Update app.py

app.py

@@ -5,6 +5,7 @@ from tensorflow.keras.models import load_model
 
 # Load the model
 model = load_model("Engine_Fault-small.h5")
+scaler = joblib.load("scaler.pkl")
 
 # Fault Type Table
 fault_table = pd.DataFrame({
@@ -24,38 +25,45 @@ required_columns = [
     "Consumption L/100KM", "Speed", "CO", "HC", "CO2", "O2", "Lambda", "AFR"
 ]
 
-def predict_fault(input_text, uploaded_csv):
-    if uploaded_csv is not None:
-        df = pd.read_csv(uploaded_csv)
-    else:
-        # Simulate the case where the text input would describe the dataset
-        return "Please upload a CSV file for prediction. Input text is only for description."
+def predict_fault(uploaded_csv):
+    if uploaded_csv is None:
+        return "Please upload a CSV file for prediction.", None
+
+    df = pd.read_csv(uploaded_csv)
 
     missing_cols = set(required_columns) - set(df.columns)
     if missing_cols:
-        return f"Missing required columns: {', '.join(missing_cols)}"
+        return f"Missing required columns: {', '.join(missing_cols)}", None
 
-    # Predict
+    # Apply scaler
     X = df[required_columns]
-    predictions = model.predict(X)
+    X_scaled = scaler.transform(X)
+
+    # Predict
+    predictions = model.predict(X_scaled)
     predicted_faults = np.argmax(predictions, axis=1)
 
-    # Attach fault description
+    # Attach fault descriptions
     df['Predicted Fault Type'] = predicted_faults
     df = df.merge(fault_table, left_on='Predicted Fault Type', right_on='Fault Type', how='left')
-    return df[['Predicted Fault Type', 'Fault Name', 'Conditions']].head(10)
+
+    # Create boxplot
+    fig, ax = plt.subplots(figsize=(12, 5))
+    df[required_columns].boxplot(ax=ax, rot=90)
+    plt.tight_layout()
+
+    return df[['Predicted Fault Type', 'Fault Name', 'Conditions']].head(10), fig
 
 # Gradio Interface
 demo = gr.Interface(
     fn=predict_fault,
     inputs=[
-        gr.Textbox(label="Dataset Description (Optional)", placeholder="Describe the dataset here..."),
         gr.File(label="Upload CSV File", file_types=[".csv"])
     ],
     outputs=[
-        gr.Dataframe(label="Predicted Faults (Top 10 Rows)")
+        gr.Dataframe(label="Predicted Faults (Top 10 Rows)"),
+        gr.Plot(label="Sensor Data Boxplot")
     ],
-    examples=[["This dataset consists of...", "sample.csv"]],
     title="Engine Fault Prediction System",
     description=(
         "Upload a CSV file containing engine sensor data to predict engine fault types.\n\n"