Deploy Engine Condition Predictor

README.md

@@ -1,13 +1,9 @@
 ---
-title: Engine Condition Predictor
-emoji: 🔧
-colorFrom: blue
-colorTo: indigo
+title: Engine Condition App
+emoji: "🚧"
+colorFrom: indigo
+colorTo: blue
 sdk: docker
-app_file: streamlit_app.py
 pinned: false
 ---
-
-# Engine Condition Predictor
-
-MLOps pipeline for engine condition monitoring and predictive maintenance.
+This Space runs a Streamlit app that predicts engine condition from sensor data.

requirements.txt

@@ -1,6 +1,6 @@
 streamlit==1.39.0
+joblib==1.4.2
 pandas==2.2.2
 numpy==2.0.2
 scikit-learn==1.6.1
-joblib==1.4.2
 huggingface_hub==0.25.1

streamlit_app.py

@@ -8,12 +8,12 @@ import os
 
 # Configuration
 HF_MODEL_REPO = os.getenv("HF_MODEL_REPO", "dhani10/engine-maintenance-model")
-MODEL_FILE = os.getenv("MODEL_FILE", "best_model.pkl") 
+MODEL_FILE = os.getenv("MODEL_FILE", "best_model.pkl")
 
-# Expected features (match your training data exactly)
+# Expected features (match your training data exactly - should be snake_case)
 EXPECTED_COLS = [
-    'Engine rpm', 'Lub oil pressure', 'Fuel pressure',
-    'Coolant pressure', 'lub oil temp', 'Coolant temp'
+    'engine_rpm', 'lub_oil_pressure', 'fuel_pressure',
+    'coolant_pressure', 'lub_oil_temp', 'coolant_temp'
 ]
 
 @st.cache_resource
@@ -72,12 +72,12 @@ def main():
     if submitted:
         # Create input data with EXACT column names from training
         input_data = 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
+            '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
         }])
 
         try: