Upload app.py with huggingface_hub

app.py

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+import streamlit as st
+import pandas as pd
+from huggingface_hub import hf_hub_download
+import joblib
+
+# Download and load the trained model
+model_path = hf_hub_download(repo_id="vihu21/predictive_maintenance", filename="best_ada_model.joblib")
+model = joblib.load(model_path)
+
+# Streamlit UI
+st.title("Engine Condition Prediction")
+st.write("""
+Fill the engine details below to predict if engine condition good or bad
+""")
+
+
+
+# User input
+Engine_Details = st.number_input("Engine_Details Size (MB)", min_value=1.0, max_value=4000.0, value=50.0, step=0.1)
+EngineRpm = st.number_input("Engine rpm", min_value=50, value=3000.0)
+LubOilPressure = st.number_input("Lub oil pressure", min_value=0, value=7.25)
+FuelPressure = st.number_input("Fuel pressure", min_value=0, value=21.4)
+CoolantPressure = st.number_input("Coolant pressure", min_value=0, value=7.5)
+lubOilTemp = st.number_input("lub oil temp", min_value=70, value=90.0)
+CoolantTemp = st.number_input("Coolant temp", min_value=60, value=195.0)
+
+# ----------------------------
+# Prepare input data
+# ----------------------------
+input_data = pd.DataFrame([{
+    'Engine rpm': EngineRpm,
+    'Lub oil pressure': LubOilPressure,
+    'Fuel pressure': FuelPressure,
+    'Coolant pressure': CoolantPressure,
+    'lub oil temp': lubOilTemp,
+    'Coolant temp': CoolantTemp
+}])
+
+
+
+# Predict button
+if st.button("Predict Engine"):
+    prediction = model.predict(input_data)[0]
+    st.subheader("Prediction Result:")
+    st.success(f"Estimated EngineCondition: **${prediction:,.2f} ")