new

app.py

@@ -3,10 +3,10 @@ import streamlit as st
 import joblib
 import pandas as pd
 import numpy as np
+import os  # Tambahan untuk manajemen file
 from sklearn.preprocessing import MinMaxScaler
 from tensorflow.keras.models import load_model
 from datetime import date, timedelta
-import yfinance as yf
 from pathlib import Path
 import matplotlib.pyplot as plt
 import matplotlib.dates as mdates
@@ -18,9 +18,19 @@ rcParams['font.family'] = 'DejaVu Sans'
 # --- Konfigurasi Halaman ---
 st.set_page_config(
     page_title="Prediksi Harga Ethereum (GRU)",
-    page_icon="🪙"
+    page_icon="🪙",
+    layout="wide" # Opsional: agar tampilan lebih luas
 )
 
+# --- KONFIGURASI FILE ADMIN ---
+DATA_FOLDER = 'dataset'
+DATA_FILE = 'databackup_eth.csv'
+DATA_PATH = os.path.join(DATA_FOLDER, DATA_FILE)
+
+# Pastikan folder dataset ada
+if not os.path.exists(DATA_FOLDER):
+    os.makedirs(DATA_FOLDER)
+
 # --- GLOBAL STYLES (UI only) ---
 st.markdown("""
 <style>
@@ -32,6 +42,15 @@ html, body, [class*="css"]  { font-family: "Inter", "DejaVu Sans", sans-serif; }
   --ring: #e5e7eb;
 }
 
+/* --- PERBAIKAN DISINI: MEMBATASI LEBAR KONTEN --- */
+/* Ini memaksa konten tetap di tengah dengan lebar maksimal tertentu */
+.block-container {
+    max-width: 1000px; /* Atur angka ini sesuai selera (misal 900px - 1200px) */
+    padding-top: 2rem;
+    padding-bottom: 2rem;
+    margin: auto; /* Posisi otomatis di tengah */
+}
+
 /* gradient title */
 .app-title {
   text-align:center;
@@ -81,74 +100,49 @@ html, body, [class*="css"]  { font-family: "Inter", "DejaVu Sans", sans-serif; }
 """, unsafe_allow_html=True)
 
 
-# --- Fungsi-fungsi Bantuan ---
+# --- Fungsi-fungsi Bantuan (JANGAN UBAH LOGIKA UTAMA) ---
 
 @st.cache_data(ttl="1h") 
 def load_eth_data():
-    ticker = "ETH-USD"
+    """
-    df = None
+   
-    
+    """
-    # 1. COBA ONLINE
+    # Cek apakah file admin ada
-    try:
+    if os.path.exists(DATA_PATH):
-        
+        try:
-        df = yf.download(
+            df = pd.read_csv(DATA_PATH)
-            ticker, 
-            start="2024-01-01", 
-            end=date.today() + timedelta(days=1), 
-            progress=False,
-            auto_adjust=True,
-            multi_level_index=False 
-        )
-        
-        if df is not None and not df.empty:
-            # Bersihkan Index & Kolom
-            df = df.reset_index()
             
-            new_cols = []
+            # --- PEMBERSIHAN DATA AGAR SESUAI FORMAT MODEL ---
-            for col in df.columns:
+            # 1. Hapus kolom index lama jika ada
-                col_name = col[0] if isinstance(col, tuple) else str(col)
+            if "Unnamed: 0" in df.columns:
-                new_cols.append(col_name)
+                df = df.drop(columns=["Unnamed: 0"])
-            df.columns = new_cols
             
-            # Pastikan kolom pertama adalah Date
+            # 2. Standarisasi nama kolom Date
-            if 'Date' not in df.columns:
+            if "Date" not in df.columns:
-                df = df.rename(columns={df.columns[0]: 'Date'})
+                # Coba cari kolom yang mirip 'date'
-
+                found = False
-            # Hapus Timezone
+                for col in df.columns:
-            df['Date'] = pd.to_datetime(df['Date']).dt.tz_localize(None)
+                    if col.lower() == "date":
-
+                        df = df.rename(columns={col: "Date"})
-            # Simpan Backup (Timpa file lama agar fresh)
+                        found = True
-            try:
+                        break
-                df.to_csv("eth_backup.csv", index=False)
+                if not found:
-            except:
+                    # Jika tidak ada header Date, asumsikan kolom pertama adalah Date
-                pass
+                    df = df.rename(columns={df.columns[0]: 'Date'})
             
-            return df, "online"
+            # 3. Konversi ke datetime
-
+            df["Date"] = pd.to_datetime(df["Date"])
-    except Exception as e:
+            # Hapus timezone jika ada agar kompatibel dengan matplotlib/numpy
-        print(f"Gagal Online: {e}")
+            if df["Date"].dt.tz is not None:
-
+                df["Date"] = df["Date"].dt.tz_localize(None)
-    # 2. COBA BACKUP (JIKA ONLINE GAGAL)
+                
-    try:
+            return df, "admin_file"
-        df_backup = pd.read_csv("eth_backup.csv")
-        
-        # Bersihkan kolom sampah jika ada
-        if "Unnamed: 0" in df_backup.columns:
-            df_backup = df_backup.drop(columns=["Unnamed: 0"])
             
-        # Pastikan kolom Date dikenali
+        except Exception as e:
-        if "Date" in df_backup.columns:
+            return None, f"error_read: {str(e)}"
-            df_backup["Date"] = pd.to_datetime(df_backup["Date"])
+    else:
-        elif df_backup.columns[0].lower() == "date":
+        return None, "no_file"
-             df_backup = df_backup.rename(columns={df_backup.columns[0]: "Date"})
+
-             df_backup["Date"] = pd.to_datetime(df_backup["Date"])
-             
-        return df_backup, "backup"
-        
-    except FileNotFoundError:
-        # 3. GAGAL TOTAL
-        return None, "error"
-    
 def validate_scaler(scaler):
     """Validasi scaler agar konsisten dengan training."""
     issues = []
@@ -191,44 +185,23 @@ def validate_model_input(model):
 
 @st.cache_resource
 def load_gru_assets():
-    """Load model & scaler hasil training (wajib pakai scaler yang sama)."""
+    """Load model & scaler hasil training."""
     model_path = Path("gru_model.h5")
     scaler_path = Path("scaler_gru.pkl")
 
     if not model_path.exists() or not scaler_path.exists():
-        st.warning(
-            f"File tidak ditemukan.\n"
-            f"Model: {model_path.resolve().name} ada? {model_path.exists()}\n"
-            f"Scaler: {scaler_path.resolve().name} ada? {scaler_path.exists()}"
-        )
         return None, None
 
     try:
         model = load_model(model_path, compile=False)
         scaler = joblib.load(scaler_path)
-
-        ok_scaler, scaler_issues, scaler_warnings = validate_scaler(scaler)
-        for w in scaler_warnings:
-            st.warning("⚠️ " + w)
-        if not ok_scaler:
-            st.error("❌ Scaler tidak kompatibel:\n- " + "\n- ".join(scaler_issues))
-            return None, None
-
-        ok_model, model_issues = validate_model_input(model)
-        if not ok_model:
-            st.error("❌ Model input tidak kompatibel:\n- " + "\n- ".join(model_issues))
-            return None, None
-
         return model, scaler
-
     except Exception as e:
         st.error(f"Gagal load aset: {e}")
         return None, None
 
 
 def predict_from_sequence_pure(model, initial_sequence_scaled, n_days):
-  
-    
     seq = np.array(initial_sequence_scaled, dtype="float32").reshape(-1)  # (time_step,)
     preds = []
 
@@ -242,7 +215,7 @@ def predict_from_sequence_pure(model, initial_sequence_scaled, n_days):
 
 
 def create_combined_chart(df, start_date, future_dates, future_predictions):
-    """Membuat grafik gabungan dengan gaya yang sama seperti referensi."""
+    """Membuat grafik gabungan."""
     context_start_date = start_date - timedelta(days=60)
     recent_df = df[(df["Date"] >= context_start_date) & (df["Date"] <= start_date)].copy()
 
@@ -271,7 +244,6 @@ def create_combined_chart(df, start_date, future_dates, future_predictions):
         color="black", s=15, alpha=0.6, zorder=5, label="Data Harian Aktual"
     )
 
-    # garis penghubung trend terakhir ke prediksi pertama
     try:
         last_trend_date = recent_df["Date"].iloc[-1]
         last_trend_price = recent_df["Trend_Aktual"].dropna().iloc[-1]
@@ -308,132 +280,184 @@ def create_combined_chart(df, start_date, future_dates, future_predictions):
     return fig
 
 
-# --- Tampilan Antarmuka Aplikasi ---
+# --- HALAMAN: DASHBOARD PUBLIK (Landing Page) ---
+def show_dashboard():
+    st.markdown("<div class='app-title'>Prediksi Harga Ethereum (GRU)</div>", unsafe_allow_html=True)
+    st.markdown("<div class='app-subtitle'>Data historis & prediksi ETH-USD berbasis GRU</div>", unsafe_allow_html=True)
 
-st.markdown("<div class='app-title'>Prediksi Harga Ethereum (GRU)</div>", unsafe_allow_html=True)
+    df, data_source = load_eth_data()
-st.markdown("<div class='app-subtitle'>Data historis & prediksi ETH-USD berbasis GRU</div>", unsafe_allow_html=True)
+    model, scaler = load_gru_assets()
 
-df, data_source = load_eth_data()
+    # LOGIC CHECK DATA
-model, scaler = load_gru_assets()
+    if data_source == "no_file":
+        st.warning("⚠️ Data historis belum tersedia. Silakan hubungi Admin untuk mengupload 'databackup_eth'.")
+        return # Stop eksekusi jika data tidak ada
+    elif "error" in data_source:
+        st.error(f"❌ Terjadi kesalahan membaca data: {data_source}")
+        return
 
-# LOGIC UI (TOAST/WARNING) DITARUH DI SINI (DILUAR CACHE)
+    if df is not None and model is not None and scaler is not None:
-if data_source == "backup":
+        # Menampilkan tabel data historis
-    st.toast("Koneksi Yahoo lambat. Menggunakan data backup lokal.", icon="⚠️")
+        st.markdown("<div class='card'>", unsafe_allow_html=True)
-elif data_source == "error":
+        st.markdown("<div class='h-section'>📚 Data Historis Harga Ethereum </div>", unsafe_allow_html=True)
-    st.error("❌ Gagal memuat data (Online gagal & Backup tidak ada).")
+        
+        # --- PERUBAHAN DISINI: HAPUS .tail(100) ---
+        # Menggunakan height=400 agar tabel lebih tinggi dan bisa di-scroll
+        st.dataframe(df, height=400, use_container_width=True) 
+
+        # Menampilkan visualisasi line chart
+        st.markdown("<div class='card'>", unsafe_allow_html=True)
+        st.markdown("<div class='h-section'>📈 Visualisasi Harga Historis</div>", unsafe_allow_html=True)
+        # Grafik ini sudah otomatis mengambil full data df
+        st.line_chart(df.rename(columns={"Date": "index"}).set_index("index")["Close"])
+        st.markdown("</div>", unsafe_allow_html=True)
+
+        st.markdown("<div class='card'>", unsafe_allow_html=True)
+        st.markdown("<div class='h-section'>🎯 Mulai Prediksi Berdasarkan Tanggal</div>", unsafe_allow_html=True)
+
+        time_step = int(model.input_shape[1])
+        min_selectable_date = (df["Date"].min() + timedelta(days=time_step)).date()
+        max_selectable_date = df["Date"].max().date()
+
+        col1, col2 = st.columns(2)
+        with col1:
+            selected_date = st.date_input(
+                "Pilih tanggal mulai prediksi:",
+                value=max_selectable_date,
+                min_value=min_selectable_date,
+                max_value=max_selectable_date,
+                help=f"Pilih tanggal antara {min_selectable_date} dan {max_selectable_date}"
+            )
+        with col2:
+            prediction_days = st.slider(
+                "Pilih jumlah hari untuk prediksi (1-30 hari):",
+                min_value=1, max_value=30, value=15, step=1
+            )
+
+        if st.button("Buat Prediksi", key="predict_button"):
+            if prediction_days < 1:
+                st.warning("Jumlah hari prediksi minimal **1**. Silakan geser slidernya dulu. 🙂")
+                st.stop()
+
+            with st.spinner("Memproses dan menjalankan prediksi..."):
+                # scaling (mengikuti scaler training)
+                close_values = df[["Close"]].values.astype(float)
+                scaled_data = scaler.transform(close_values)
+
+                # Cocokkan berdasarkan tanggal (robust)
+                selected_date_dt = pd.to_datetime(selected_date)
+                mask = df["Date"].dt.date == selected_date_dt.date()
+                idxs = np.where(mask.to_numpy())[0]
+
+                if len(idxs) == 0:
+                    st.error(f"Tanggal {selected_date_dt.strftime('%Y-%m-%d')} tidak ditemukan dalam dataset.")
+                    st.stop()
+
+                start_index = int(idxs[0])
+
+                if start_index - time_step < 0:
+                    st.error(
+                        f"Tanggal terlalu awal untuk diprediksi. "
+                        f"Butuh {time_step} hari data sebelumnya."
+                    )
+                    st.stop()
+
+                initial_sequence = scaled_data[start_index - time_step: start_index]  # (time_step,1)
+
+                # ✅ Prediksi MURNI dari model
+                future_predictions_scaled = predict_from_sequence_pure(model, initial_sequence, prediction_days)
+
+                # ✅ Inverse transform MURNI dari scaler
+                future_predictions = scaler.inverse_transform(future_predictions_scaled)
+
+                # sanity-check
+                if not np.isfinite(future_predictions).all():
+                    st.error("❌ Hasil prediksi mengandung NaN/Inf. Periksa kompatibilitas model/scaler.")
+                    st.stop()
+
+                future_dates = [selected_date_dt + timedelta(days=i) for i in range(1, prediction_days + 1)]
+
+                st.markdown("<div class='card'>", unsafe_allow_html=True)
+                st.subheader("📊 Grafik Prediksi (Historis + Prediksi)")
+                fig = create_combined_chart(df, selected_date_dt, future_dates, future_predictions)
+                st.pyplot(fig)
+                st.markdown("</div>", unsafe_allow_html=True)
+
+                st.markdown("<div class='card'>", unsafe_allow_html=True)
+                st.subheader("🧾 Tabel Detail Prediksi")
+                prediction_table_df = pd.DataFrame({
+                    "Tanggal": [d.strftime("%Y-%m-%d") for d in future_dates],
+                    "Harga Prediksi (USD)": [f"USD {price[0]:,.0f}" for price in future_predictions]
+                })
+                prediction_table_df.index = prediction_table_df.index + 1
+                st.dataframe(prediction_table_df, use_container_width=True)
+                st.markdown("</div>", unsafe_allow_html=True)
+
+                last_price = float(df.loc[start_index, "Close"])
+                max_prediction = float(np.max(future_predictions))
+                min_prediction = float(np.min(future_predictions))
+
+                st.info(f"""
+    📊 **Informasi Prediksi:**
+    - Harga terakhir pada tanggal {selected_date.strftime('%Y-%m-%d')}: USD {last_price:,.0f}
+    - Harga prediksi tertinggi: USD {max_prediction:,.0f}
+    - Harga prediksi terendah: USD {min_prediction:,.0f}
+    """)
+
+        st.markdown("</div>", unsafe_allow_html=True)
+        st.markdown("<div class='footer'>Dibuat oleh Muhammad Gilman Nadhif Azmi</div>", unsafe_allow_html=True)
+
+    elif model is None:
+        st.error("Gagal memuat model. Pastikan file 'gru_model.h5' ada di folder yang sama dengan aplikasi.")
+    elif scaler is None:
+        st.error("Gagal memuat scaler. Pastikan file 'scaler_gru.pkl' ada di folder yang sama dengan aplikasi.")
+
+# --- HALAMAN: ADMIN LOGIN & UPLOAD ---
+def show_admin_page():
+    st.title("🔐 Admin Panel")
+    st.write("Area khusus admin untuk memperbarui dataset harga Ethereum.")
+
+    # Simple Password check
+    password = st.text_input("Masukkan Password Admin:", type="password")
     
-if df is not None and model is not None and scaler is not None:
+    if password == "admin123": # Ganti password sesuai keinginan
-    # Menampilkan tabel data historis
+        st.success("Akses Diterima.")
-    st.markdown("<div class='card'>", unsafe_allow_html=True)
+        st.divider()
-    st.markdown("<div class='h-section'>📚 Data Historis Harga Ethereum</div>", unsafe_allow_html=True)
+        
-    st.dataframe(df, height=300, use_container_width=True)
+        st.subheader("📂 Upload Dataset Baru")
-    st.markdown("</div>", unsafe_allow_html=True)
+        st.info(f"File akan disimpan sebagai: `{DATA_FILE}` di dalam folder sistem.")
+        
+        uploaded_file = st.file_uploader("Upload file CSV (Format: Date, Close, dll)", type=['csv'])
+        
+        if uploaded_file is not None:
+            # Baca preview
+            try:
+                df_preview = pd.read_csv(uploaded_file)
+                st.write("Preview Data:", df_preview.head())
+                
+                if st.button("💾 Simpan & Update Sistem"):
+                    # Simpan file ke path yang ditentukan
+                    df_preview.to_csv(DATA_PATH, index=False)
+                    
+                    # Clear cache agar halaman public langsung berubah
+                    st.cache_data.clear()
+                    
+                    st.success(f"Berhasil! Data telah diperbarui. Pengguna publik sekarang melihat data baru.")
+                    
+            except Exception as e:
+                st.error(f"File error: {e}")
+                
+    elif password:
+        st.error("Password Salah.")
 
-    # Menampilkan visualisasi line chart
-    st.markdown("<div class='card'>", unsafe_allow_html=True)
-    st.markdown("<div class='h-section'>📈 Visualisasi Harga Historis</div>", unsafe_allow_html=True)
-    st.line_chart(df.rename(columns={"Date": "index"}).set_index("index")["Close"])
-    st.markdown("</div>", unsafe_allow_html=True)
 
-    st.markdown("<div class='card'>", unsafe_allow_html=True)
+# --- MAIN NAVIGATION (SIDEBAR) ---
-    st.markdown("<div class='h-section'>🎯 Mulai Prediksi Berdasarkan Tanggal</div>", unsafe_allow_html=True)
+# Ini adalah logika utama yang memisahkan tampilan Admin dan User Biasa
 
-    time_step = int(model.input_shape[1])
+st.sidebar.title("Navigasi")
-    min_selectable_date = (df["Date"].min() + timedelta(days=time_step)).date()
+menu = st.sidebar.radio("Pilih Halaman:", ["Dashboard (Public)", "Admin Panel"])
-    max_selectable_date = df["Date"].max().date()
 
-    col1, col2 = st.columns(2)
+if menu == "Dashboard (Public)":
-    with col1:
+    show_dashboard()
-        selected_date = st.date_input(
+elif menu == "Admin Panel":
-            "Pilih tanggal mulai prediksi:",
+    show_admin_page()
-            value=max_selectable_date,
-            min_value=min_selectable_date,
-            max_value=max_selectable_date,
-            help=f"Pilih tanggal antara {min_selectable_date} dan {max_selectable_date}"
-        )
-    with col2:
-        prediction_days = st.slider(
-            "Pilih jumlah hari untuk prediksi (1-30 hari):",
-            min_value=1, max_value=30, value=15, step=1
-        )
-
-    if st.button("Buat Prediksi", key="predict_button"):
-        if prediction_days < 1:
-            st.warning("Jumlah hari prediksi minimal **1**. Silakan geser slidernya dulu. 🙂")
-            st.stop()
-
-        with st.spinner("Memproses dan menjalankan prediksi..."):
-            # scaling (mengikuti scaler training)
-            close_values = df[["Close"]].values.astype(float)
-            scaled_data = scaler.transform(close_values)
-
-            # Cocokkan berdasarkan tanggal (robust)
-            selected_date_dt = pd.to_datetime(selected_date)
-            mask = df["Date"].dt.date == selected_date_dt.date()
-            idxs = np.where(mask.to_numpy())[0]
-
-            if len(idxs) == 0:
-                st.error(f"Tanggal {selected_date_dt.strftime('%Y-%m-%d')} tidak ditemukan dalam dataset.")
-                st.stop()
-
-            start_index = int(idxs[0])
-
-            if start_index - time_step < 0:
-                st.error(
-                    f"Tanggal terlalu awal untuk diprediksi. "
-                    f"Butuh {time_step} hari data sebelumnya. Pilih tanggal setelah "
-                    f"{(df['Date'].min() + timedelta(days=time_step)).strftime('%Y-%m-%d')}."
-                )
-                st.stop()
-
-            initial_sequence = scaled_data[start_index - time_step: start_index]  # (time_step,1)
-
-            # ✅ Prediksi MURNI dari model
-            future_predictions_scaled = predict_from_sequence_pure(model, initial_sequence, prediction_days)
-
-            # ✅ Inverse transform MURNI dari scaler
-            future_predictions = scaler.inverse_transform(future_predictions_scaled)
-
-            # sanity-check
-            if not np.isfinite(future_predictions).all():
-                st.error("❌ Hasil prediksi mengandung NaN/Inf. Periksa kompatibilitas model/scaler.")
-                st.stop()
-
-            future_dates = [selected_date_dt + timedelta(days=i) for i in range(1, prediction_days + 1)]
-
-            st.markdown("<div class='card'>", unsafe_allow_html=True)
-            st.subheader("📊 Grafik Prediksi (Historis + Prediksi)")
-            fig = create_combined_chart(df, selected_date_dt, future_dates, future_predictions)
-            st.pyplot(fig)
-            st.markdown("</div>", unsafe_allow_html=True)
-
-            st.markdown("<div class='card'>", unsafe_allow_html=True)
-            st.subheader("🧾 Tabel Detail Prediksi")
-            prediction_table_df = pd.DataFrame({
-                "Tanggal": [d.strftime("%Y-%m-%d") for d in future_dates],
-                "Harga Prediksi (USD)": [f"USD {price[0]:,.0f}" for price in future_predictions]
-            })
-            prediction_table_df.index = prediction_table_df.index + 1
-            st.dataframe(prediction_table_df, use_container_width=True)
-            st.markdown("</div>", unsafe_allow_html=True)
-
-            last_price = float(df.loc[start_index, "Close"])
-            max_prediction = float(np.max(future_predictions))
-            min_prediction = float(np.min(future_predictions))
-
-            st.info(f"""
-📊 **Informasi Prediksi:**
-- Harga terakhir pada tanggal {selected_date.strftime('%Y-%m-%d')}: USD {last_price:,.0f}
-- Harga prediksi tertinggi: USD {max_prediction:,.0f}
-- Harga prediksi terendah: USD {min_prediction:,.0f}
-""")
-
-    st.markdown("</div>", unsafe_allow_html=True)
-    st.markdown("<div class='footer'>Dibuat oleh Muhammad Gilman Nadhif Azmi</div>", unsafe_allow_html=True)
-
-elif model is None:
-    st.error("Gagal memuat model. Pastikan file 'gru_model.h5' ada di folder yang sama dengan aplikasi.")
-elif scaler is None:
-    st.error("Gagal memuat scaler. Pastikan file 'scaler_gru.pkl' ada di folder yang sama dengan aplikasi.")
-else:
-    st.error("Gagal memuat data. Coba cek koneksi internet atau sumber data yfinance.")

eth_backup.csv

@@ -715,4 +715,4 @@ Date,Close,High,Low,Open,Volume
 2025-12-14,3060.5947265625,3128.622802734375,3034.692626953125,3116.743896484375,15619543350
 2025-12-15,2964.18310546875,3175.1181640625,2899.685791015625,3060.4814453125,28765976892
 2025-12-16,2964.180908203125,2978.92138671875,2890.01171875,2964.379638671875,22709189818
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