Upload folder using huggingface_hub

app.py

@@ -43,6 +43,57 @@ st.info("Enter the current readings from the engine sensors below:")
 # Creating columns for a cleaner layout
 col1, col2 = st.columns(2)
 
+with col1:
+    engine_rpm = st.number_input("Engine RPM", min_value=0, max_value=10000, value=700, step=10)
+    lub_oil_pressure = st.number_input("Lub Oil Pressure (bar)", min_value=0.0, max_value=20.0, value=2.5, format="%.4f")
+    fuel_pressure = st.number_input("Fuel Pressure (bar)", min_value=0.0, max_value=50.0, value=11.8, format="%.4f")
+
+%%writefile predictive_maintenance_project/deployment/app.py
+import streamlit as st
+import pandas as pd
+import joblib
+import os
+from huggingface_hub import hf_hub_download
+
+# ==========================================
+# 1. Page Configuration & Model Loading
+# ==========================================
+st.set_page_config(page_title="Engine Predictive Maintenance", layout="centered")
+
+# Configuration from previous training steps
+REPO_ID = "dpanchali/predictive_maintenance_model"
+FILENAME = "predictive_maintenance_model.joblib"
+
+@st.cache_resource
+def load_model():
+    """Download and load the model from Hugging Face Hub."""
+    try:
+        # Download the model file from the repository
+        model_path = hf_hub_download(repo_id=REPO_ID, filename=FILENAME)
+        model = joblib.load(model_path)
+        return model
+    except Exception as e:
+        st.error(f"Error loading model from Hugging Face: {e}")
+        return None
+
+# Load the model
+model = load_model()
+
+# ==========================================
+# 2. UI Layout
+# ==========================================
+st.title("Engine Condition Predictor")
+st.markdown("""
+This application uses a trained **XGBoost** model to predict the health of an engine
+based on real-time sensor data.
+""")
+
+st.header("Input Engine Sensor Data")
+st.info("Enter the current readings from the engine sensors below:")
+
+# Creating columns for a cleaner layout
+col1, col2 = st.columns(2)
+
 with col1:
     engine_rpm = st.number_input("Engine RPM", min_value=0, max_value=10000, value=700, step=10)
     lub_oil_pressure = st.number_input("Lub Oil Pressure (bar)", min_value=0.0, max_value=20.0, value=2.5, format="%.4f")
@@ -53,6 +104,9 @@ with col2:
     lub_oil_temp = st.number_input("Lub Oil Temp (°C)", min_value=0.0, max_value=200.0, value=84.1, format="%.4f")
     coolant_temp = st.number_input("Coolant Temp (°C)", min_value=0.0, max_value=200.0, value=81.6, format="%.4f")
 
+    load_index = engine_rpm * fuel_pressure / 100 
+    thermal_stress = coolant_temp - lub_oil_temp
+
 # ==========================================
 # 3. Prediction Logic
 # ==========================================
@@ -65,7 +119,9 @@ if st.button("Predict Engine Condition", type="primary"):
             'Fuel pressure': fuel_pressure,
             'Coolant pressure': coolant_pressure,
             'lub oil temp': lub_oil_temp,
-            'Coolant temp': coolant_temp
+            'Coolant temp': coolant_temp,
+            'load_index' : load_index,
+            'thermal_stress'  : thermal_stress
         }])
 
         # Perform prediction
@@ -90,6 +146,37 @@ if st.button("Predict Engine Condition", type="primary"):
     else:
         st.warning("Model is not loaded. Please check your Hugging Face credentials and Repo ID.")
 
+# ==========================================
+# 4. Footer
+# ==========================================
+st.sidebar.markdown("### Model Information")
+st.sidebar.text(f"Repo: {REPO_ID}")
+st.sidebar.text(f"File: {FILENAME}")
+st.sidebar.markdown("---")
+st.sidebar.write("This tool is intended for predictive maintenance scheduling based on sensor patterns.")
+
+        # Perform prediction
+        prediction = model.predict(input_data)[0]
+        prediction_proba = model.predict_proba(input_data)[0]
+
+        # Display results
+        st.divider()
+        st.subheader("Results")
+
+        if prediction == 1:
+            st.success("**Prediction: Engine is in Good Condition**")
+        else:
+            st.error("**Prediction: Maintenance Required (Potential Fault Detected)**")
+
+        # Display confidence scores
+        st.write(f"**Confidence Score:** {max(prediction_proba)*100:.2f}%")
+
+        # Optional: Display gauge or progress bar for health
+        health_score = prediction_proba[1] # Probability of class 1 (Good)
+        st.progress(health_score)
+    else:
+        st.warning("Model is not loaded. Please check your Hugging Face credentials and Repo ID.")
+
 # ==========================================
 # 4. Footer
 # ==========================================