Update app.py

app.py

@@ -0,0 +1,135 @@
+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)