Initial Docker-based Streamlit deployment

Dockerfile

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+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

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----
-title: Engine Predictive Maintenance App
-emoji: ⚡
-colorFrom: yellow
-colorTo: purple
-sdk: docker
-pinned: false
----
+# Engine Predictive Maintenance App
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+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.

app.py

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+import streamlit as st
+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.title("🔧 Engine Predictive Maintenance System")
+st.write(
+    "Predict whether an engine requires maintenance "
+    "based on sensor inputs."
+)
+
+# 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 = 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)
+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)
+
+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
+}])
+
+if st.button("Predict Engine Condition"):
+    prediction = model.predict(input_df)[0]
+
+    if prediction == 1:
+        st.error("⚠️ Engine requires maintenance!")
+    else:
+        st.success("✅ Engine is operating normally.")

requirements.txt

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+streamlit==1.43.2
+pandas==2.2.2
+numpy==2.0.2
+scikit-learn==1.6.1
+xgboost==2.1.4
+joblib==1.4.2
+huggingface_hub>=0.34.0