Rename app.py to streamlit_app.py for Streamlit loading

Dockerfile

@@ -1,20 +1,10 @@
-FROM python:3.13.5-slim
+FROM python:3.10-slim
 
 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 requirements.txt .
 RUN pip3 install -r requirements.txt
 
-EXPOSE 8501
-
-HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
+COPY . .
 
-ENTRYPOINT ["streamlit", "run", "src/streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0"]
+CMD ["streamlit", "run", "app.py", "--server.port=7860", "--server.address=0.0.0.0"]

requirements.txt

@@ -1,3 +1,7 @@
-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

streamlit_app.py

@@ -0,0 +1,57 @@
+import streamlit as st
+import pandas as pd
+import joblib
+from huggingface_hub import hf_hub_download
+
+# Hugging Face Model Repo
+MODEL_REPO = "Vignesh-vigu/PM-XGBoost-Model"
+MODEL_FILE = "best_xgb_model.joblib"
+
+# Download and Load Model
+@st.cache_resource
+def load_model():
+    model_path = hf_hub_download(
+        repo_id=MODEL_REPO,
+        filename=MODEL_FILE,
+        repo_type="model"
+    )
+    return joblib.load(model_path)
+
+model = load_model()
+
+# Page Config
+st.set_page_config(page_title="Engine Predictive Maintenance",
+                   page_icon="⚙️", layout="wide")
+
+st.title("🚗 Engine Predictive Maintenance System")
+st.write("Predict whether an engine requires maintenance using sensor data.")
+
+# Sidebar Inputs
+st.sidebar.header("📍 Engine Sensor Inputs")
+
+rpm = st.sidebar.number_input("Engine RPM", min_value=0, max_value=4000, value=750)
+oil_pressure = st.sidebar.number_input("Lub Oil Pressure (bar)", min_value=0.0, max_value=15.0, value=3.0)
+fuel_pressure = st.sidebar.number_input("Fuel Pressure (bar)", min_value=0.0, max_value=30.0, value=5.0)
+coolant_pressure = st.sidebar.number_input("Coolant Pressure (bar)", min_value=0.0, max_value=20.0, value=2.0)
+oil_temp = st.sidebar.number_input("Lub Oil Temperature (°C)", min_value=0.0, max_value=200.0, value=75.0)
+cool_temp = st.sidebar.number_input("Coolant Temperature (°C)", min_value=0.0, max_value=250.0, value=80.0)
+
+if st.sidebar.button("🔍 Predict Engine Condition"):
+    input_df = pd.DataFrame([[
+        rpm, oil_pressure, fuel_pressure,
+        coolant_pressure, oil_temp, cool_temp
+    ]], columns=[
+        "Engine rpm", "Lub oil pressure", "Fuel pressure",
+        "Coolant pressure", "lub oil temp", "Coolant temp"
+    ])
+
+    prediction = model.predict(input_df)[0]
+    status = ("⚠️ Faulty Engine — Maintenance Required!"
+              if prediction == 1
+              else "✅ Normal Engine — No Action Required")
+
+    st.subheader("🧾 Prediction Result:")
+    st.markdown(f"### {status}")
+
+    st.write("### 🔍 Input Data")
+    st.dataframe(input_df, use_container_width=True)