Upload folder using huggingface_hub

Dockerfile

@@ -1,20 +1,34 @@
-FROM python:3.13.5-slim
+# Base image
+FROM python:3.9-slim
 
+# Environment settings
+ENV PYTHONDONTWRITEBYTECODE=1
+ENV PYTHONUNBUFFERED=1
+
+# Create non-root user
+RUN useradd -m -u 1000 user
+
+# Set working directory
 WORKDIR /app
 
-RUN apt-get update && apt-get install -y \
-    build-essential \
-    curl \
-    git \
-    && rm -rf /var/lib/apt/lists/*
+# Copy dependency file first (better caching)
+COPY requirements.txt .
+
+# Install dependencies
+RUN pip install --no-cache-dir -r requirements.txt
 
-COPY requirements.txt ./
-COPY src/ ./src/
+# Copy application code
+COPY --chown=user . /app
 
-RUN pip3 install -r requirements.txt
+# Switch to non-root user
+USER user
 
+# Expose Streamlit port
 EXPOSE 8501
 
-HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
+# Streamlit configuration
+ENV STREAMLIT_SERVER_PORT=8501
+ENV STREAMLIT_SERVER_ADDRESS=0.0.0.0
 
-ENTRYPOINT ["streamlit", "run", "src/streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0"]
+# Run Streamlit app
+CMD ["streamlit", "run", "engine_predict/deployment/app.py"]

app.py

@@ -0,0 +1,60 @@
+import streamlit as st
+import pandas as pd
+import joblib
+from huggingface_hub import hf_hub_download
+
+# ----------------------------
+# Load model from Hugging Face
+# ----------------------------
+HF_MODEL_REPO = "vihu21/adaboost-predictive-maintenance"
+MODEL_FILE = "adaboost_pm.joblib"
+
+model_path = hf_hub_download(
+    repo_id=HF_MODEL_REPO,
+    filename=MODEL_FILE,
+    repo_type="model"
+)
+
+model = joblib.load(model_path)
+
+# ----------------------------
+# Streamlit UI
+# ----------------------------
+st.title("🔧 Engine Condition Prediction")
+st.write("Fill in the engine parameters to predict engine condition.")
+
+# ----------------------------
+# User Inputs
+# ----------------------------
+engine_rpm = st.number_input("Engine RPM", min_value=50, max_value=6000, value=3000)
+lub_oil_pressure = st.number_input("Lub Oil Pressure", min_value=0.0, value=7.25)
+fuel_pressure = st.number_input("Fuel Pressure", min_value=0.0, value=21.4)
+coolant_pressure = st.number_input("Coolant Pressure", min_value=0.0, value=7.5)
+lub_oil_temp = st.number_input("Lub Oil Temperature", min_value=50.0, value=90.0)
+coolant_temp = st.number_input("Coolant Temperature", min_value=60.0, value=195.0)
+
+# ----------------------------
+# Prepare input data
+# ⚠️ Column names must EXACTLY match training
+# ----------------------------
+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
+}])
+
+# ----------------------------
+# Prediction
+# ----------------------------
+if st.button("Predict Engine Condition"):
+    prediction = model.predict(input_data)[0]
+
+    st.subheader("Prediction Result")
+
+    if prediction == 1 or prediction == "Good":
+        st.success("✅ Engine Condition: GOOD")
+    else:
+        st.error("⚠️ Engine Condition: BAD")

requirements.txt

@@ -1,3 +1,8 @@
-altair
-pandas
-streamlit
+pandas==2.2.2
+numpy==2.0.2
+scikit-learn==1.6.1
+xgboost==2.1.4
+joblib==1.4.2
+streamlit==1.43.2
+huggingface_hub==0.29.3
+mlflow