Upload app.py with huggingface_hub

app.py

@@ -1,3 +1,54 @@
-# Your Gradio/Streamlit app here
-import gradio as gr
-gr.Interface(lambda x: x, "text", "text").launch()
+
+import streamlit as st
+import pandas as pd
+import joblib
+from huggingface_hub import hf_hub_download
+
+HF_USERNAME = "Vbhadiar"
+MODEL_ID = f"{HF_USERNAME}/engine-failure-classifier"
+
+@st.cache_resource
+def load_model():
+    model_path = hf_hub_download(
+        repo_id=MODEL_ID,
+        filename="best_model.pkl",
+        repo_type="model"
+    )
+    return joblib.load(model_path)
+
+model = load_model()
+
+st.title("🔧 Engine Predictive Maintenance")
+st.markdown("Predict whether an engine requires maintenance based on sensor readings.")
+
+col1, col2 = st.columns(2)
+
+with col1:
+    engine_rpm = st.number_input("Engine RPM", 0, 3000, 800)
+    lub_oil_pressure = st.number_input("Lub Oil Pressure (bar)", 0.0, 10.0, 3.5, 0.1)
+    fuel_pressure = st.number_input("Fuel Pressure (bar)", 0.0, 25.0, 7.0, 0.1)
+
+with col2:
+    coolant_pressure = st.number_input("Coolant Pressure (bar)", 0.0, 10.0, 2.5, 0.1)
+    lub_oil_temp = st.number_input("Lub Oil Temp (°C)", 50.0, 120.0, 78.0, 0.5)
+    coolant_temp = st.number_input("Coolant Temp (°C)", 50.0, 200.0, 80.0, 0.5)
+
+if st.button("Predict"):
+    df_input = 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],
+    })
+    pred = model.predict(df_input)[0]
+    prob = model.predict_proba(df_input)[0][1]
+
+    if pred == 1:
+        st.error(f"⚠️ Maintenance required (failure probability: {prob*100:.1f}%)")
+    else:
+        st.success(f"✅ Engine normal (failure probability: {prob*100:.1f}% ")
+
+    st.write("Input data:")
+    st.dataframe(df_input)