Upload folder using huggingface_hub

Dockerfile

@@ -1,20 +1,23 @@
-FROM python:3.13.5-slim
+# Use a minimal base image with Python 3.9 installed
+FROM python:3.9
 
+# Set the working directory inside the container to /app
 WORKDIR /app
 
-RUN apt-get update && apt-get install -y \
-    build-essential \
-    curl \
-    git \
-    && rm -rf /var/lib/apt/lists/*
-
-COPY requirements.txt ./
-COPY src/ ./src/
+# 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 8501
+RUN useradd -m -u 1000 user
+USER user
+ENV HOME=/home/user \
+	PATH=/home/user/.local/bin:$PATH
+
+WORKDIR $HOME/app
 
-HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
+COPY --chown=user . $HOME/app
 
-ENTRYPOINT ["streamlit", "run", "src/streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0"]
+# Define the command to run the Streamlit app on port "8501" and make it accessible externally
+CMD ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0", "--server.enableXsrfProtection=false"]

app.py

@@ -0,0 +1,60 @@
+import pandas as pd
+import streamlit as st
+import joblib
+from huggingface_hub import hf_hub_download
+
+st.set_page_config(page_title="Engine Predictive Maintenance", layout="centered")
+
+st.title("🔧 Engine Predictive Maintenance – Fault Prediction")
+st.write("Enter live engine sensor readings to predict whether the engine is **Normal (0)** or **Faulty (1)**.")
+
+# ---- CONFIG ----
+MODEL_REPO_ID = "cbendale10/Capstone-Predictive-Maintenance-model"  # <-- HF model repo
+MODEL_FILENAME = "best_predictive_maintenance_model_v1.joblib"  # best model uploaded
+
+@st.cache_resource
+def load_model():
+    model_path = hf_hub_download(repo_id=MODEL_REPO_ID, filename=MODEL_FILENAME)
+    return joblib.load(model_path)
+
+model = load_model()
+
+# ---- INPUTS ----
+st.subheader("Sensor Inputs")
+
+engine_rpm = st.number_input("Engine RPM", min_value=0.0, value=750.0, step=1.0)
+lub_oil_pressure = st.number_input("Lub Oil Pressure", min_value=0.0, value=3.1, step=0.01)
+fuel_pressure = st.number_input("Fuel Pressure", min_value=0.0, value=6.2, step=0.01)
+coolant_pressure = st.number_input("Coolant Pressure", min_value=0.0, value=2.1, step=0.01)
+lub_oil_temp = st.number_input("Lub Oil Temperature (°C)", min_value=0.0, value=76.8, step=0.01)
+coolant_temp = st.number_input("Coolant Temperature (°C)", min_value=0.0, value=78.3, step=0.01)
+
+# ---- DATAFRAME (required by rubric) ----
+input_df = 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
+}])
+
+st.write("### Input DataFrame")
+st.dataframe(input_df)
+
+if st.button("Predict Engine Condition"):
+    pred = model.predict(input_df)[0]
+    proba = None
+    if hasattr(model, "predict_proba"):
+        proba = model.predict_proba(input_df)[0, 1]
+
+    st.write("### Prediction Result")
+    if int(pred) == 1:
+        st.error("⚠️ Engine Condition: **Faulty (1)** — Maintenance Recommended")
+    else:
+        st.success("✅ Engine Condition: **Normal (0)** — No Maintenance Required")
+
+    if proba is not None:
+        st.info(f"Fault probability: **{proba:.2f}**")
+
+st.caption("Model loaded from Hugging Face Model Hub. Built for Capstone Predictive Maintenance.")

requirements.txt

@@ -1,3 +1,7 @@
-altair
-pandas
-streamlit
+pandas==2.2.2
+numpy==1.26.4
+scikit-learn==1.6.0
+joblib==1.5.1
+huggingface_hub==0.32.6
+streamlit==1.43.2
+mlflow==3.0.1