Spaces:
Running
Running
File size: 4,967 Bytes
0244d3c 6934db6 7e141c2 0244d3c 9825333 0244d3c 6934db6 0244d3c 6934db6 0244d3c 6934db6 0244d3c 7e141c2 0244d3c 7e141c2 0244d3c 9825333 0244d3c 9825333 0244d3c 6934db6 0244d3c 7e141c2 0244d3c 7e141c2 0244d3c |
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 |
import gradio as gr
import json
import matplotlib.pyplot as plt
import pandas as pd
import io
import base64
import ast
# Function to process and visualize log probs
def visualize_logprobs(json_input):
try:
# Try to parse as JSON first
try:
data = json.loads(json_input)
except json.JSONDecodeError:
# If JSON fails, try to parse as Python literal (e.g., with single quotes)
data = ast.literal_eval(json_input)
# Extract tokens and log probs, skipping None values
tokens = [entry['token'] for entry in data['content'] if entry['logprob'] is not None]
logprobs = [entry['logprob'] for entry in data['content'] if entry['logprob'] is not None]
# Prepare data for the table
table_data = []
for entry in data['content']:
if entry['logprob'] is not None:
token = entry['token']
logprob = entry['logprob']
top_logprobs = entry['top_logprobs']
# Extract top 3 alternatives, sorted by log prob (most probable first)
top_3 = sorted(top_logprobs.items(), key=lambda x: x[1], reverse=True)[:3]
row = [token, f"{logprob:.4f}"]
for alt_token, alt_logprob in top_3:
row.append(f"{alt_token}: {alt_logprob:.4f}")
# Pad with empty strings if fewer than 3 alternatives
while len(row) < 5:
row.append("")
table_data.append(row)
# Create the plot
plt.figure(figsize=(10, 5))
plt.plot(range(len(logprobs)), logprobs, marker='o', linestyle='-', color='b')
plt.title("Log Probabilities of Generated Tokens")
plt.xlabel("Token Position")
plt.ylabel("Log Probability")
plt.grid(True)
plt.xticks(range(len(logprobs)), tokens, rotation=45, ha='right')
plt.tight_layout()
# Save plot to a bytes buffer
buf = io.BytesIO()
plt.savefig(buf, format='png', bbox_inches='tight')
buf.seek(0)
plt.close()
# Convert buffer to base64 for Gradio
img_bytes = buf.getvalue()
img_base64 = base64.b64encode(img_bytes).decode('utf-8')
img_html = f'<img src="data:image/png;base64,{img_base64}" style="max-width: 100%; height: auto;">'
# Create a DataFrame for the table
df = pd.DataFrame(
table_data,
columns=["Token", "Log Prob", "Top 1 Alternative", "Top 2 Alternative", "Top 3 Alternative"]
)
# Generate colored text based on log probabilities
# Normalize log probs to [0, 1] for color scaling (0 = most uncertain, 1 = most confident)
min_logprob = min(logprobs)
max_logprob = max(logprobs)
if max_logprob == min_logprob:
normalized_probs = [0.5] * len(logprobs) # Avoid division by zero
else:
normalized_probs = [(lp - min_logprob) / (max_logprob - min_logprob) for lp in logprobs]
# Create HTML for colored text
colored_text = ""
for i, (token, norm_prob) in enumerate(zip(tokens, normalized_probs)):
# Map normalized probability to RGB color (green for high confidence, red for low)
# Use a simple linear interpolation: green (0, 255, 0) to red (255, 0, 0)
r = int(255 * (1 - norm_prob)) # Red increases as uncertainty increases
g = int(255 * norm_prob) # Green decreases as uncertainty increases
b = 0 # Blue stays 0 for simplicity
color = f'rgb({r}, {g}, {b})'
colored_text += f'<span style="color: {color}; font-weight: bold;">{token}</span>'
if i < len(tokens) - 1:
colored_text += " " # Add space between tokens
colored_text_html = f'<p>{colored_text}</p>'
return img_html, df, colored_text_html
except Exception as e:
return f"Error: {str(e)}", None, None
# Gradio interface
with gr.Blocks(title="Log Probability Visualizer") as app:
gr.Markdown("# Log Probability Visualizer")
gr.Markdown("Paste your JSON or Python dictionary log prob data below to visualize the tokens and their probabilities.")
# Input
json_input = gr.Textbox(label="JSON Input", lines=10, placeholder="Paste your JSON or Python dict here...")
# Outputs
plot_output = gr.HTML(label="Log Probability Plot")
table_output = gr.Dataframe(label="Token Log Probabilities and Top Alternatives")
text_output = gr.HTML(label="Colored Text (Confidence Visualization)")
# Button to trigger visualization
btn = gr.Button("Visualize")
btn.click(
fn=visualize_logprobs,
inputs=json_input,
outputs=[plot_output, table_output, text_output]
)
# Launch the app
app.launch() |