Update app.py

app.py

@@ -24,8 +24,8 @@ import base64
 
 
 # Streamlit UI
-st.title('Sales Forecasting App')
-st.write("This app uses the SARIMAX model to predict future sales with exogenous variables (temp avg and humidity avg).")
+st.title('EIS Prediksi Demand Produk UMKM')
+st.write("UDINUS - ASPOO")
 
 #Read Dataset
 
@@ -50,9 +50,9 @@ last_date = target_train.index[-1]
 st.write(last_date)
 
 # Streamlit UI
-st.subheader("Predict sales for the next 7 days?")
+st.subheader("Memprediksi permintaan produk UMKM Anda 7 hari kedepan?")
 
-if st.button("Yes"):
+if st.button("Ya"):
     
     #Fungsi forecast
     @st.cache_resource
@@ -102,15 +102,15 @@ if st.button("Yes"):
     pred_SARIMAX_selected = selected_rows['pred_SARIMAX']
 
     # Show the predictions
-    st.subheader('Predicted Sales for the Next 7 Days')
+    st.subheader('Prediksi Permintaan Barang 7 Hari Kedepan')
     st.write(pred_SARIMAX_selected)
     
     # Plot the predictions
     plt.figure(figsize=(10, 5))
     plt.plot(pred_SARIMAX_selected.index, pred_SARIMAX_selected, label='Predicted Sales', marker='o')
-    plt.xlabel('Date')
-    plt.ylabel('Sales')
-    plt.title('Predicted Sales for the Next 7 Days')
+    plt.xlabel('Tanggal')
+    plt.ylabel('Penjualan')
+    plt.title('Prediksi Permintaan Barang 7 Hari Kedepan')
     plt.grid()
     plt.legend()
     st.pyplot(plt)
@@ -120,4 +120,4 @@ if st.button("Yes"):
     b64 = base64.b64encode(csv.encode()).decode()  # Konversi DataFrame ke base64
 
     # Show download button
-    st.markdown(f'<a href="data:file/csv;base64,{b64}" download="hasil_prediksi.csv">Download Prediction (CSV)</a>', unsafe_allow_html=True)
+    st.markdown(f'<a href="data:file/csv;base64,{b64}" download="hasil_prediksi.csv">Unduh Hasil Prediksi (CSV)</a>', unsafe_allow_html=True)