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import gradio as gr
import pandas as pd
import numpy as np
from momentfm import MOMENTPipeline
import matplotlib.pyplot as plt
from io import StringIO
# Initialize model
model = MOMENTPipeline.from_pretrained(
"AutonLab/MOMENT-1-large",
model_kwargs={"task_name": "reconstruction"},
)
model.init()
def detect_anomalies(data_input, threshold=0.1):
try:
# Read data
if isinstance(data_input, str):
df = pd.read_csv(StringIO(data_input))
else:
return "Error: Please provide CSV data"
# Validate columns
if 'timestamp' not in df.columns or 'value' not in df.columns:
return "Error: CSV must contain 'timestamp' and 'value' columns", None, None
# Convert timestamp and sort
df['timestamp'] = pd.to_datetime(df['timestamp'])
df = df.sort_values('timestamp')
# Get values as numpy array
values = df['value'].values.astype(float)
# Detect anomalies
reconstruction = model.reconstruct(values)
errors = np.abs(values - reconstruction)
# Apply threshold (using relative error)
threshold_value = threshold * np.max(errors)
df['anomaly_score'] = errors
df['is_anomaly'] = errors > threshold_value
# Create plot
fig, ax = plt.subplots(figsize=(10, 4))
ax.plot(df['timestamp'], df['value'], label='Value', color='blue')
ax.scatter(
df.loc[df['is_anomaly'], 'timestamp'],
df.loc[df['is_anomaly'], 'value'],
color='red', label='Anomaly'
)
ax.set_title('Sensor Data with Anomalies')
ax.legend()
# Prepare results
stats = {
"total_points": len(df),
"anomalies_detected": sum(df['is_anomaly']),
"max_anomaly_score": float(np.max(errors)),
"threshold_used": float(threshold_value)
}
return fig, stats, df.to_dict('records')
except Exception as e:
return f"Error: {str(e)}", None, None
# Gradio interface
with gr.Blocks() as demo:
gr.Markdown("## 🛠️ Equipment Anomaly Detection")
with gr.Row():
with gr.Column():
data_input = gr.Textbox(
label="Paste CSV data (timestamp,value)",
value="""timestamp,value
2025-04-01 00:00:00,100
2025-04-01 01:00:00,102
2025-04-01 02:00:00,98
2025-04-01 03:00:00,105
2025-04-01 04:00:00,103
2025-04-01 05:00:00,107
2025-04-01 06:00:00,200
2025-04-01 07:00:00,108
2025-04-01 08:00:00,110
2025-04-01 09:00:00,98
2025-04-01 10:00:00,99
2025-04-01 11:00:00,102
2025-04-01 12:00:00,101""",
lines=10
)
threshold = gr.Slider(0.01, 0.5, value=0.1, label="Anomaly Threshold")
submit_btn = gr.Button("Detect Anomalies")
with gr.Column():
plot_output = gr.Plot()
stats_output = gr.JSON(label="Statistics")
data_output = gr.JSON(label="Detailed Results")
submit_btn.click(
detect_anomalies,
inputs=[data_input, threshold],
outputs=[plot_output, stats_output, data_output]
)
demo.launch()
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