new

app.py

@@ -1,3 +1,4 @@
+import requests
 import streamlit as st
 import joblib
 import pandas as pd
@@ -11,262 +12,428 @@ import matplotlib.pyplot as plt
 import matplotlib.dates as mdates
 from matplotlib import rcParams
 
-# Set font
+# 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="🪙")
+st.set_page_config(
+    page_title="Prediksi Harga Ethereum (GRU)",
+    page_icon="🪙"
+)
 
-# --- GLOBAL STYLES ---
+# --- 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;
+  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;
+  -webkit-background-clip: text;
+  -webkit-text-fill-color: transparent;
   margin: 0.25rem 0 0.5rem 0;
 }
-.app-subtitle { text-align:center; color: #6b7280; margin-bottom: 1.25rem; }
+
-.card {
+/* subtitle */
-  background: #ffffff; border: 1px solid #e5e7eb; border-radius: 16px;
+.app-subtitle {
-  padding: 1rem 1.25rem; box-shadow: 0 6px 20px rgba(0,0,0,0.05); margin-bottom: 1rem;
+  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;
 }
-.h-section { font-weight:700; font-size: 20px; margin: 0 0 .5rem 0; }
-.footer { text-align:center; color: #6b7280; font-size: 13px; margin-top: 2rem; }
 </style>
 """, unsafe_allow_html=True)
 
-# --- TOMBOL RESET CACHE (PENTING!) ---
-with st.sidebar:
-    st.header("⚙️ Kontrol Data")
-    if st.button("🔄 Paksa Update Data (Clear Cache)"):
-        st.cache_data.clear()
-        st.success("Cache dihapus! Silakan tekan 'R' untuk reload.")
-        st.stop() # Hentikan app sebentar biar user reload
 
-# --- FUNGSI LOAD DATA (VERSI YFINANCE ONLY) ---
+# --- Fungsi-fungsi Bantuan ---
+
 @st.cache_data(ttl="1h") 
 def load_eth_data():
-    """
-    Fokus: Download via Library yfinance.
-    Fitur: 
-    1. Mengatasi data bolong (Resample Daily).
-    2. Auto Adjust harga (OHLC bersih).
-    """
     ticker = "ETH-USD"
+    df = None
     
-    # Kita set start date agak jauh biar grafiknya bagus
+    # 1. COBA ONLINE
-    start_date = "2024-01-01"
-    end_date = date.today() + timedelta(days=1)
-    
-    st.toast("Sedang menghubungi server Yahoo Finance...", icon="⏳")
-    
     try:
-        # DOWNLOAD ONLINE
+        
         df = yf.download(
             ticker, 
-            start=start_date, 
+            start="2024-01-01", 
-            end=end_date, 
+            end=date.today() + timedelta(days=1), 
             progress=False,
-            auto_adjust=True, # Biar harga bersih
+            auto_adjust=True,
             multi_level_index=False 
         )
         
         if df is not None and not df.empty:
-            # 1. Bersihkan Index
+            # Bersihkan Index & Kolom
             df = df.reset_index()
             
-            # 2. Rapikan Kolom (Cegah MultiIndex/Tuple)
             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
             
-            # 3. Pastikan kolom Date ada
+            # Pastikan kolom pertama adalah Date
             if 'Date' not in df.columns:
                 df = df.rename(columns={df.columns[0]: 'Date'})
-            
-            # 4. Hapus Timezone
-            df['Date'] = pd.to_datetime(df['Date']).dt.tz_localize(None)
-            
-            # --- BAGIAN PENTING: TAMBAL DATA BOLONG (RESAMPLING) ---
-            # Ini mengatasi masalah "Loncat" dari tgl 15 ke 17.
-            # Kita paksa buat tanggal harian (Daily) lengkap.
-            df = df.sort_values('Date').set_index('Date')
-            
-            # Buat range tanggal penuh dari awal sampai akhir data
-            full_idx = pd.date_range(start=df.index.min(), end=df.index.max(), freq='D')
-            
-            # Reindex & Forward Fill (Isi kekosongan dengan data hari sebelumnya)
-            df = df.reindex(full_idx).ffill().reset_index()
-            df = df.rename(columns={'index': 'Date'})
-            # -------------------------------------------------------
 
-            # 5. Simpan Backup Otomatis
+            # 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"Error yfinance: {e}")
 
-    # FALLBACK: JIKA DOWNLOAD GAGAL, BACA BACKUP LAMA
+    except Exception as e:
+        print(f"Gagal Online: {e}")
+
+    # 2. COBA BACKUP (JIKA ONLINE GAGAL)
     try:
         df_backup = pd.read_csv("eth_backup.csv")
-        if "Unnamed: 0" in df_backup.columns: df_backup = df_backup.drop(columns=["Unnamed: 0"])
-        if "Date" not in df_backup.columns: df_backup = df_backup.rename(columns={df_backup.columns[0]: "Date"})
-        df_backup["Date"] = pd.to_datetime(df_backup["Date"]).dt.tz_localize(None)
         
+        # 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:
+        
+    except FileNotFoundError:
+        # 3. GAGAL TOTAL
         return None, "error"
-
+    
-
-# --- Fungsi Helper Lainnya (Model & Scaler) ---
 def validate_scaler(scaler):
-    issues, warnings = [], []
+    """Validasi scaler agar konsisten dengan training."""
-    if not isinstance(scaler, MinMaxScaler): warnings.append(f"Scaler bukan MinMaxScaler.")
+    issues = []
-    return (len(issues)==0), issues, warnings
+    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
+        shape = model.input_shape  # biasanya (None, time_step, 1)
-        if shape[2] != 1: issues.append(f"Fitur model = {shape[2]}, input = 1.")
+        if not (isinstance(shape, (list, tuple)) and len(shape) == 3):
-    except: pass
+            issues.append(f"input_shape tidak sesuai harapan: {shape} (harus 3 dimensi).")
-    return (len(issues)==0), issues
+        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(): return None, None
+
+    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: return None, None
+
+    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)
+  
+    
+    seq = np.array(initial_sequence_scaled, dtype="float32").reshape(-1)  # (time_step,)
     preds = []
+
     for _ in range(n_days):
-        x = seq.reshape(1, -1, 1)
+        x = seq.reshape(1, -1, 1)                    # (1, time_step, 1)
-        y = float(model.predict(x, verbose=0)[0, 0])
+        y = float(model.predict(x, verbose=0)[0, 0]) # output model (scaled)
         preds.append(y)
-        seq = np.append(seq[1:], 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):
-    context_start = start_date - timedelta(days=60)
-    recent_df = df[(df["Date"] >= context_start) & (df["Date"] <= start_date)].copy()
-    recent_df["Trend"] = recent_df["Close"].rolling(window=7).mean()
-    
-    fig, ax = plt.subplots(figsize=(14, 8))
-    ax.plot(recent_df["Date"], recent_df["Close"], color="gray", alpha=0.5, label="Harga Aktual")
-    ax.plot(recent_df["Date"], recent_df["Trend"], color="#1f77b4", linewidth=2, label="Tren Aktual")
-    ax.plot(future_dates, future_predictions.flatten(), color="#d62728", linewidth=2, marker="o", markersize=4, label="Prediksi GRU")
-    
-    if not recent_df.empty:
-        ax.plot([recent_df["Date"].iloc[-1], future_dates[0]], 
-                [recent_df["Trend"].dropna().iloc[-1], future_predictions.flatten()[0]], 
-                color="#d62728", linestyle="--", alpha=0.7)
 
-    ax.set_title("Analisis Tren & Prediksi Ethereum")
+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=60)
+    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["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
+    )
+    ax.plot(
+        future_dates, predictions_flat,
+        color="#d62728", linewidth=2.5, label="Harga Prediksi (GRU)",
+        alpha=0.9, marker="o", markersize=4
+    )
+    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.legend()
+    ax.grid(True, alpha=0.3, linestyle="--")
-    ax.grid(True, alpha=0.3)
+
+    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
 
 
-# --- MAIN UI ---
+# --- 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)
 
-# LOAD DATA
+df, data_source = load_eth_data()
-df, status = load_eth_data()
-
-# Notifikasi Status
-if status == "backup":
-    st.toast("Koneksi Yahoo Gagal. Pakai Backup.", icon="⚠️")
-elif status == "error":
-    st.error("❌ Gagal memuat data (Library gagal & Backup tidak ada).")
-    st.stop()
-
-# LOAD MODEL
 model, scaler = load_gru_assets()
 
-if df is not None and model is not None and scaler is not None:
+# 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).")
     
-    # INFO UPDATE DATA
+if df is not None and model is not None and scaler is not None:
-    last_date = df["Date"].max()
+    # Menampilkan tabel data historis
-    st.info(f"📅 Data Terupdate sampai: **{last_date.strftime('%d %B %Y')}**")
-
-    # 1. Tampilkan Data
     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.tail(10).sort_values("Date", ascending=False), height=300, use_container_width=True) # Tampilkan 10 data terakhir biar kelihatan update
+    st.dataframe(df, height=300, use_container_width=True)
     st.markdown("</div>", unsafe_allow_html=True)
 
-    # 2. Chart Historis
+    # 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.set_index("Date")["Close"])
+    st.line_chart(df.rename(columns={"Date": "index"}).set_index("index")["Close"])
     st.markdown("</div>", unsafe_allow_html=True)
 
-    # 3. Prediksi
     st.markdown("<div class='card'>", unsafe_allow_html=True)
-    st.markdown("<div class='h-section'>🎯 Mulai Prediksi</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])
-    min_date = (df["Date"].min() + timedelta(days=time_step)).date()
+    min_selectable_date = (df["Date"].min() + timedelta(days=time_step)).date()
-    max_date = df["Date"].max().date()
+    max_selectable_date = df["Date"].max().date()
 
-    c1, c2 = st.columns(2)
+    col1, col2 = st.columns(2)
-    with c1:
+    with col1:
-        # Default value ke hari ini (max_date)
+        selected_date = st.date_input(
-        start_date = st.date_input("Mulai tanggal:", value=max_date, min_value=min_date, max_value=max_date)
+            "Pilih tanggal mulai prediksi:",
-    with c2:
+            value=max_selectable_date,
-        days = st.slider("Jumlah hari:", 1, 30, 15)
+            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"):
+    if st.button("Buat Prediksi", key="predict_button"):
-        with st.spinner("Memproses..."):
+        if prediction_days < 1:
-            vals = df[["Close"]].values.astype(float)
+            st.warning("Jumlah hari prediksi minimal **1**. Silakan geser slidernya dulu. 🙂")
-            scaled = scaler.transform(vals)
+            st.stop()
-            
+
-            check_date = pd.to_datetime(start_date)
+        with st.spinner("Memproses dan menjalankan prediksi..."):
-            # Cari index tanggal
+            # scaling (mengikuti scaler training)
-            idx = df[df["Date"].dt.date == check_date.date()].index
+            close_values = df[["Close"]].values.astype(float)
-            
+            scaled_data = scaler.transform(close_values)
-            if len(idx) > 0:
+
-                idx = idx[0]
+            # Cocokkan berdasarkan tanggal (robust)
-                if idx < time_step:
+            selected_date_dt = pd.to_datetime(selected_date)
-                     st.error(f"Data tidak cukup (butuh {time_step} hari sebelumnya).")
+            mask = df["Date"].dt.date == selected_date_dt.date()
-                else:
+            idxs = np.where(mask.to_numpy())[0]
-                    seq = scaled[idx-time_step : idx]
+
-                    pred_scaled = predict_from_sequence_pure(model, seq, days)
+            if len(idxs) == 0:
-                    pred_real = scaler.inverse_transform(pred_scaled)
+                st.error(f"Tanggal {selected_date_dt.strftime('%Y-%m-%d')} tidak ditemukan dalam dataset.")
-                    
+                st.stop()
-                    f_dates = [check_date + timedelta(days=i) for i in range(1, days+1)]
+
-                    
+            start_index = int(idxs[0])
-                    st.markdown("<div class='card'>", unsafe_allow_html=True)
+
-                    fig = create_combined_chart(df, check_date, f_dates, pred_real)
+            if start_index - time_step < 0:
-                    st.pyplot(fig)
+                st.error(
-                    
+                    f"Tanggal terlalu awal untuk diprediksi. "
-                    res_df = pd.DataFrame({"Tanggal": [d.strftime("%Y-%m-%d") for d in f_dates], "Harga (USD)": pred_real.flatten()})
+                    f"Butuh {time_step} hari data sebelumnya. Pilih tanggal setelah "
-                    st.dataframe(res_df, use_container_width=True)
+                    f"{(df['Date'].min() + timedelta(days=time_step)).strftime('%Y-%m-%d')}."
-                    st.markdown("</div>", unsafe_allow_html=True)
+                )
-            else:
+                st.stop()
-                st.error("Tanggal tidak ditemukan dalam dataset.")
+
+            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/Scaler. Pastikan file .h5 dan .pkl ada.")
+    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

@@ -709,3 +709,10 @@ Date,Close,High,Low,Open,Volume
 2025-12-08,3125.197998046875,3177.86767578125,3043.703857421875,3061.013671875,25262165670
 2025-12-09,3321.114990234375,3395.838623046875,3092.87646484375,3124.942626953125,32012553374
 2025-12-10,3325.39013671875,3446.622802734375,3290.14697265625,3321.2041015625,30694387989
+2025-12-11,3237.056884765625,3327.344482421875,3149.034423828125,3324.38671875,29066243707
+2025-12-12,3084.172607421875,3265.37255859375,3050.267333984375,3237.025634765625,24391003174
+2025-12-13,3116.6953125,3134.849365234375,3080.07861328125,3084.129638671875,9916869400
+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
+2025-12-17,2933.59375,2969.88525390625,2920.556884765625,2962.631103515625,19631171584