app.py

import gradio as gr
import numpy as np
import pandas as pd
from momentfm import MOMENTPipeline
import matplotlib.pyplot as plt
from io import StringIO

# Initialize the MOMENT model
model = MOMENTPipeline.from_pretrained(
    "AutonLab/MOMENT-1-large",
    model_kwargs={"task_name": "reconstruction"},
)
model.init()

def detect_anomalies(data_input, threshold=0.05):
    """
    Process time-series data and detect anomalies using MOMENT model
    """
    try:
        # Handle different input types
        if isinstance(data_input, str):
            # Try to read as CSV
            try:
                df = pd.read_csv(StringIO(data_input))
            except:
                # Try to read as JSON
                try:
                    df = pd.read_json(StringIO(data_input))
                except:
                    return "Error: Could not parse input data. Please provide valid CSV or JSON."
        elif isinstance(data_input, dict):
            df = pd.DataFrame(data_input)
        else:
            return "Error: Unsupported input format"
        
        # Check for required columns
        if 'timestamp' not in df.columns or 'value' not in df.columns:
            return "Error: Data must contain 'timestamp' and 'value' columns"
        
        # Convert timestamp to datetime if needed
        df['timestamp'] = pd.to_datetime(df['timestamp'])
        df = df.sort_values('timestamp')
        
        # Prepare data for MOMENT model
        time_series = df['value'].values.astype(float)
        
        # Get reconstruction from the model
        reconstruction = model.reconstruct(time_series)
        
        # Calculate reconstruction error
        error = np.abs(time_series - reconstruction)
        
        # Detect anomalies based on threshold
        df['anomaly_score'] = error
        df['is_anomaly'] = error > threshold * np.max(error)
        
        # Create plot
        fig, ax = plt.subplots(figsize=(12, 6))
        ax.plot(df['timestamp'], df['value'], label='Original', color='blue')
        ax.scatter(
            df[df['is_anomaly']]['timestamp'], 
            df[df['is_anomaly']]['value'], 
            color='red', 
            label='Anomaly'
        )
        ax.set_title('Time Series with Anomalies Detected')
        ax.set_xlabel('Timestamp')
        ax.set_ylabel('Value')
        ax.legend()
        ax.grid(True)
        
        # Prepare results
        anomalies = df[df['is_anomaly']]
        stats = {
            "total_points": len(df),
            "anomalies_detected": len(anomalies),
            "anomaly_percentage": f"{100 * len(anomalies)/len(df):.2f}%",
            "max_anomaly_score": np.max(error),
            "threshold_used": threshold
        }
        
        return fig, stats, df.to_dict(orient='records')
    
    except Exception as e:
        return f"Error processing data: {str(e)}"

# Create Gradio interface
with gr.Blocks(title="Equipment Anomaly Detection") as demo:
    gr.Markdown("# 🛠️ Equipment Sensor Anomaly Detection")
    gr.Markdown("""
    **Detect anomalies in equipment sensor data using the MOMENT-1-large model**
    - Upload CSV/JSON data with 'timestamp' and 'value' columns
    - Adjust the sensitivity threshold as needed
    - Get visual and statistical results
    """)
    
    with gr.Row():
        with gr.Column():
            input_data = gr.Textbox(
                label="Paste your time-series data (CSV/JSON)",
                placeholder="timestamp,value\n2023-01-01,1.2\n2023-01-02,1.5...",
                lines=5
            )
            file_upload = gr.File(label="Or upload a file")
            threshold = gr.Slider(
                minimum=0.01,
                maximum=0.2,
                value=0.05,
                step=0.01,
                label="Anomaly Detection Sensitivity (lower = more sensitive)"
            )
            submit_btn = gr.Button("Detect Anomalies", variant="primary")
        
        with gr.Column():
            plot_output = gr.Plot(label="Anomaly Detection Results")
            stats_output = gr.JSON(label="Detection Statistics")
            data_output = gr.JSON(label="Processed Data with Anomaly Scores")
    
    # Handle file upload
    def process_file(file):
        if file:
            with open(file.name, 'r') as f:
                return f.read()
        return ""
    
    file_upload.change(process_file, inputs=file_upload, outputs=input_data)
    
    submit_btn.click(
        detect_anomalies,
        inputs=[input_data, threshold],
        outputs=[plot_output, stats_output, data_output]
    )

if __name__ == "__main__":
    demo.launch(server_name="0.0.0.0", server_port=7860)