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65075c7 ef34a75 65075c7 ef34a75 65075c7 | 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 | import pandas as pd
import json
import plotly.express as px
import gradio as gr
def load_data():
file_path = "causalquest_embeddings_tsne.json"
with open(file_path, 'r') as f:
data = json.load(f)
df = pd.DataFrame({
'query_id': data['query_id'],
'query': data['query'],
'source': data['source'],
'is_causal': data['is_causal'],
'domain_class': data['domain_class'],
'action_class': data['action_class'],
'is_subjective': data['is_subjective'],
'x': data['tsne_x'],
'y': data['tsne_y']
})
# subsample data for faster visualization
df = df.sample(1000, random_state=42)
return df
def create_plot(df):
fig = px.scatter(df, x='x', y='y', hover_data=['query', 'source', 'is_causal', 'domain_class', 'action_class', 'is_subjective'])
# Remove the hover box
fig.update_traces(hoverinfo='none', hovertemplate=None)
# Function to wrap long text
def wrap_text(text, max_length=50):
if len(text) <= max_length:
return text
words = text.split()
lines = []
current_line = []
current_length = 0
for word in words:
if current_length + len(word) + 1 <= max_length:
current_line.append(word)
current_length += len(word) + 1
else:
lines.append(' '.join(current_line))
current_line = [word]
current_length = len(word)
if current_line:
lines.append(' '.join(current_line))
return '<br>'.join(lines)
# Apply text wrapping to queries
df['wrapped_query'] = df['query'].apply(wrap_text)
# Add custom hover text with wrapped query
fig.update_traces(
customdata=df[['wrapped_query', 'source', 'is_causal', 'domain_class', 'action_class', 'is_subjective']],
hovertemplate='Query: %{customdata[0]}<br>Source: %{customdata[1]}<br>Is Causal: %{customdata[2]}'
)
return fig
def visualize_data(processed_file):
df = load_data()
fig = create_plot(df)
return fig
# Create Gradio interface
iface = gr.Interface(
fn=visualize_data,
inputs=None,
outputs=gr.Plot(label="tsne Visualization"),
title="CausalQuest Visualization",
description="Visualize causalquest data using pre-computed embeddings and tsne coordinates",
allow_flagging="never",
fill_width=True
)
# Launch the app
iface.launch() |