Upload app.py with huggingface_hub

app.py

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+
+import streamlit as st
+import pandas as pd
+import joblib
+from huggingface_hub import hf_hub_download
+
+MODEL_REPO_ID = "Amittripipathi/DecisionTree-engine-predictive-model"
+MODEL_FILENAME = "DecisionTree_engine_model.pkl"
+
+# Download model from HF Model Hub & load
+model_path = hf_hub_download(repo_id=MODEL_REPO_ID, filename=MODEL_FILENAME)
+model = joblib.load(model_path)
+
+# Streamlit UI
+st.title("🚗 Engine Failure Prediction")
+st.write("Predict whether an engine is faulty or operating normally based on sensor readings.")
+
+# Input form
+engine_rpm = st.number_input("Engine RPM", min_value=0, max_value=3000, value=750)
+lub_oil_pressure = st.number_input("Lubricating Oil Pressure (MPa)", min_value=0.0, max_value=10.0, value=3.0)
+fuel_pressure = st.number_input("Fuel Pressure (MPa)", min_value=0.0, max_value=30.0, value=6.0)
+coolant_pressure = st.number_input("Coolant Pressure (MPa)", min_value=0.0, max_value=10.0, value=2.0)
+lub_oil_temp = st.number_input("Lubricating Oil Temperature (°C)", min_value=0.0, max_value=200.0, value=78.0)
+coolant_temp = st.number_input("Coolant Temperature (°C)", min_value=0.0, max_value=200.0, value=78.0)
+
+if st.button("Predict Engine Condition"):
+    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
+    }])
+
+    prediction = model.predict(input_df)[0]
+    result = "⚠️ Faulty Engine" if prediction == 1 else "✅ Normal Engine"
+    st.subheader(result)