Update app.py

app.py

@@ -0,0 +1,70 @@
+import gradio as gr
+import pandas as pd
+import numpy as np
+from sklearn.ensemble import IsolationForest
+
+def generate_sample_data():
+    # Simulate 200 time points of energy consumption
+    rng = np.random.default_rng(seed=42)
+    normal_consumption = rng.normal(loc=100, scale=10, size=200)
+    
+    # Inject anomalies (potential theft) - unusually low or high consumption spikes
+    anomalies = rng.choice([50, 200], size=10)  # Very low or very high
+    anomaly_indices = rng.choice(range(200), size=10, replace=False)
+    normal_consumption[anomaly_indices] = anomalies
+    
+    df = pd.DataFrame({
+        'timestamp': pd.date_range(start='2025-01-01', periods=200, freq='H'),
+        'consumption': normal_consumption
+    })
+    return df
+
+def detect_energy_theft(file=None, use_sample=False):
+    if use_sample:
+        df = generate_sample_data()
+    else:
+        try:
+            df = pd.read_csv(file.name)
+        except Exception as e:
+            return f"Error reading file: {e}"
+
+    if 'consumption' not in df.columns:
+        return "CSV must have a 'consumption' column."
+
+    X = df[['consumption']].values
+
+    model = IsolationForest(contamination=0.05, random_state=42)
+    model.fit(X)
+
+    df['anomaly_score'] = model.decision_function(X)
+    df['anomaly'] = model.predict(X)
+    df['theft_detected'] = df['anomaly'] == -1
+
+    suspicious = df[df['theft_detected'] == True][['timestamp', 'consumption', 'anomaly_score']]
+    if suspicious.empty:
+        return "No energy theft anomalies detected."
+    else:
+        return suspicious
+
+# Gradio UI
+with gr.Blocks() as demo:
+    gr.Markdown("# Energy Theft Detection")
+    gr.Markdown("Upload CSV with 'consumption' column or use sample data to detect anomalies.")
+    
+    with gr.Row():
+        file_input = gr.File(label="Upload your CSV file", file_types=['.csv'])
+        sample_btn = gr.Button("Use Sample Data")
+
+    output = gr.Dataframe(headers=["timestamp", "consumption", "anomaly_score"], label="Suspicious Anomalies Detected")
+
+    def on_file_submit(file):
+        return detect_energy_theft(file=file, use_sample=False)
+
+    def on_sample_click():
+        return detect_energy_theft(use_sample=True)
+
+    file_input.change(on_file_submit, inputs=file_input, outputs=output)
+    sample_btn.click(on_sample_click, outputs=output)
+
+if __name__ == "__main__":
+    demo.launch()