Upload folder using huggingface_hub

app.py

@@ -2,42 +2,39 @@ import streamlit as st
 import pandas as pd
 from huggingface_hub import hf_hub_download
 import joblib
+import os
 
-#Download the model from model hub
-model_path = hf_hub_download(repo_id="Shalyn/PredictiveMaintanence-model",filename="engine_condition_model_v1.joblib")
+st.title("Predictive Maintenance Prediction Tool")
 
-#loading the model
+# Load model
+model_path = hf_hub_download(
+    repo_id="Shalyn/PredictiveMaintanence-model",
+    filename="engine_condition_model_v1.joblib",
+    token=os.getenv("HF_TOKEN")
+)
 model = joblib.load(model_path)
 
-#Building Streamlit app UI
-st.title("Predictive Maintanence Prediction Tool")
-st.write("This tool helps to forecast potential failures in vehicles before they occur.")
-st.write("Kindly enter the vehicle sensor values to predict if the vehicle needs maintanence.")
+# User input
+Engine_RPM = st.number_input("Engine RPM", min_value=0)
+Lub_Oil_Pressure = st.number_input("Lub Oil Pressure")
+Fuel_Pressure = st.number_input("Fuel Pressure")
+Coolant_Pressure = st.number_input("Coolant Pressure")
+Lub_Oil_Temperature = st.number_input("Lub Oil Temperature")
+Coolant_Temperature = st.number_input("Coolant Temperature")
 
-#collect user input
-Engine_RPM = st.number_input("Engine_RPM(The number of revolutions per minute (RPM) of the engine, indicating engine speed. It is defined in Revolutions per Minute (RPM))")
-Lub_Oil_Pressure = st.number_input("Lub_Oil_Pressure (The pressure of the lubricating oil in the engine, essential for reducing friction and wear. It is defined in bar or kilopascals (kPa))")
-Fuel_Pressure = st.number_input("Fuel_Pressure (The pressure at which fuel is supplied to the engine, critical for proper combustion. It is defined in bar or kilopascals (kPa))")
-Coolant_Pressure = st.number_input("Coolant_Pressure (The pressure of the engine coolant, affecting engine temperature regulation. It is defined in bar or kilopascals (kPa) )")
-Lub_Oil_Temperature = st.number_input("Lub_Oil_Temperature (The temperature of the lubricating oil, which impacts viscosity and engine performance. It is defined in degrees Celsius (°C) )")
-Coolant_Temperature = st.number_input("Coolant_Temperature (The temperature of the engine coolant, crucial for preventing overheating. It is defined in degrees Celsius (°C))")
-
-#Converting the inputs to match training datarow
 input_data = pd.DataFrame([{
     'Engine rpm': Engine_RPM,
     'Lub oil pressure': Lub_Oil_Pressure,
     'Fuel pressure': Fuel_Pressure,
     'Coolant pressure': Coolant_Pressure,
-    'lub oil temp' : Lub_Oil_Temperature,
+    'lub oil temp': Lub_Oil_Temperature,
     'Coolant temp': Coolant_Temperature
 }])
 
-#setting up classification threshold
+# Prediction
 classification_threshold = 0.45
-
-#Creating prediction button
 if st.button("Predict"):
-  prediction_prob = model.predict_proba(input_data)[0,1]
-  prediction = (prediction_prob>classification_threshold).astype(int)
-  result = "Off/False/Active" if prediction == 0 else "On/True/Faulty"
-  st.write(f"Based on the given input the vehicle is {result}")
+    prediction_prob = model.predict_proba(input_data)[0,1]
+    prediction = int(prediction_prob > classification_threshold)
+    result = "Off/False/Active" if prediction == 0 else "On/True/Faulty"
+    st.write(f"Vehicle status: **{result}**")