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import gradio as gr
import requests
import json
from typing import Dict, List, Tuple
BRAND_EXAMPLES = [
"Nike - Just Do It. The power of determination.",
"Apple - Think Different. Innovation redefined.",
"McDonald's - I'm Lovin' It. Creating joy.",
"BMW - The Ultimate Driving Machine.",
"L'Oréal - Because You're Worth It."
]
def get_top_features(text: str, k: int = 5) -> Dict:
url = "https://www.neuronpedia.org/api/search-with-topk"
payload = {
"modelId": "gemma-2-2b",
"layer": "0-gemmascope-mlp-16k",
"sourceSet": "gemma-scope",
"text": text,
"k": k,
"maxDensity": 0.01,
"ignoreBos": True
}
response = requests.post(
url,
headers={"Content-Type": "application/json"},
json=payload
)
return response.json() if response.status_code == 200 else None
def format_output(data: Dict) -> Tuple[str, str, str]:
if not data:
return "Error analyzing text", "", ""
output = "# Neural Feature Analysis\n\n"
# Format token-feature analysis
for result in data['results']:
token = result['token']
if token == '<bos>': # Skip BOS token
continue
features = result['top_features']
if features:
output += f"\n## Token: '{token}'\n"
for feat in features:
feat_index = feat['feature_index']
activation = feat['activation_value']
output += f"- **Feature {feat_index}**: activation = {activation:.2f}\n"
# Get highest activation feature for dashboard
max_activation = 0
max_feature = None
for result in data['results']:
for feature in result['top_features']:
if feature['activation_value'] > max_activation:
max_activation = feature['activation_value']
max_feature = feature['feature_index']
if max_feature:
dashboard_url = f"https://www.neuronpedia.org/gemma-2-2b/0-gemmascope-mlp-16k/{max_feature}?embed=true&embedexplanation=true&embedplots=true&embedtest=true&height=300"
iframe = f'<iframe src="{dashboard_url}" width="100%" height="600px" frameborder="0" style="border:1px solid #eee;border-radius:8px;"></iframe>'
feature_label = f"Feature {max_feature} Dashboard (Highest Activation: {max_activation:.2f})"
else:
iframe = ""
feature_label = "No significant features found"
return output, iframe, feature_label
def create_interface():
with gr.Blocks() as interface:
gr.Markdown("# Neural Feature Analyzer")
gr.Markdown("Analyze text using Gemma's interpretable neural features\n\nShows top 5 most activated features for each token with density < 1%")
with gr.Row():
with gr.Column():
input_text = gr.Textbox(
lines=5,
placeholder="Enter text to analyze...",
label="Input Text"
)
analyze_btn = gr.Button("Analyze Neural Features", variant="primary")
gr.Examples(BRAND_EXAMPLES, inputs=input_text)
with gr.Column():
output_text = gr.Markdown()
feature_label = gr.Text(show_label=False)
dashboard = gr.HTML()
analyze_btn.click(
fn=lambda text: format_output(get_top_features(text)),
inputs=input_text,
outputs=[output_text, dashboard, feature_label]
)
return interface
if __name__ == "__main__":
create_interface().launch() |