Upload folder using huggingface_hub

Dockerfile

@@ -0,0 +1,27 @@
+# Use a minimal base image with Python 3.12 installed
+FROM python:3.12-slim
+
+# Set the working directory inside the container to /app
+WORKDIR /app
+
+# Copy all files from the current directory on the host to the container's /app directory
+COPY . .
+
+# Install Python dependencies listed in requirements.txt
+RUN pip3 install -r requirements.txt
+
+# Expose port (HuggingFace Spaces expects 7860)
+EXPOSE 7860
+
+# Create a non-root user with a home dir
+RUN useradd -m app && chown -R app:app /app
+
+# Switch to that user
+USER app
+
+# Point Streamlit config to the new user's home (writable)
+ENV HOME=/home/app
+ENV STREAMLIT_CONFIG_DIR=/home/app/.streamlit
+
+# Define the command to run the Streamlit app on port 7860 and make it accessible externally
+CMD ["streamlit", "run", "app.py", "--server.port=7860", "--server.address=0.0.0.0", "--server.enableXsrfProtection=false"]

README.md

@@ -1,10 +1,13 @@
 ---
 title: Predictive Maintenance App
-emoji: 🌍
-colorFrom: pink
-colorTo: green
+emoji: 🔧
+colorFrom: red
+colorTo: yellow
 sdk: docker
+app_port: 7860
 pinned: false
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Predictive Maintenance Prediction App
+
+Predict whether an engine requires maintenance based on sensor readings.

app.py

@@ -0,0 +1,128 @@
+import streamlit as st
+import pandas as pd
+from huggingface_hub import hf_hub_download
+import joblib
+import os
+
+# ---------- Page Config ----------
+st.set_page_config(
+    page_title="Predictive Maintenance",
+    page_icon="🔧",
+    layout="wide",
+    initial_sidebar_state="collapsed",
+)
+
+# ---------- Custom CSS ----------
+st.markdown(
+    """
+    <style>
+    .block-container {
+        padding-top: 2rem;
+    }
+    div.stButton > button {
+        background-color: #007bff;
+        color: white;
+        border: none;
+        padding: 0.75rem 3rem;
+        border-radius: 0.5rem;
+        cursor: pointer;
+    }
+    div.stButton > button:hover {
+        background-color: #0069d9;
+    }
+    </style>
+    """,
+    unsafe_allow_html=True
+)
+
+# ---------- Helper Functions ----------
+def slugify(name: str) -> str:
+    """Convert name to HF-compatible slug (underscores to hyphens)."""
+    return name.replace("_", "-")
+
+# ---------- Load Model (full pipeline with preprocessor) ----------
+hf_username = os.getenv("HF_USERNAME")
+hf_model_name = slugify(os.getenv("HF_MODEL_NAME", "predictive-maintenance-model"))
+
+model_path = hf_hub_download(
+    repo_id=f"{hf_username}/{hf_model_name}",
+    filename="best_engine_maintenance_model.joblib"
+)
+model = joblib.load(model_path)
+
+# Production threshold for maintenance classification
+THRESHOLD = 0.4
+
+# ---------- App Header ----------
+st.title("🔧 Engine Predictive Maintenance")
+st.write("Predict whether an engine requires maintenance based on sensor readings.")
+
+st.markdown("---")
+
+# ---------- Sensor Readings ----------
+st.subheader("📊 Sensor Readings")
+
+col1, col2, col3 = st.columns(3, gap="medium")
+
+# Column 1 - Engine Performance
+with col1:
+    st.markdown("**Engine Performance**")
+    engine_rpm = st.number_input("Engine RPM", min_value=0, max_value=3000, value=1500)
+    fuel_pressure = st.number_input("Fuel Pressure (bar)", min_value=0.0, max_value=25.0, value=6.5, step=0.1)
+
+# Column 2 - Lubrication System
+with col2:
+    st.markdown("**Lubrication System**")
+    lub_oil_pressure = st.number_input("Lub Oil Pressure (bar)", min_value=0.0, max_value=10.0, value=3.5, step=0.1)
+    lub_oil_temp = st.number_input("Lub Oil Temperature (°C)", min_value=0.0, max_value=150.0, value=85.0, step=1.0)
+
+# Column 3 - Cooling System
+with col3:
+    st.markdown("**Cooling System**")
+    coolant_pressure = st.number_input("Coolant Pressure (bar)", min_value=0.0, max_value=10.0, value=2.0, step=0.1)
+    coolant_temp = st.number_input("Coolant Temperature (°C)", min_value=0.0, max_value=200.0, value=90.0, step=1.0)
+
+st.markdown("---")
+
+# ---------- Derived Features (calculated automatically) ----------
+rpm_x_fuel_pressure = engine_rpm * fuel_pressure
+rpm_bins = 0 if engine_rpm < 300 else (1 if engine_rpm <= 1500 else 2)
+oil_health_index = lub_oil_pressure / lub_oil_temp if lub_oil_temp > 0 else 0
+
+# ---------- Create Input DataFrame ----------
+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,
+    'rpm_x_fuel_pressure': rpm_x_fuel_pressure,
+    'rpm_bins': rpm_bins,
+    'oil_health_index': oil_health_index
+}])
+
+# ---------- Preview & Predict ----------
+st.subheader("📦 Feature Preview")
+with st.expander("Click to expand (includes derived features)", expanded=False):
+    cols = st.columns(3)
+    for i, (field, value) in enumerate(input_data.iloc[0].items()):
+        with cols[i % 3]:
+            display_value = f"{value:.4f}" if isinstance(value, float) else value
+            st.metric(label=field, value=display_value)
+
+if st.button("Predict Maintenance Need"):
+    # Pipeline handles preprocessing (StandardScaler)
+    probability = model.predict_proba(input_data)[0, 1]
+    prediction = 1 if probability >= THRESHOLD else 0
+
+    st.markdown("---")
+    st.subheader("Prediction Result")
+
+    if prediction == 1:
+        st.error(f"⚠️ **Maintenance Required** (Probability: {probability:.2%})")
+        st.write("The engine shows signs of degradation. Schedule maintenance soon.")
+    else:
+        st.success(f"✅ **Normal Operation** (Probability of failure: {probability:.2%})")
+        st.write("The engine is operating within normal parameters.")
+        st.balloons()

requirements.txt

@@ -0,0 +1,5 @@
+pandas==2.2.3
+huggingface_hub==0.27.1
+streamlit==1.41.1
+joblib==1.4.2
+scikit-learn==1.6.0