Create app.py

app.py

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+import pickle
+import streamlit as stream
+import numpy as np
+import pandas as pd
+
+#importing the model
+with open("svm_model.pkl", 'rb') as model_file:
+    model = pickle.load(model_file)
+
+#importing the scaler model
+with open("scaler.pkl", 'rb') as scaler_file:
+    scaler = pickle.load(scaler_file)
+
+log_df = pd.DataFrame(columns=['Engine rpm', 'Lub oil pressure', 'Fuel pressure', 'Coolant pressure', 'lub oil temp', 'Coolant temp', 'Prediction'])
+
+def EngineHealth_predict(input_data, model, scaler, log_df):
+
+    columns = ['Engine rpm', 'Lub oil pressure', 'Fuel pressure', 'Coolant pressure', 'lub oil temp', 'Coolant temp']
+    input_df = pd.DataFrame([input_data], columns=columns)
+
+    input_array = np.array(input_df).reshape(1, -1)
+
+    input_scaled = scaler.transform(input_array)
+
+    prediction = model.predict(input_scaled)
+
+
+    if prediction[0] == 0:
+        health_status = "Engine is in Good Health!"
+    else:
+        health_status = "Engine is not in Good Health"
+
+    input_data.append(prediction[0])
+    log_df.loc[len(log_df)] = input_data
+
+    return health_status, log_df
+
+stream.title("Engine Health Prediction")
+
+engine_rpm = stream.number_input("Enter Engine rpm:", min_value=0.0, step=0.1)
+lub_oil_pressure = stream.number_input("Enter Lub oil pressure:", min_value=0.0, step=0.1)
+fuel_pressure = stream.number_input("Enter Fuel pressure:", min_value=0.0, step=0.1)
+coolant_pressure = stream.number_input("Enter Coolant pressure:", min_value=0.0, step=0.1)
+lub_oil_temp = stream.number_input("Enter Lub oil temp:", min_value=0.0, step=0.1)
+coolant_temp = stream.number_input("Enter Coolant temp:", min_value=0.0, step=0.1)
+
+
+if stream.button('Predict'):
+    input_data = [
+        engine_rpm,
+        lub_oil_pressure,
+        fuel_pressure,
+        coolant_pressure,
+        lub_oil_temp,
+        coolant_temp
+    ]
+    
+    health_status, log_df = EngineHealth_predict(input_data, model, scaler, log_df)
+    
+    stream.write(f"Prediction: {health_status}")
+    
+    # Display the log DataFrame
+    stream.write("Log of Predictions:")
+    stream.dataframe(log_df)