Update src/streamlit_app.py

src/streamlit_app.py

@@ -45,6 +45,7 @@ def get_active_cycle():
     c.execute("SELECT id, start_date FROM cycles WHERE active = 1 ORDER BY id DESC LIMIT 1")
     return c.fetchone()
 
+# (Fungsi ini tetap disimpan sebagai cadangan/helper, meski logika utamanya sekarang menggunakan Pandas)
 def get_rolling_30(cycle_id, current_temp):
     c = db.cursor()
     c.execute("SELECT raw_temp FROM daily_logs WHERE cycle_id = ? ORDER BY log_date DESC LIMIT 29", (cycle_id,))
@@ -52,15 +53,12 @@ def get_rolling_30(cycle_id, current_temp):
     past_temps = []
     for row in c.fetchall():
         try:
-            # Pastikan data tidak None/kosong dan paksa menjadi angka (float)
             if row[0] is not None:
                 past_temps.append(float(row[0]))
         except ValueError:
-            # Jika ada data yang corrupt (misal terisi huruf/simbol), abaikan
             pass
+            
     all_temps = past_temps + [float(current_temp)]
-    
-    # Hitung rata-rata, cegah error jika list kosong
     if len(all_temps) == 0:
         return 0.0
         
@@ -77,25 +75,48 @@ with st.sidebar:
     if active_cycle:
         st.success(f"Siklus Aktif: {active_cycle[1]}")
         
-        # --- FITUR IMPORT CSV ---
+        # --- FITUR IMPORT CSV (Versi Diperbarui) ---
         st.markdown("---")
         st.subheader("📤 Bulk Import Data Historis")
-        st.caption("Gunakan CSV dengan kolom: 'Tanggal' (YYYY-MM-DD) dan 'Suhu'")
+        st.caption("Gunakan CSV dengan kolom: 'Tanggal' dan 'Bodi Tengah (°C)'")
         uploaded_file = st.file_uploader("Pilih file CSV", type="csv")
         
         if uploaded_file is not None:
             try:
-                data_import = pd.read_csv(uploaded_file)
+                # 1. Melewati baris judul (skiprows=1) & Penanganan Encoding
+                try:
+                    data_import = pd.read_csv(uploaded_file, encoding='utf-8-sig', skiprows=1)
+                except UnicodeDecodeError:
+                    uploaded_file.seek(0)
+                    data_import = pd.read_csv(uploaded_file, encoding='latin1', skiprows=1)
+
+                # 2. Cek apakah pemisahnya ternyata titik koma (;)
+                if len(data_import.columns) == 1:
+                    uploaded_file.seek(0)
+                    data_import = pd.read_csv(uploaded_file, encoding='utf-8-sig', sep=';', skiprows=1)
+                    
+                # 3. Bersihkan nama kolom dari spasi
+                data_import.columns = data_import.columns.str.strip()
+
+                # 4. Samakan nama kolom di CSV ("Bodi Tengah (°C)") ke "Suhu"
+                if 'Bodi Tengah (°C)' in data_import.columns:
+                    data_import.rename(columns={'Bodi Tengah (°C)': 'Suhu'}, inplace=True)
+
+                st.info("Preview Data yang akan diimpor:")
+                st.dataframe(data_import[['Tanggal', 'Suhu']].head(3)) 
+
                 if st.button("Konfirmasi Import Data"):
                     count = 0
-                    for index, row in data_import.iterrows():
-                        # Cek apakah data sudah ada untuk menghindari duplikat
+                    # Abaikan data kosong pada kolom vital
+                    for index, row in data_import.dropna(subset=['Tanggal', 'Suhu']).iterrows():
                         db.execute("INSERT INTO daily_logs (cycle_id, log_date, raw_temp) VALUES (?, ?, ?)", 
                                    (active_cycle[0], str(row['Tanggal']), float(row['Suhu'])))
                         count += 1
                     db.commit()
                     st.success(f"✅ Berhasil mengimpor {count} data historis!")
                     st.rerun()
+            except KeyError as e:
+                st.error(f"Kolom tidak ditemukan: {e}. Pastikan file memiliki kolom 'Tanggal' dan 'Bodi Tengah (°C)'.")
             except Exception as e:
                 st.error(f"Gagal memproses CSV: {e}")
         
@@ -113,10 +134,14 @@ with st.sidebar:
             db.commit()
             st.rerun()
 
-# MAIN AREA (Logika Prediksi Harian)
+# --- MAIN AREA (Versi Diperbarui: Otomatis Update) ---
 if active_cycle:
     cycle_id, start_date_str = active_cycle
-    start_date = datetime.date.fromisoformat(start_date_str)
+    # Pastikan start_date dikonversi dengan aman menggunakan Pandas
+    try:
+        start_date = pd.to_datetime(start_date_str).date()
+    except Exception:
+        start_date = datetime.date.fromisoformat(start_date_str)
     
     col1, col2 = st.columns([1, 2])
     
@@ -126,30 +151,48 @@ if active_cycle:
         temp_raw = st.number_input("Suhu Bodi Tengah (°C)", 200.0, 500.0, 350.0)
         
         if st.button("Simpan & Prediksi", type="primary"):
-            hari_ops = (tgl_skrg - start_date).days
-            roll30 = get_rolling_30(cycle_id, temp_raw)
-            
-            if model:
-                pred_mm = float(model.predict([[hari_ops, roll30]])[0])
-                db.execute("INSERT INTO daily_logs (cycle_id, log_date, raw_temp) VALUES (?, ?, ?)", 
-                           (cycle_id, tgl_skrg.isoformat(), temp_raw))
-                db.commit()
-                st.session_state['hasil'] = {
-                    'mm': pred_mm, 'hari': hari_ops, 'roll': roll30,
-                    'sisa': max(0, int((pred_mm - 100) / 0.191))
-                }
+            # Hanya menyimpan data ke DB, perhitungannya ditangani di Col2
+            db.execute("INSERT INTO daily_logs (cycle_id, log_date, raw_temp) VALUES (?, ?, ?)", 
+                       (cycle_id, tgl_skrg.isoformat(), temp_raw))
+            db.commit()
+            st.rerun()
     
     with col2:
         st.subheader("📊 Visualisasi & Analisis")
-        if 'hasil' in st.session_state:
-            res = st.session_state['hasil']
-            m1, m2, m3 = st.columns(3)
-            m1.metric("Ketebalan BTA", f"{res['mm']:.1f} mm")
-            m2.metric("Umur Operasi", f"{res['hari']} Hari")
-            m3.metric("Est. Sisa Umur", f"{res['sisa']} Hari")
         
-        # Tampilkan Grafik (akan otomatis terupdate setelah import CSV)
-        df_hist = pd.read_sql_query(f"SELECT log_date, raw_temp FROM daily_logs WHERE cycle_id={cycle_id} ORDER BY log_date ASC", db)
-        if not df_hist.empty:
-            st.line_chart(df_hist.set_index('log_date'))
-            st.write(f"Total data dalam database: **{len(df_hist)} baris**")
+        # Tarik semua data dari DB menggunakan Pandas
+        df_hist = pd.read_sql_query(f"SELECT log_date, raw_temp FROM daily_logs WHERE cycle_id={cycle_id}", db)
+        
+        if not df_hist.empty and model is not None:
+            # Rapikan dan urutkan tanggal (antisipasi beda format CSV "MM/DD/YYYY" vs Streamlit "YYYY-MM-DD")
+            df_hist['log_date'] = pd.to_datetime(df_hist['log_date'], format="mixed", dayfirst=False)
+            df_hist = df_hist.sort_values('log_date').reset_index(drop=True)
+            
+            # Ambil data hari terakhir yang ada di database
+            latest_row = df_hist.iloc[-1]
+            latest_date = latest_row['log_date'].date()
+            
+            # Perhitungan Hari Operasi
+            hari_ops = (latest_date - start_date).days
+            
+            # Hitung Rolling 30 dari Pandas
+            tail_30 = pd.to_numeric(df_hist['raw_temp'].tail(30), errors='coerce').dropna()
+            roll30 = tail_30.mean() if not tail_30.empty else 0.0
+            
+            # Lakukan Prediksi Model
+            pred_mm = float(model.predict([[hari_ops, roll30]])[0])
+            sisa_hari = max(0, int((pred_mm - 100) / 0.191))
+            
+            # Tampilkan ke Layar secara Otomatis
+            m1, m2, m3 = st.columns(3)
+            m1.metric("Ketebalan BTA", f"{pred_mm:.1f} mm")
+            m2.metric("Umur Operasi", f"{hari_ops} Hari")
+            m3.metric("Est. Sisa Umur", f"{sisa_hari} Hari")
+            
+            # Kembalikan tipe data ke string untuk chart agar rapi
+            df_hist['log_date'] = df_hist['log_date'].dt.strftime('%Y-%m-%d')
+            st.line_chart(df_hist.set_index('log_date')['raw_temp'])
+            st.write(f"Total data dalam database: **{len(df_hist)} baris**")
+            
+        else:
+            st.info("Belum ada data untuk dianalisis. Silakan input harian atau import CSV.")