Create app.py

app.py

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+import pandas as pd
+import gradio as gr
+from sklearn.preprocessing import StandardScaler
+from sklearn.svm import OneClassSVM
+
+# Function to detect theft and provide insights
+def detect_theft_from_file(uploaded_file):
+    # Determine the file type and read it
+    if uploaded_file.name.endswith('.csv'):
+        df = pd.read_csv(uploaded_file.name)
+    elif uploaded_file.name.endswith(('.xlsx', '.xls')):
+        df = pd.read_excel(uploaded_file.name)
+    else:
+        return "Unsupported file format. Please upload a CSV or Excel file."
+
+    # Ensure required format (Consumer ID + 12 months + optional Last Year)
+    if len(df.columns) < 13:
+        return "The file must have at least 13 columns: 'Consumer ID', 12 months (e.g., Jan-Dec), and optionally 'Last Year Avg'."
+
+    feature_columns = df.columns[1:13]  # 12 months
+    data = df[feature_columns].values
+
+    # Normalize the data
+    scaler = StandardScaler()
+    data_scaled = scaler.fit_transform(data)
+
+    # One-Class SVM model
+    model = OneClassSVM(nu=0.1, kernel='rbf', gamma='scale')
+    model.fit(data_scaled)
+
+    # Predict anomalies
+    predictions = model.predict(data_scaled)
+
+    # Add Reasons and Tips
+    reasons = []
+    tips = []
+    for i, pred in enumerate(predictions):
+        if pred == -1:  # Theft detected
+            current_pattern = data[i]
+            if 'Last Year Avg' in df.columns:
+                last_year_avg = df['Last Year Avg'].iloc[i]
+                if current_pattern.mean() < 0.5 * last_year_avg:
+                    reasons.append("Unusual drop compared to last year's average.")
+                elif current_pattern.mean() > 1.5 * last_year_avg:
+                    reasons.append("Unusual rise compared to last year's average.")
+                else:
+                    reasons.append("Irregular monthly pattern detected.")
+            else:
+                reasons.append("Irregular monthly pattern detected.")
+
+            tips.append(
+                "1. Inspect the meter physically for tampering.\n"
+                "2. Compare with neighborhood usage for similar consumption patterns.\n"
+                "3. Check if there are bypass connections or rewiring."
+            )
+        else:  # No theft detected
+            reasons.append("No irregularities detected.")
+            tips.append("No action required.")
+
+    # Results
+    df['Anomaly'] = ['Theft' if pred == -1 else 'No Theft' for pred in predictions]
+    df['Reason'] = reasons
+    df['Tips'] = tips
+    return df[['Consumer ID', 'Anomaly', 'Reason', 'Tips']]
+
+# Function for manual input detection
+def detect_theft_from_manual_input(consumer_id, *monthly_kwh):
+    # Create a DataFrame from manual input
+    df = pd.DataFrame(
+        {
+            'Consumer ID': [consumer_id],
+            **{f'Month {i+1}': [monthly_kwh[i]] for i in range(12)},
+        }
+    )
+
+    # Normalize the data
+    data = df.iloc[:, 1:].values  # Select monthly data
+    scaler = StandardScaler()
+    data_scaled = scaler.fit_transform(data)
+
+    # One-Class SVM model
+    model = OneClassSVM(nu=0.1, kernel='rbf', gamma='scale')
+    model.fit(data_scaled)
+
+    # Predict anomaly
+    prediction = model.predict(data_scaled)
+
+    # Add Reason and Tips
+    if prediction[0] == -1:  # Theft detected
+        reason = "Irregular monthly pattern detected."
+        tips = (
+            "1. Inspect the meter physically for tampering.\n"
+            "2. Compare with neighborhood usage for similar consumption patterns.\n"
+            "3. Check if there are bypass connections or rewiring."
+        )
+    else:
+        reason = "No irregularities detected."
+        tips = "No action required."
+
+    df['Anomaly'] = ['Theft' if prediction[0] == -1 else 'No Theft']
+    df['Reason'] = [reason]
+    df['Tips'] = [tips]
+    return df[['Consumer ID', 'Anomaly', 'Reason', 'Tips']]
+
+# Gradio Interface
+file_interface = gr.Interface(
+    fn=detect_theft_from_file,
+    inputs=gr.File(label="Upload Meter Data File (CSV, XLSX, XLS)"),
+    outputs=gr.Dataframe(label="Meter Theft Detection")
+)
+
+manual_input_interface = gr.Interface(
+    fn=detect_theft_from_manual_input,
+    inputs=[
+        gr.Textbox(label="Consumer ID", placeholder="Enter Consumer ID"),
+        *[gr.Number(label=f"Month {i+1} kWh") for i in range(12)],
+    ],
+    outputs=gr.Dataframe(label="Meter Theft Detection")
+)
+
+# Combine interfaces
+iface = gr.TabbedInterface(
+    interface_list=[file_interface, manual_input_interface],
+    tab_names=["Upload File", "Manual Input"]
+)
+
+# Launch the Gradio app
+iface.launch()