores'][category_name]
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card_content = f"<div class='card'><div class='card-title'>{category_name}</div>"
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# Sort sections within each category
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sorted_sections = sorted(category_data.items(),
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key=lambda x: float(re.match(r'^(\d+\.?\d*)', x[0]).group(1)))
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category_yes = 0
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category_no = 0
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category_na = 0
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for section, details in sorted_sections:
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status = details['status']
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sources = details.get('sources', [])
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questions = details.get('questions', {})
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section_class = "section-na" if status == "N/A" else "section-active"
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status_class = status.lower()
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status_icon = "●" if status == "Yes" else "○" if status == "N/A" else "×"
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card_content += f"<div class='section {section_class}'>"
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card_content += f"<div class='section-header'><h3>{section}</h3>"
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card_content += f"<span class='status-badge {status_class}'>{status_icon} {status}</span></div>"
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if sources:
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card_content += "<div class='sources-list'>"
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for source in sources:
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card_content += "</div>"
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card_content += "</div>"
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if questions:
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yes_count = sum(1 for v in questions.values() if v)
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total_count = len(questions)
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card_content += "<details class='question-accordion'>"
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if status == "N/A":
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card_content += f"<summary>View {total_count} N/A items</summary>"
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else:
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card_content += f"<summary>View details ({yes_count}/{total_count} completed)</summary>"
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card_content += "<div class='questions'>"
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for question, is_checked in questions.items():
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if status == "N/A":
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style_class = "na"
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icon = "○"
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category_na += 1
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total_na += 1
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else:
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total_no += 1
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card_content += f"<div class='question-item {style_class}'>{icon} {question}</div>"
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card_content += "</div></details>"
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card_content += "</div>"
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if category_yes + category_no > 0:
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category_score = category_yes / (category_yes + category_no) * 100
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card_content += f"<div class='category-score'>Category Score: {category_score:.2f}% (Yes: {category_yes}, No: {category_no}, N/A: {category_na})</div>"
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elif category_na > 0:
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card_content += f"<div class='category-score'>Category Score: N/A (All {category_na} items not applicable)</div>"
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card_content += "</div>"
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all_cards_content += card_content
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all_cards_content += "</div>"
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if total_yes + total_no > 0:
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total_score = total_yes / (total_yes + total_no) * 100
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total_score_md = f"<div class='total-score'>Total Score: {total_score:.2f}% (Yes: {total_yes}, No: {total_no}, N/A: {total_na})</div>"
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total_score_md = "<div class='total-score'>No applicable scores (all items N/A)</div>"
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return [
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gr.update(value=metadata_html, visible=True),
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gr.update(value=all_cards_content, visible=True),
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gr.update(value=total_score_md, visible=True)
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]
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background-color: #2d2d2d;
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color: #999;
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}
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.section-header {
|
459 |
+
display: flex;
|
460 |
+
justify-content: space-between;
|
461 |
+
align-items: center;
|
462 |
+
margin-bottom: 10px;
|
463 |
+
}
|
464 |
+
|
465 |
+
.status-badge {
|
466 |
+
font-size: 0.9em;
|
467 |
+
padding: 4px 8px;
|
468 |
+
border-radius: 12px;
|
469 |
+
font-weight: 500;
|
470 |
+
}
|
471 |
+
|
472 |
+
.status-badge.yes {
|
473 |
+
background-color: #e6ffe6;
|
474 |
+
color: #006600;
|
475 |
+
}
|
476 |
+
|
477 |
+
.status-badge.no {
|
478 |
+
background-color: #ffe6e6;
|
479 |
+
color: #990000;
|
480 |
+
}
|
481 |
+
|
482 |
+
.status-badge.n\/a {
|
483 |
+
background-color: #f0f0f0;
|
484 |
+
color: #666666;
|
485 |
+
}
|
486 |
+
|
487 |
+
.question-accordion {
|
488 |
+
margin-top: 10px;
|
489 |
+
}
|
490 |
+
|
491 |
+
.question-accordion summary {
|
492 |
+
cursor: pointer;
|
493 |
+
padding: 8px;
|
494 |
+
background-color: #f8f9fa;
|
495 |
+
border-radius: 4px;
|
496 |
+
margin-bottom: 10px;
|
497 |
+
font-weight: 500;
|
498 |
+
}
|
499 |
+
|
500 |
+
.question-accordion summary:hover {
|
501 |
+
background-color: #e9ecef;
|
502 |
+
}
|
503 |
+
|
504 |
+
.dark .status-badge.yes {
|
505 |
+
background-color: #1a3a1a;
|
506 |
+
color: #90EE90;
|
507 |
+
}
|
508 |
+
|
509 |
+
.dark .status-badge.no {
|
510 |
+
background-color: #3a1a1a;
|
511 |
+
color: #FFB6B6;
|
512 |
+
}
|
513 |
+
|
514 |
+
.dark .status-badge.n\/a {
|
515 |
+
background-color: #2d2d2d;
|
516 |
+
color: #999999;
|
517 |
+
}
|
518 |
+
|
519 |
+
.dark .question-accordion summary {
|
520 |
+
background-color: #2a2a2a;
|
521 |
+
}
|
522 |
+
|
523 |
+
.dark .question-accordion summary:hover {
|
524 |
+
background-color: #333333;
|
525 |
+
}
|
526 |
+
.metadata-card {
|
527 |
+
margin-bottom: 30px;
|
528 |
+
width: 100% !important;
|
529 |
+
}
|
530 |
+
|
531 |
+
.metadata-content {
|
532 |
+
display: flex;
|
533 |
+
flex-direction: column;
|
534 |
+
gap: 12px;
|
535 |
+
}
|
536 |
+
|
537 |
+
.metadata-row {
|
538 |
+
display: flex;
|
539 |
+
align-items: flex-start;
|
540 |
+
gap: 10px;
|
541 |
+
line-height: 1.5;
|
542 |
+
}
|
543 |
+
|
544 |
+
.metadata-label {
|
545 |
+
font-weight: 600;
|
546 |
+
min-width: 100px;
|
547 |
+
color: #555;
|
548 |
+
}
|
549 |
+
|
550 |
+
.metadata-value {
|
551 |
+
color: #333;
|
552 |
+
}
|
553 |
+
|
554 |
+
.metadata-link {
|
555 |
+
color: #007bff;
|
556 |
+
text-decoration: none;
|
557 |
+
}
|
558 |
+
|
559 |
+
.metadata-link:hover {
|
560 |
+
text-decoration: underline;
|
561 |
+
}
|
562 |
+
|
563 |
+
.modality-container {
|
564 |
+
display: flex;
|
565 |
+
flex-wrap: wrap;
|
566 |
+
gap: 8px;
|
567 |
+
}
|
568 |
+
|
569 |
+
.modality-badge {
|
570 |
+
display: inline-flex;
|
571 |
+
align-items: center;
|
572 |
+
gap: 4px;
|
573 |
+
padding: 4px 10px;
|
574 |
+
background-color: #f0f7ff;
|
575 |
+
border: 1px solid #cce3ff;
|
576 |
+
border-radius: 15px;
|
577 |
+
font-size: 0.9em;
|
578 |
+
color: #0066cc;
|
579 |
+
}
|
580 |
+
|
581 |
+
.dark .metadata-label {
|
582 |
+
color: #aaa;
|
583 |
+
}
|
584 |
+
|
585 |
+
.dark .metadata-value {
|
586 |
+
color: #ddd;
|
587 |
+
}
|
588 |
+
|
589 |
+
.dark .metadata-link {
|
590 |
+
color: #66b3ff;
|
591 |
+
}
|
592 |
+
|
593 |
+
.dark .modality-badge {
|
594 |
+
background-color: #1a2733;
|
595 |
+
border-color: #2c3e50;
|
596 |
+
color: #99ccff;
|
597 |
+
}
|
598 |
"""
|
599 |
|
600 |
+
first_model = next(iter(models.values()))
|
601 |
+
category_choices = list(first_model['scores'].keys())
|
602 |
+
|
603 |
with gr.Blocks(css=css) as demo:
|
604 |
gr.Markdown("# AI Model Social Impact Scorecard Dashboard")
|
605 |
|
|
|
615 |
model_multi_chooser = gr.Dropdown(choices=list(models.keys()),
|
616 |
label="Select Models for Comparison",
|
617 |
multiselect=True, interactive=True, visible=False)
|
618 |
+
category_filter = gr.CheckboxGroup(choices=category_choices,
|
619 |
label="Filter Categories",
|
620 |
+
value=category_choices,
|
621 |
visible=False)
|
622 |
|
623 |
with gr.Column(visible=True) as leaderboard_tab:
|
|
|
627 |
category_chart = gr.Plot()
|
628 |
|
629 |
with gr.Column(visible=False) as detailed_scorecard_tab:
|
630 |
+
model_metadata = gr.HTML()
|
631 |
all_category_cards = gr.HTML()
|
632 |
total_score = gr.Markdown()
|
633 |
|