CI/CD: auto-deploy Streamlit app

Dockerfile

@@ -3,11 +3,9 @@ FROM python:3.10-slim
 WORKDIR /app
 
 COPY requirements.txt .
-
 RUN pip install --no-cache-dir -r requirements.txt
 
 COPY app.py .
 
 EXPOSE 7860
-
 CMD ["streamlit", "run", "app.py", "--server.port=7860", "--server.address=0.0.0.0"]

README.md

@@ -11,10 +11,4 @@ pinned: false
 
 # Engine Predictive Maintenance App
 
-This Streamlit application predicts whether an engine requires maintenance
-based on real-time sensor inputs such as RPM, pressure, and temperature.
-
-The machine learning model is trained using XGBoost and hosted on the
-Hugging Face Model Hub. The application is deployed using Docker on
-Hugging Face Spaces.
-
+Docker-based Streamlit app deployed using Hugging Face Spaces.

app.py

@@ -3,36 +3,22 @@ import pandas as pd
 import joblib
 from huggingface_hub import hf_hub_download
 
-st.set_page_config(
-    page_title="Engine Predictive Maintenance",
-    layout="centered"
-)
+st.set_page_config(page_title="Engine Predictive Maintenance")
 
-st.title("🔧 Engine Predictive Maintenance System")
-st.write(
-    "Predict whether an engine requires maintenance "
-    "based on sensor inputs."
-)
+st.title("🔧 Engine Predictive Maintenance")
 
-# Load model from Hugging Face Model Hub
 MODEL_REPO = "Vignesh-vigu/engine-predictive-maintenance-model"
 MODEL_FILE = "xgboost_model.pkl"
 
-model_path = hf_hub_download(
-    repo_id=MODEL_REPO,
-    filename=MODEL_FILE
-)
-
+model_path = hf_hub_download(repo_id=MODEL_REPO, filename=MODEL_FILE)
 model = joblib.load(model_path)
 
-st.subheader("Enter Engine Sensor Values")
-
-engine_rpm = st.number_input("Engine RPM", min_value=0, max_value=3000, value=1000)
+engine_rpm = st.number_input("Engine RPM", 0, 3000, 1000)
 lub_oil_pressure = st.number_input("Lub Oil Pressure", value=3.0)
 fuel_pressure = st.number_input("Fuel Pressure", value=10.0)
 coolant_pressure = st.number_input("Coolant Pressure", value=2.5)
-lub_oil_temp = st.number_input("Lub Oil Temperature (°C)", value=80.0)
-coolant_temp = st.number_input("Coolant Temperature (°C)", value=85.0)
+lub_oil_temp = st.number_input("Lub Oil Temperature", value=80.0)
+coolant_temp = st.number_input("Coolant Temperature", value=85.0)
 
 input_df = pd.DataFrame([{
     "engine_rpm": engine_rpm,
@@ -43,10 +29,9 @@ input_df = pd.DataFrame([{
     "coolant_temp": coolant_temp
 }])
 
-if st.button("Predict Engine Condition"):
-    prediction = model.predict(input_df)[0]
-
-    if prediction == 1:
-        st.error("⚠️ Engine requires maintenance!")
+if st.button("Predict"):
+    pred = model.predict(input_df)[0]
+    if pred == 1:
+        st.error("⚠️ Engine needs maintenance")
     else:
-        st.success("✅ Engine is operating normally.")
+        st.success("✅ Engine is healthy")

requirements.txt

@@ -6,3 +6,4 @@ xgboost==2.1.4
 joblib==1.4.2
 dill==0.3.8
 huggingface_hub>=0.34.0
+