Upload folder using huggingface_hub

DockerFile

@@ -1,5 +1,5 @@
 
-# Use a Python base image
+# Use a standard Python image
 FROM python:3.9-slim
 
 # Set the working directory
@@ -12,9 +12,6 @@ RUN pip install --no-cache-dir -r requirements.txt
 # Copy the application script
 COPY app.py .
 
-# Expose the port Streamlit runs on
-EXPOSE 8501
-
-# CRITICAL FIX: Use CMD to combine the execution command and filename
-# This ensures 'streamlit run app.py' is executed upon container start.
+# Command to run the Streamlit app
+# This is the industry standard for HF Streamlit Docker deployments
 CMD ["streamlit", "run", "app.py"]

app.py

@@ -4,77 +4,98 @@ import pandas as pd
 import joblib
 from huggingface_hub import hf_hub_download
 
-
-MODEL_REPO_ID = "RajendrakumarPachaiappan/engine-predictive-model"
-MODEL_FILE = "final_random_forest_model.joblib" 
+# --- Configuration ---
+REPO_ID_MODEL = "RajendrakumarPachaiappan/engine-predictive-model"
+MODEL_FILE = "final_random_forest_model.joblib"
 SCALER_FILE = "standard_scaler.joblib"
 
-FEATURE_COLS = ['Engine rpm', 'Lub oil pressure', 'Fuel pressure', 
-                'Coolant pressure', 'lub oil temp', 'Coolant temp']
-
+# The feature columns must match the order expected by the scaler (validated against joblib file)
+FEATURE_COLS = ['Engine_RPM', 'Lub_Oil_Pressure', 'Fuel_Pressure',
+                'Coolant_Pressure', 'Lub_Oil_Temperature', 'Coolant_Temperature']
 
-@st.cache_resource
+# --- Resource Loading Function ---
+@st.cache_resource(show_spinner=False) # Suppress default spinner since we use custom status messages
 def load_model_and_scaler():
-    """Downloads and loads the model and scaler from Hugging Face Hub."""
-    st.info("Loading model and scaler from Hugging Face Hub...")
+    """
+    Downloads and loads the model and scaler from Hugging Face Hub.
+    Does NOT use st. commands inside to avoid initial warnings.
+    """
     try:
-        model_path = hf_hub_download(repo_id=MODEL_REPO_ID, filename=MODEL_FILE)
+        # Download files from Hugging Face
+        model_path = hf_hub_download(repo_id=REPO_ID_MODEL, filename=MODEL_FILE)
         model = joblib.load(model_path)
-        scaler_path = hf_hub_download(repo_id=MODEL_REPO_ID, filename=SCALER_FILE)
+        scaler_path = hf_hub_download(repo_id=REPO_ID_MODEL, filename=SCALER_FILE)
         scaler = joblib.load(scaler_path)
-        st.success("Artifacts loaded successfully!")
         return model, scaler
     except Exception as e:
-        st.error(f"Error loading artifacts from Hugging Face Hub: {e}")
-        return None, None
+        # Re-raise a descriptive exception for the main script to catch
+        raise Exception(f"Failed to load required artifacts: {e}")
 
-model, scaler = load_model_and_scaler()
+# --- Streamlit UI and Prediction Logic ---
 
-# Streamlit UI and Prediction Logic
 st.set_page_config(page_title="Predictive Maintenance", layout="wide")
-st.title("Engine Health Predictor")
-st.markdown("Use the sliders to simulate real-time sensor data and predict the **Engine Condition** (0=Healthy, 1=Faulty).")
+st.title("Engine Health Predictor ⚙️")
+
+# 1. Load Resources and Display Status
+st.info("Loading predictive model and scaler from Hugging Face Hub...")
+try:
+    # This call triggers the download/caching
+    model, scaler = load_model_and_scaler()
+    st.success("Artifacts loaded successfully! Ready for prediction.")
+
+    st.markdown("Use the sliders to simulate real-time sensor data and predict the **Engine Condition** (0=Healthy, 1=Faulty).")
 
+except Exception as e:
+    # Display error and halt execution if resources fail to load
+    st.error(f"🔴 Error loading resources: {e}")
+    st.stop()
+
+
+# 2. Input Sliders
+# Use columns for a cleaner layout
 col1, col2, col3 = st.columns(3)
 
 with col1:
-    
-    Engine_rpm = st.slider("Engine RPM (rev/min)", min_value=60, max_value=2300, value=791, step=10)
-    Lub_oil_pressure = st.slider("Lub Oil Pressure (bar)", min_value=0.0, max_value=7.3, value=3.3, step=0.1)
-    Fuel_pressure = st.slider("Fuel Pressure (bar)", min_value=0.0, max_value=22.0, value=6.7, step=0.1)
+    Engine_RPM = st.slider("Engine RPM (rev/min)", min_value=60, max_value=2300, value=791, step=10)
+    Lub_Oil_Pressure = st.slider("Lub Oil Pressure (bar)", min_value=0.0, max_value=7.3, value=3.3, step=0.1)
+    Fuel_Pressure = st.slider("Fuel Pressure (bar)", min_value=0.0, max_value=22.0, value=6.7, step=0.1)
 
 with col2:
+    Coolant_Pressure = st.slider("Coolant Pressure (bar)", min_value=0.0, max_value=7.5, value=2.3, step=0.1)
+    Lub_Oil_Temperature = st.slider("Lub Oil Temp (°C)", min_value=71.0, max_value=90.0, value=78.0, step=0.1)
+    Coolant_Temperature = st.slider("Coolant Temp (°C)", min_value=60.0, max_value=200.0, value=78.5, step=0.5)
 
-    Coolant_pressure = st.slider("Coolant Pressure (bar)", min_value=0.0, max_value=7.5, value=2.3, step=0.1)
-    Lub_oil_temp = st.slider("Lub Oil Temp (°C)", min_value=71.0, max_value=90.0, value=78.0, step=0.1)
-    Coolant_temp = st.slider("Coolant Temp (°C)", min_value=60.0, max_value=200.0, value=78.5, step=0.5)
-    
-# Prediction
+# 3. Prediction
 if st.button("Predict Engine Condition", type="primary"):
-    if model and scaler:
-        
-        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_temp], 
-            'Coolant temp': [Coolant_temp]
-        }, index=[0])
-        
-        
-        scaled_data = scaler.transform(input_data)
-        
-        prediction = model.predict(scaled_data)[0]
-        prediction_proba = model.predict_proba(scaled_data)[0]
-
-        # Display Results
-        st.subheader("Prediction Result:")
-        if prediction == 1:
-            st.error(f"**FAULTY (Requires Maintenance)**")
-            st.markdown(f"**Confidence (Faulty):** `{prediction_proba[1]*100:.2f}%`")
-            st.warning("**Actionable Insight:** The model predicts a high risk of failure. Schedule maintenance immediately.")
-        else:
-            st.success(f"**HEALTHY (Normal Operation)**")
-            st.markdown(f"**Confidence (Healthy):** `{prediction_proba[0]*100:.2f}%`")
-            st.info("Engine is operating within normal parameters. Continue monitoring.")
+
+    # a. Prepare Input
+    input_data = pd.DataFrame({
+        'Engine_RPM': [Engine_RPM],
+        'Lub_Oil_Pressure': [Lub_Oil_Pressure],
+        'Fuel_Pressure': [Fuel_Pressure],
+        'Coolant_Pressure': [Coolant_Pressure],
+        'Lub_Oil_Temperature': [Lub_Oil_Temperature],
+        'Coolant_Temperature': [Coolant_Temperature]
+    })
+
+    # b. Scale Input
+    # Important: Use FEATURE_COLS to ensure correct order for the scaler
+    input_scaled = scaler.transform(input_data[FEATURE_COLS])
+
+    # c. Make Prediction
+    prediction = model.predict(input_scaled)[0]
+    # Get the probability of the *faulty* class (1)
+    prediction_proba = model.predict_proba(input_scaled)[:, 1][0] 
+
+    # d. Display Results
+    st.divider()
+    st.subheader("Prediction Result:")
+    
+    if prediction == 1:
+        st.error(f"Engine Condition: **FAULTY** (Probability of Fault: {prediction_proba:.2f}) ⚠️")
+        st.write("Immediate maintenance is recommended to prevent breakdown.")
+    else:
+        st.success(f"Engine Condition: **HEALTHY** (Probability of Fault: {prediction_proba:.2f}) ✅")
+        st.write("Engine is operating normally. Continue regular monitoring.")
+
+    st.caption("Note: Probability of fault close to 0.5 indicates uncertainty.")

hosting.py

@@ -5,7 +5,7 @@ import os
 api = HfApi(token=os.getenv("HF_TOKEN"))
 api.upload_folder(
     folder_path="/content/Predictive_Maintenance_Project/deployment",
-   
+
     repo_id="RajendrakumarPachaiappan/EnginePredictionModel",
 
     repo_type="space",