Upload folder using huggingface_hub

DockerFile

@@ -12,7 +12,7 @@ RUN pip install --no-cache-dir -r requirements.txt
 # Copy the application file
 COPY app.py .
 
-# Expose the port (Hugging Face Spaces default for Streamlit/Gradio is 8501 or 7860, but leaving it general is fine)
+# Expose the port
 EXPOSE 8501
 
 # Command to run the Streamlit application

README.md

@@ -1,5 +1,5 @@
 ---
-title: Engine Predictive Maintenance Model
+title: Predict Engine Condition
 colorFrom: blue
 colorTo: green
 sdk: streamlit

app.py

@@ -5,19 +5,18 @@ import pandas as pd
 import numpy as np
 from huggingface_hub import hf_hub_download
 
-# --- Hugging Face Model Repository Details ---
 REPO_ID_MODEL = "RajendrakumarPachaiappan/engine-predictive-model"
-# Assuming the best model found in the notebook was Random Forest based on your script
-MODEL_FILENAME = "final_random_forest_model.joblib" 
+
+MODEL_FILENAME = "final_random_forest_model.joblib"
 SCALER_FILENAME = "standard_scaler.joblib"
 
-# The six feature columns for input
+# Feature Columns for Input
 FEATURE_COLS = [
     'Engine_RPM', 'Lub_Oil_Pressure', 'Fuel_Pressure',
     'Coolant_Pressure', 'Lub_Oil_Temperature', 'Coolant_Temperature'
 ]
 
-# --- Caching Function to Load Model and Scaler ---
+# Caching Function to Load Model and Scaler
 @st.cache_resource
 def load_artifacts():
     """
@@ -42,34 +41,30 @@ def load_artifacts():
 # Load the model and scaler
 model, scaler = load_artifacts()
 
-# --- Streamlit UI Setup ---
+# Streamlit Setup
 st.set_page_config(
     page_title="Engine Predictive Maintenance",
     layout="centered",
     initial_sidebar_state="expanded"
 )
 
-st.title("🚗 Engine Predictive Maintenance Model")
+st.title("Predict Engine Condition")
 st.markdown(
     "Use the sliders below to input the current engine sensor readings and predict "
     "if the engine is running **NORMAL (0)** or requires **IMMEDIATE MAINTENANCE (1)**. "
     "This model uses a pre-trained Random Forest classifier."
 )
 st.markdown(
-    "**Note:** The slider ranges are based on the minimum and maximum values observed in the training dataset (19,535 records)."
+    "**Note:** The slider ranges are based on the minimum and maximum values observed in the training dataset"
 )
 
 if model is None or scaler is None:
-    st.stop() # Stop the app if artifacts failed to load
+    st.stop() 
 
-# --- Input Components ---
 st.header("Sensor Readings")
 
-
-# Define ranges based on the provided dataset statistics
-# Format: (min_value, max_value, mean_value/default) - all values are floats.
 ranges = {
-    'Engine_RPM': (61.0, 2239.0, 791.0), 
+    'Engine_RPM': (61.0, 2239.0, 791.0),
     'Lub_Oil_Pressure': (0.0, 7.3, 3.3),
     'Fuel_Pressure': (0.0, 21.1, 6.7),
     'Coolant_Pressure': (0.0, 7.5, 2.3),
@@ -78,17 +73,14 @@ ranges = {
 }
 
 input_values = {}
-# Use Streamlit columns for a cleaner, side-by-side layout
 col1, col2 = st.columns(2)
 columns = [col1, col2]
 
 for i, col_name in enumerate(FEATURE_COLS):
-    # Determine which column to place the widget in
-    current_col = columns[i % 2] 
+    current_col = columns[i % 2]
 
     min_val, max_val, default_val = ranges[col_name]
-    
-    # Clean up names for display in the UI and set units
+
     label = col_name.replace('_', ' ')
     unit = ""
     if "RPM" in col_name:
@@ -109,32 +101,24 @@ for i, col_name in enumerate(FEATURE_COLS):
             help=f"Current reading for {label}. Full data range: [{min_val}, {max_val}]"
         )
 
-# --- Prediction Logic ---
+# Prediction Logic
 if st.button("Predict Engine Condition", type="primary"):
-    # 1. Prepare data for the model
-    # Convert input_values dictionary to a DataFrame row
     input_df = pd.DataFrame([input_values], columns=FEATURE_COLS)
-
-    # 2. Scale the input data using the pre-trained StandardScaler
-    # The scaler must be fitted on the same data distribution as the model
     input_scaled = scaler.transform(input_df)
 
-    # 3. Make prediction
-    # The output is a numpy array, we take the first element (the prediction)
     prediction = model.predict(input_scaled)[0]
-    
-    # 4. Display results
+
     st.subheader("Prediction Result")
-    
+
     if prediction == 1:
         st.error(
-            "🔴 FAULTY (1): Immediate Maintenance Required! "
+            "FAULTY (1): Immediate Maintenance Required! "
             "High probability of engine failure detected. Check for high RPM, low pressures, or extreme temperatures."
         )
     else:
         st.success(
-            "🟢 NORMAL (0): Operating within expected parameters. "
+            "NORMAL (0): Operating within expected parameters. "
             "Engine health is currently good."
         )
-        
+
     st.caption(f"Raw Model Prediction (0=Normal, 1=Faulty): {prediction}")