new

app1.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
@@ -18,19 +18,9 @@ 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>
@@ -42,15 +32,6 @@ 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;
@@ -100,49 +81,74 @@ html, body, [class*="css"]  { font-family: "Inter", "DejaVu Sans", sans-serif; }
 """, unsafe_allow_html=True)
 
 
-# --- Fungsi-fungsi Bantuan (JANGAN UBAH LOGIKA UTAMA) ---
+# --- Fungsi-fungsi Bantuan ---
 
 @st.cache_data(ttl="1h") 
 def load_eth_data():
-    """
+    ticker = "ETH-USD"
-   
+    df = None
-    """
+    
-    # Cek apakah file admin ada
+    # 1. COBA ONLINE
-    if os.path.exists(DATA_PATH):
+    try:
-        try:
+        
-            df = pd.read_csv(DATA_PATH)
+        df = yf.download(
+            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()
             
-            # --- PEMBERSIHAN DATA AGAR SESUAI FORMAT MODEL ---
+            new_cols = []
-            # 1. Hapus kolom index lama jika ada
+            for col in df.columns:
-            if "Unnamed: 0" in df.columns:
+                col_name = col[0] if isinstance(col, tuple) else str(col)
-                df = df.drop(columns=["Unnamed: 0"])
+                new_cols.append(col_name)
+            df.columns = new_cols
             
-            # 2. Standarisasi nama kolom Date
+            # Kolom pertama adalah Date
-            if "Date" not in df.columns:
+            if 'Date' not in df.columns:
-                # Coba cari kolom yang mirip 'date'
+                df = df.rename(columns={df.columns[0]: 'Date'})
-                found = False
-                for col in df.columns:
-                    if col.lower() == "date":
-                        df = df.rename(columns={col: "Date"})
-                        found = True
-                        break
-                if not found:
-                    # Jika tidak ada header Date, asumsikan kolom pertama adalah Date
-                    df = df.rename(columns={df.columns[0]: 'Date'})
-            
-            # 3. Konversi ke datetime
-            df["Date"] = pd.to_datetime(df["Date"])
-            # Hapus timezone jika ada agar kompatibel dengan matplotlib/numpy
-            if df["Date"].dt.tz is not None:
-                df["Date"] = df["Date"].dt.tz_localize(None)
-                
-            return df, "admin_file"
-            
-        except Exception as e:
-            return None, f"error_read: {str(e)}"
-    else:
-        return None, "no_file"
 
+            # Hapus Timezone
+            df['Date'] = pd.to_datetime(df['Date']).dt.tz_localize(None)
+
+            # Simpan Backup (Timpa file lama agar fresh)
+            try:
+                df.to_csv("eth_backup.csv", index=False)
+            except:
+                pass
+            
+            return df, "online"
+
+    except Exception as e:
+        print(f"Gagal Online: {e}")
+
+    # 2. COBA BACKUP (JIKA ONLINE GAGAL)
+    try:
+        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
+        if "Date" in df_backup.columns:
+            df_backup["Date"] = pd.to_datetime(df_backup["Date"])
+        elif df_backup.columns[0].lower() == "date":
+             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 = []
@@ -185,23 +191,44 @@ def validate_model_input(model):
 
 @st.cache_resource
 def load_gru_assets():
-    """Load model & scaler hasil training."""
+    """Load model & scaler hasil training (wajib pakai scaler yang sama)."""
     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 = []
 
@@ -215,8 +242,8 @@ 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."""
+    """Membuat grafik gabungan dengan gaya yang sama seperti referensi."""
-    context_start_date = start_date - timedelta(days=60)
+    context_start_date = start_date - timedelta(days=609)
     recent_df = df[(df["Date"] >= context_start_date) & (df["Date"] <= start_date)].copy()
 
     window_size = 7
@@ -226,24 +253,32 @@ def create_combined_chart(df, start_date, future_dates, future_predictions):
 
     fig, ax = plt.subplots(figsize=(14, 8))
 
-    ax.plot(
+    
-        recent_df["Date"], recent_df["Close"],
-        color="gray", linewidth=1.0, label="Harga Aktual (Harian)", alpha=0.5
-    )
     ax.plot(
         recent_df["Date"], recent_df["Trend_Aktual"],
         color="#1f77b4", linewidth=2.5, label="Tren Harga Aktual", alpha=0.9
     )
+    # Membuat tren prediksi menggunakan moving average 7 hari
+    pred_trend = (
+        pd.Series(predictions_flat)
+        .rolling(window=7, min_periods=1)
+        .mean()
+    )
+
     ax.plot(
-        future_dates, predictions_flat,
+        future_dates,
-        color="#d62728", linewidth=2.5, label="Harga Prediksi (GRU)",
+        pred_trend,
-        alpha=0.9, marker="o", markersize=4
+        color="#d62728",
+        linewidth=2.5,
+        label="Tren Harga Prediksi (GRU)",
+        alpha=0.9
     )
     ax.scatter(
         recent_df["Date"], recent_df["Close"],
         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]
@@ -280,184 +315,165 @@ def create_combined_chart(df, start_date, future_dates, future_predictions):
     return fig
 
 
-# --- HALAMAN: DASHBOARD PUBLIK (Landing Page) ---
+# --- Tampilan Antarmuka Aplikasi ---
-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)
 
-    df, data_source = load_eth_data()
+st.markdown("<div class='app-title'>Prediksi Harga Ethereum (GRU)</div>", unsafe_allow_html=True)
-    model, scaler = load_gru_assets()
+st.markdown("<div class='app-subtitle'>Data historis & prediksi ETH-USD berbasis GRU</div>", unsafe_allow_html=True)
 
-    # LOGIC CHECK DATA
+df, data_source = load_eth_data()
-    if data_source == "no_file":
+model, scaler = load_gru_assets()
-        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
 
-    if df is not None and model is not None and scaler is not None:
+# LOGIC UI (TOAST/WARNING) DITARUH DI SINI (DILUAR CACHE)
-        # Menampilkan tabel data historis
+if data_source == "backup":
-        st.markdown("<div class='card'>", unsafe_allow_html=True)
+    st.toast("Koneksi Yahoo lambat. Menggunakan data backup lokal.", icon="⚠️")
-        st.markdown("<div class='h-section'>📚 Data Historis Harga Ethereum </div>", unsafe_allow_html=True)
+elif data_source == "error":
-        
+    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
+if df is not None and model is not None and scaler is not None:
-        st.dataframe(df, height=400, use_container_width=True) 
+    # Menampilkan tabel data historis
+    st.markdown("<div class='card'>", unsafe_allow_html=True)
+    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.markdown("</div>", unsafe_allow_html=True)
 
-        # Menampilkan visualisasi line chart
+    # Menampilkan visualisasi line chart
-        st.markdown("<div class='card'>", unsafe_allow_html=True)
+    st.markdown("<div class='card'>", unsafe_allow_html=True)
-        st.markdown("<div class='h-section'>📈 Visualisasi Harga Historis</div>", 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.line_chart(df.rename(columns={"Date": "index"}).set_index("index")["Close"])
+    st.markdown("</div>", unsafe_allow_html=True)
-        st.markdown("</div>", unsafe_allow_html=True)
 
-        st.markdown("<div class='card'>", 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)
+    st.markdown("<div class='h-section'>🎯 Mulai Prediksi Berdasarkan Tanggal</div>", unsafe_allow_html=True)
 
-        time_step = int(model.input_shape[1])
+    time_step = int(model.input_shape[1])
-        min_selectable_date = (df["Date"].min() + timedelta(days=time_step)).date()
+    min_selectable_date = (df["Date"].min() + timedelta(days=time_step)).date()
-        max_selectable_date = df["Date"].max().date()
+    max_selectable_date = df["Date"].max().date()
 
-        col1, col2 = st.columns(2)
+    col1, col2 = st.columns(2)
-        with col1:
+    with col1:
-            selected_date = st.date_input(
+        selected_date = st.date_input(
-                "Pilih tanggal mulai prediksi:",
+            "Pilih tanggal mulai prediksi:",
-                value=max_selectable_date,
+            value=max_selectable_date,
-                min_value=min_selectable_date,
+            min_value=min_selectable_date,
-                max_value=max_selectable_date,
+            max_value=max_selectable_date,
-                help=f"Pilih tanggal antara {min_selectable_date} dan {max_selectable_date}"
+            help=f"Pilih tanggal antara {min_selectable_date} dan {max_selectable_date}"
-            )
+        )
-        with col2:
+    with col2:
-            prediction_days = st.slider(
+        prediction_days = st.slider(
-                "Pilih jumlah hari untuk prediksi (1-30 hari):",
+            "Pilih jumlah hari untuk prediksi (1-30 hari):",
-                min_value=1, max_value=30, value=15, step=1
+            min_value=1, max_value=30, value=15, step=1
-            )
+        )
 
-        if st.button("Buat Prediksi", key="predict_button"):
+    if st.button("Buat Prediksi", key="predict_button"):
-            if prediction_days < 1:
+        if prediction_days < 1:
-                st.warning("Jumlah hari prediksi minimal **1**. Silakan geser slidernya dulu. 🙂")
+            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()
 
-            with st.spinner("Memproses dan menjalankan prediksi..."):
+            start_index = int(idxs[0])
-                # scaling (mengikuti scaler training)
-                close_values = df[["Close"]].values.astype(float)
-                scaled_data = scaler.transform(close_values)
 
-                # Cocokkan berdasarkan tanggal (robust)
+            if start_index - time_step < 0:
-                selected_date_dt = pd.to_datetime(selected_date)
+                st.error(
-                mask = df["Date"].dt.date == selected_date_dt.date()
+                    f"Tanggal terlalu awal untuk diprediksi. "
-                idxs = np.where(mask.to_numpy())[0]
+                    f"Butuh {time_step} hari data sebelumnya. Pilih tanggal setelah "
+                    f"{(df['Date'].min() + timedelta(days=time_step)).strftime('%Y-%m-%d')}."
+                )
+                st.stop()
 
-                if len(idxs) == 0:
+            initial_sequence = scaled_data[start_index - time_step: start_index]  # (time_step,1)
-                    st.error(f"Tanggal {selected_date_dt.strftime('%Y-%m-%d')} tidak ditemukan dalam dataset.")
-                    st.stop()
 
-                start_index = int(idxs[0])
+            # ✅ Prediksi MURNI dari model
+            future_predictions_scaled = predict_from_sequence_pure(model, initial_sequence, prediction_days)
 
-                if start_index - time_step < 0:
+            # ✅ Inverse transform MURNI dari scaler
-                    st.error(
+            future_predictions = scaler.inverse_transform(future_predictions_scaled)
-                        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)
+            # sanity-check
+            if not np.isfinite(future_predictions).all():
+                st.error("❌ Hasil prediksi mengandung NaN/Inf. Periksa kompatibilitas model/scaler.")
+                st.stop()
 
-                # ✅ Prediksi MURNI dari model
+            future_dates = [selected_date_dt + timedelta(days=i) for i in range(1, prediction_days + 1)]
-                future_predictions_scaled = predict_from_sequence_pure(model, initial_sequence, prediction_days)
 
-                # ✅ Inverse transform MURNI dari scaler
+            st.markdown("<div class='card'>", unsafe_allow_html=True)
-                future_predictions = scaler.inverse_transform(future_predictions_scaled)
+            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)
 
-                # sanity-check
+            st.markdown("<div class='card'>", unsafe_allow_html=True)
-                if not np.isfinite(future_predictions).all():
+            
-                    st.error("❌ Hasil prediksi mengandung NaN/Inf. Periksa kompatibilitas model/scaler.")
+            #Test 
-                    st.stop()
+            st.subheader("📈 Grafik Detail Prediksi")
 
-                future_dates = [selected_date_dt + timedelta(days=i) for i in range(1, prediction_days + 1)]
+            fig2, ax2 = plt.subplots(figsize=(12, 5))
 
-                st.markdown("<div class='card'>", unsafe_allow_html=True)
+            ax2.plot(
-                st.subheader("📊 Grafik Prediksi (Historis + Prediksi)")
+                future_dates,
-                fig = create_combined_chart(df, selected_date_dt, future_dates, future_predictions)
+                future_predictions.flatten(),
-                st.pyplot(fig)
+                marker='o',
-                st.markdown("</div>", unsafe_allow_html=True)
+                linewidth=2.5
+            )
 
-                st.markdown("<div class='card'>", unsafe_allow_html=True)
+            ax2.set_title(
-                st.subheader("🧾 Tabel Detail Prediksi")
+                "Grafik Detail Hasil Prediksi Harga Ethereum",
-                prediction_table_df = pd.DataFrame({
+                fontsize=14,
-                    "Tanggal": [d.strftime("%Y-%m-%d") for d in future_dates],
+                fontweight="bold"
-                    "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"])
+            ax2.set_xlabel("Tanggal")
-                max_prediction = float(np.max(future_predictions))
+            ax2.set_ylabel("Harga Prediksi (USD)")
-                min_prediction = float(np.min(future_predictions))
 
-                st.info(f"""
+            ax2.grid(True, alpha=0.3)
-    📊 **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)
+            plt.xticks(rotation=45)
-        st.markdown("<div class='footer'>Dibuat oleh Muhammad Gilman Nadhif Azmi</div>", unsafe_allow_html=True)
+            plt.tight_layout()
 
-    elif model is None:
+            st.pyplot(fig2)
-        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 ---
+            st.markdown("</div>", unsafe_allow_html=True)
-def show_admin_page():
-    st.title("🔐 Admin Panel")
-    st.write("Area khusus admin untuk memperbarui dataset harga Ethereum.")
 
-    # Simple Password check
+            st.markdown("<div class='card'>", unsafe_allow_html=True)
-    password = st.text_input("Masukkan Password Admin:", type="password")
-    
-    if password == "admin123": # Ganti password sesuai keinginan
-        st.success("Akses Diterima.")
-        st.divider()
-        
-        st.subheader("📂 Upload Dataset Baru")
-        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.")
 
+            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)
 
-# --- MAIN NAVIGATION (SIDEBAR) ---
+            last_price = float(df.loc[start_index, "Close"])
-# Ini adalah logika utama yang memisahkan tampilan Admin dan User Biasa
+            max_prediction = float(np.max(future_predictions))
+            min_prediction = float(np.min(future_predictions))
 
-st.sidebar.title("Navigasi")
+            st.info(f"""
-menu = st.sidebar.radio("Pilih Halaman:", ["Dashboard (Public)", "Admin Panel"])
+📊 **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}
+""")
 
-if menu == "Dashboard (Public)":
+    st.markdown("</div>", unsafe_allow_html=True)
-    show_dashboard()
+    st.markdown("<div class='footer'>Dibuat oleh Muhammad Gilman Nadhif Azmi</div>", unsafe_allow_html=True)
-elif menu == "Admin Panel":
+
-    show_admin_page()
+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.")

apptest.py

@@ -0,0 +1,479 @@
+import requests
+import streamlit as st
+import joblib
+import pandas as pd
+import numpy as np
+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
+from matplotlib import rcParams
+
+# Set font untuk mendukung karakter Indonesia
+rcParams['font.family'] = 'DejaVu Sans'
+
+# --- Konfigurasi Halaman ---
+st.set_page_config(
+    page_title="Prediksi Harga Ethereum (GRU)",
+    page_icon="🪙"
+)
+
+# --- GLOBAL STYLES (UI only) ---
+st.markdown("""
+<style>
+/* font & warna dasar */
+html, body, [class*="css"]  { font-family: "Inter", "DejaVu Sans", sans-serif; }
+:root {
+  --card-bg: #ffffff;
+  --muted: #6b7280;
+  --ring: #e5e7eb;
+}
+
+/* gradient title */
+.app-title {
+  text-align:center;
+  font-weight:800;
+  font-size: 32px;
+  background: linear-gradient(90deg, #6EE7F9 0%, #7C3AED 50%, #F59E0B 100%);
+  -webkit-background-clip: text;
+  -webkit-text-fill-color: transparent;
+  margin: 0.25rem 0 0.5rem 0;
+}
+
+/* subtitle */
+.app-subtitle {
+  text-align:center;
+  color: var(--muted);
+  margin-bottom: 1.25rem;
+}
+
+/* card container */
+.card {
+  background: var(--card-bg);
+  border: 1px solid var(--ring);
+  border-radius: 16px;
+  padding: 1rem 1.25rem;
+  box-shadow: 0 6px 20px rgba(0,0,0,0.05);
+  margin-bottom: 1rem;
+}
+
+/* section heading */
+.h-section {
+  font-weight:700;
+  font-size: 20px;
+  margin: 0 0 .5rem 0;
+}
+
+/* table tweaks */
+.dataframe tbody tr:hover { background-color: #fafafa; }
+
+/* footer */
+.footer {
+  text-align:center;
+  color: var(--muted);
+  font-size: 13px;
+  margin-top: 2rem;
+}
+</style>
+""", unsafe_allow_html=True)
+
+
+# --- Fungsi-fungsi Bantuan ---
+
+@st.cache_data(ttl="1h") 
+def load_eth_data():
+    ticker = "ETH-USD"
+    df = None
+    
+    # 1. COBA ONLINE
+    try:
+        
+        df = yf.download(
+            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 = []
+            for col in df.columns:
+                col_name = col[0] if isinstance(col, tuple) else str(col)
+                new_cols.append(col_name)
+            df.columns = new_cols
+            
+            # Kolom pertama adalah Date
+            if 'Date' not in df.columns:
+                df = df.rename(columns={df.columns[0]: 'Date'})
+
+            # Hapus Timezone
+            df['Date'] = pd.to_datetime(df['Date']).dt.tz_localize(None)
+
+            # Simpan Backup (Timpa file lama agar fresh)
+            try:
+                df.to_csv("eth_backup.csv", index=False)
+            except:
+                pass
+            
+            return df, "online"
+
+    except Exception as e:
+        print(f"Gagal Online: {e}")
+
+    # 2. COBA BACKUP (JIKA ONLINE GAGAL)
+    try:
+        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
+        if "Date" in df_backup.columns:
+            df_backup["Date"] = pd.to_datetime(df_backup["Date"])
+        elif df_backup.columns[0].lower() == "date":
+             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 = []
+    warnings = []
+
+    if not isinstance(scaler, MinMaxScaler):
+        warnings.append(f"Scaler bukan MinMaxScaler (terdeteksi: {type(scaler).__name__}). Pastikan ini scaler training.")
+
+    required_attrs = ["n_features_in_", "data_min_", "data_max_", "scale_", "min_"]
+    for a in required_attrs:
+        if not hasattr(scaler, a):
+            issues.append(f"Scaler belum ter-fit atau tidak valid (atribut '{a}' tidak ada).")
+
+    if hasattr(scaler, "n_features_in_"):
+        if int(scaler.n_features_in_) != 1:
+            issues.append(f"Scaler mengharapkan {scaler.n_features_in_} fitur, tapi aplikasi hanya pakai 1 fitur ('Close').")
+
+    ok = (len(issues) == 0)
+    return ok, issues, warnings
+
+
+def validate_model_input(model):
+    """Pastikan input model bentuknya (None, time_step, 1)."""
+    issues = []
+    try:
+        shape = model.input_shape  # biasanya (None, time_step, 1)
+        if not (isinstance(shape, (list, tuple)) and len(shape) == 3):
+            issues.append(f"input_shape tidak sesuai harapan: {shape} (harus 3 dimensi).")
+        else:
+            if shape[2] != 1:
+                issues.append(f"Jumlah fitur input model = {shape[2]}, tapi aplikasi membentuk fitur=1.")
+            if shape[1] is None or int(shape[1]) <= 1:
+                issues.append(f"time_step tidak valid: {shape[1]}.")
+    except Exception as e:
+        issues.append(f"Gagal membaca input_shape model: {e}")
+
+    ok = (len(issues) == 0)
+    return ok, issues
+
+
+@st.cache_resource
+def load_gru_assets():
+    """Load model & scaler hasil training (wajib pakai scaler yang sama)."""
+    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 = []
+
+    for _ in range(n_days):
+        x = seq.reshape(1, -1, 1)                    # (1, time_step, 1)
+        y = float(model.predict(x, verbose=0)[0, 0]) # output model (scaled)
+        preds.append(y)
+        seq = np.append(seq[1:], y)                  # autoregressive (murni)
+
+    return np.array(preds, dtype="float32").reshape(-1, 1)
+
+
+def create_combined_chart(df, start_date, future_dates, future_predictions):
+    """Membuat grafik gabungan dengan gaya yang sama seperti referensi."""
+    context_start_date = start_date - timedelta(days=609)
+    recent_df = df[(df["Date"] >= context_start_date) & (df["Date"] <= start_date)].copy()
+
+    window_size = 7
+    recent_df["Trend_Aktual"] = recent_df["Close"].rolling(window=window_size).mean()
+
+    predictions_flat = future_predictions.flatten()
+
+    fig, ax = plt.subplots(figsize=(14, 8))
+
+    
+    ax.plot(
+        recent_df["Date"], recent_df["Trend_Aktual"],
+        color="#1f77b4", linewidth=2.5, label="Tren Harga Aktual", alpha=0.9
+    )
+    # Membuat tren prediksi menggunakan moving average 7 hari
+    pred_trend = (
+        pd.Series(predictions_flat)
+        .rolling(window=7, min_periods=1)
+        .mean()
+    )
+
+    ax.plot(
+        future_dates,
+        pred_trend,
+        color="#d62728",
+        linewidth=2.5,
+        label="Tren Harga Prediksi (GRU)",
+        alpha=0.9
+    )
+    ax.scatter(
+        recent_df["Date"], recent_df["Close"],
+        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]
+        first_prediction_date = future_dates[0]
+        first_prediction_price = predictions_flat[0]
+
+        ax.plot(
+            [last_trend_date, first_prediction_date],
+            [last_trend_price, first_prediction_price],
+            color="#d62728", linestyle="--", linewidth=2.0, alpha=0.7
+        )
+    except Exception:
+        pass
+
+    ax.set_xlabel("Waktu", fontsize=12, fontweight="bold")
+    ax.set_ylabel("Harga (USD)", fontsize=12, fontweight="bold")
+    ax.set_title("Analisis Tren Historis dan Prediksi Harga Ethereum", fontsize=16, fontweight="bold", pad=20)
+
+    ax.xaxis.set_major_formatter(mdates.DateFormatter("%b %d, %Y"))
+    plt.xticks(rotation=45)
+    ax.grid(True, alpha=0.3, linestyle="--")
+
+    handles, labels = ax.get_legend_handles_labels()
+    order = [2, 0, 1, 3]
+    if len(handles) == 4:
+        ax.legend([handles[idx] for idx in order], [labels[idx] for idx in order], loc="upper left", fontsize=11)
+    else:
+        ax.legend(loc="upper left", fontsize=11)
+
+    plt.tight_layout()
+    ax.set_facecolor("#f8f9fa")
+    fig.patch.set_facecolor("white")
+
+    return fig
+
+
+# --- Tampilan Antarmuka Aplikasi ---
+
+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)
+
+df, data_source = load_eth_data()
+model, scaler = load_gru_assets()
+
+# LOGIC UI (TOAST/WARNING) DITARUH DI SINI (DILUAR CACHE)
+if data_source == "backup":
+    st.toast("Koneksi Yahoo lambat. Menggunakan data backup lokal.", icon="⚠️")
+elif data_source == "error":
+    st.error("❌ Gagal memuat data (Online gagal & Backup tidak ada).")
+    
+if df is not None and model is not None and scaler is not None:
+    # Menampilkan tabel data historis
+    st.markdown("<div class='card'>", unsafe_allow_html=True)
+    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.markdown("</div>", unsafe_allow_html=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)
+    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. 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)
+            
+            #Test 
+            st.subheader("📈 Grafik Detail Prediksi")
+
+            fig2, ax2 = plt.subplots(figsize=(12, 5))
+
+            ax2.plot(
+                future_dates,
+                future_predictions.flatten(),
+                marker='o',
+                linewidth=2.5
+            )
+
+            ax2.set_title(
+                "Grafik Detail Hasil Prediksi Harga Ethereum",
+                fontsize=14,
+                fontweight="bold"
+            )
+
+            ax2.set_xlabel("Tanggal")
+            ax2.set_ylabel("Harga Prediksi (USD)")
+
+            ax2.grid(True, alpha=0.3)
+
+            plt.xticks(rotation=45)
+            plt.tight_layout()
+
+            st.pyplot(fig2)
+
+            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

@@ -882,4 +882,27 @@ Date,Close,High,Low,Open,Volume
 2026-05-30,2019.458251953125,2028.4417724609375,2000.165771484375,2011.909423828125,7478416074
 2026-05-31,2004.341552734375,2034.1192626953125,1991.90673828125,2019.242431640625,9253505209
 2026-06-01,2003.22119140625,2017.6077880859375,1956.1522216796875,2004.2425537109375,19458810001
-2026-06-03,1848.1400146484375,1870.7220458984375,1848.860107421875,1857.32568359375,25659957248
+2026-06-02,1857.71630859375,2003.970703125,1838.05859375,2003.26025390625,25205401403
+2026-06-03,1811.7322998046875,1889.058837890625,1771.3145751953125,1857.6417236328125,24083350214
+2026-06-04,1769.58056640625,1817.3465576171875,1717.802490234375,1811.7425537109375,27112623897
+2026-06-05,1580.86083984375,1772.1077880859375,1540.1619873046875,1769.986083984375,39924177669
+2026-06-06,1568.7677001953125,1599.9534912109375,1506.5057373046875,1580.88037109375,18426248218
+2026-06-07,1686.396240234375,1714.970703125,1563.77587890625,1568.74951171875,15172606860
+2026-06-08,1690.1485595703125,1713.4710693359375,1644.79150390625,1686.251220703125,17427269148
+2026-06-09,1637.7066650390625,1695.52294921875,1613.834228515625,1690.21337890625,15196855528
+2026-06-10,1620.1376953125,1665.7607421875,1603.8258056640625,1637.6348876953125,12655731412
+2026-06-11,1672.2806396484375,1690.4488525390625,1620.08447265625,1620.1400146484375,12379882531
+2026-06-12,1665.1278076171875,1689.4443359375,1650.628662109375,1671.8189697265625,9989137815
+2026-06-13,1680.214599609375,1693.603271484375,1661.609130859375,1665.0487060546875,6101094488
+2026-06-14,1724.61328125,1729.350830078125,1654.2105712890625,1680.146484375,7804982667
+2026-06-15,1794.961181640625,1847.769775390625,1709.253662109375,1724.5716552734375,17921331594
+2026-06-16,1790.398193359375,1837.2041015625,1758.130126953125,1794.93798828125,14698492893
+2026-06-17,1747.885498046875,1807.2828369140625,1724.7178955078125,1790.396240234375,14185457929
+2026-06-18,1709.533447265625,1760.880859375,1670.1038818359375,1747.8778076171875,12942260783
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