Upload folder using huggingface_hub

app.py

@@ -17,6 +17,8 @@ model = load_model()
 
 st.title("Engine Condition Prediction")
 
+# Single row prediction
+
 #engine_rpm = st.number_input("Engine rpm", min_value=0, step=100)
 #lub_oil_pressure = st.number_input("Lub oil pressure", format="%.3f")
 #fuel_pressure = st.number_input("Fuel pressure", format="%.3f")
@@ -104,3 +106,27 @@ if st.button("Predict"):
 
     st.success(f"Engine Condition: {label}")
 
+
+# Batch prediction
+st.header("Batch Prediction from CSV")
+uploaded_file = st.file_uploader("Upload CSV file for batch prediction", type="csv")
+
+if uploaded_file is not None:
+    input_data = pd.read_csv(uploaded_file)
+    required_cols = ["Engine rpm","Lub oil pressure","Fuel pressure","Coolant pressure","lub oil temp","Coolant temp"]
+    
+    if not all(col in input_data.columns for col in required_cols):
+        st.error(f"CSV must contain columns: {', '.join(required_cols)}")
+    else:
+        predictions = model.predict(input_data)
+        input_data["Engine Condition"] = ["FAULTY" if p == 1 else "NORMAL" for p in predictions]
+        st.success("Predictions completed!")
+        st.dataframe(input_data)
+        
+        csv = input_data.to_csv(index=False).encode("utf-8")
+        st.download_button(
+            label="Download Predictions as CSV",
+            data=csv,
+            file_name="engine_predictions.csv",
+            mime="text/csv",
+        )