new

app.py

@@ -1,4 +1,3 @@
-import requests
 import streamlit as st
 import joblib
 import pandas as pd
@@ -12,461 +11,262 @@ import matplotlib.pyplot as plt
 import matplotlib.dates as mdates
 from matplotlib import rcParams
 
-# Set font untuk mendukung karakter Indonesia
+# Set font
 rcParams['font.family'] = 'DejaVu Sans'
 
 # --- Konfigurasi Halaman ---
-st.set_page_config(
+st.set_page_config(page_title="Prediksi Harga Ethereum (GRU)", page_icon="🪙")
-    page_title="Prediksi Harga Ethereum (GRU)",
-    page_icon="🪙"
-)
 
-# --- GLOBAL STYLES (UI only) ---
+# --- GLOBAL STYLES ---
 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;
+  text-align:center; font-weight:800; font-size: 32px;
-  font-weight:800;
-  font-size: 32px;
   background: linear-gradient(90deg, #6EE7F9 0%, #7C3AED 50%, #F59E0B 100%);
-  -webkit-background-clip: text;
+  -webkit-background-clip: text; -webkit-text-fill-color: transparent;
-  -webkit-text-fill-color: transparent;
   margin: 0.25rem 0 0.5rem 0;
 }
-
+.app-subtitle { text-align:center; color: #6b7280; margin-bottom: 1.25rem; }
-/* subtitle */
-.app-subtitle {
-  text-align:center;
-  color: var(--muted);
-  margin-bottom: 1.25rem;
-}
-
-/* card container */
 .card {
-  background: var(--card-bg);
+  background: #ffffff; border: 1px solid #e5e7eb; border-radius: 16px;
-  border: 1px solid var(--ring);
+  padding: 1rem 1.25rem; box-shadow: 0 6px 20px rgba(0,0,0,0.05); margin-bottom: 1rem;
-  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-fungsi Bantuan ---
+# --- FUNGSI LOAD DATA (VERSI YFINANCE ONLY) ---
-
 @st.cache_data(ttl="1h") 
 def load_eth_data():
     """
-    Metode Hybrid (Tumpuk Data) dengan Debug Info.
+    Fokus: Download via Library yfinance.
+    Fitur: 
+    1. Mengatasi data bolong (Resample Daily).
+    2. Auto Adjust harga (OHLC bersih).
     """
     ticker = "ETH-USD"
-    df_final = None
     
-    # --- BAGIAN 1: BACA DATA LAMA (BASE) ---
+    # Kita set start date agak jauh biar grafiknya bagus
+    start_date = "2024-01-01"
+    end_date = date.today() + timedelta(days=1)
+    
+    st.toast("Sedang menghubungi server Yahoo Finance...", icon="⏳")
+    
     try:
-        df_base = pd.read_csv("eth_backup.csv")
+        # DOWNLOAD ONLINE
-        
+        df = yf.download(
-        # Bersihkan kolom sampah
-        if "Unnamed: 0" in df_base.columns:
-            df_base = df_base.drop(columns=["Unnamed: 0"])
-        
-        # Standarisasi kolom Date
-        if "Date" not in df_base.columns:
-             df_base = df_base.rename(columns={df_base.columns[0]: "Date"})
-        
-        df_base["Date"] = pd.to_datetime(df_base["Date"]).dt.tz_localize(None)
-        
-    except FileNotFoundError:
-        df_base = pd.DataFrame(columns=["Date", "Open", "High", "Low", "Close", "Volume"])
-
-    # --- BAGIAN 2: HITUNG TANGGAL DOWNLOAD ---
-    today = date.today()
-    
-    if not df_base.empty:
-        last_date_csv = df_base["Date"].max()
-        start_download = last_date_csv + timedelta(days=1)
-    else:
-        start_download = pd.to_datetime("2020-01-01")
-        last_date_csv = "Kosong"
-
-    # DEBUG INFO DI SIDEBAR (Supaya kelihatan prosesnya)
-    with st.sidebar:
-        st.write(f"📂 **Status Data:**")
-        st.write(f"- Terakhir di CSV: `{str(last_date_csv).split()[0]}`")
-        st.write(f"- Target Download: `{str(start_download.date())}` s.d `{today}`")
-
-    # --- BAGIAN 3: DOWNLOAD & GABUNG ---
-    # Hanya download jika tanggal download <= hari ini
-    if start_download.date() <= today:
-        try:
-            df_new = yf.download(
             ticker, 
-                start=start_download, 
+            start=start_date, 
-                end=today + timedelta(days=1), 
+            end=end_date, 
             progress=False,
-                auto_adjust=True,
+            auto_adjust=True, # Biar harga bersih
             multi_level_index=False 
         )
         
-            if df_new is not None and not df_new.empty:
+        if df is not None and not df.empty:
-                df_new = df_new.reset_index()
+            # 1. Bersihkan Index
+            df = df.reset_index()
             
-                # Rapikan kolom
+            # 2. Rapikan Kolom (Cegah MultiIndex/Tuple)
             new_cols = []
-                for col in df_new.columns:
+            for col in df.columns:
                 col_name = col[0] if isinstance(col, tuple) else str(col)
                 new_cols.append(col_name)
-                df_new.columns = new_cols
+            df.columns = new_cols
             
-                if 'Date' not in df_new.columns:
+            # 3. Pastikan kolom Date ada
-                    df_new = df_new.rename(columns={df_new.columns[0]: 'Date'})
+            if 'Date' not in df.columns:
+                df = df.rename(columns={df.columns[0]: 'Date'})
             
-                df_new['Date'] = pd.to_datetime(df_new['Date']).dt.tz_localize(None)
+            # 4. Hapus Timezone
+            df['Date'] = pd.to_datetime(df['Date']).dt.tz_localize(None)
             
-                # INFO DEBUG
+            # --- BAGIAN PENTING: TAMBAL DATA BOLONG (RESAMPLING) ---
-                st.sidebar.success(f"✅ Berhasil download {len(df_new)} hari baru!")
+            # Ini mengatasi masalah "Loncat" dari tgl 15 ke 17.
+            # Kita paksa buat tanggal harian (Daily) lengkap.
+            df = df.sort_values('Date').set_index('Date')
             
-                # GABUNGKAN
+            # Buat range tanggal penuh dari awal sampai akhir data
-                df_final = pd.concat([df_base, df_new], ignore_index=True)
+            full_idx = pd.date_range(start=df.index.min(), end=df.index.max(), freq='D')
             
-            else:
+            # Reindex & Forward Fill (Isi kekosongan dengan data hari sebelumnya)
-                st.sidebar.warning("⚠️ Download dijalankan tapi data kosong (Mungkin libur/belum close).")
+            df = df.reindex(full_idx).ffill().reset_index()
-                df_final = df_base
+            df = df.rename(columns={'index': 'Date'})
+            # -------------------------------------------------------
+
+            # 5. Simpan Backup Otomatis
+            try:
+                df.to_csv("eth_backup.csv", index=False)
+            except:
+                pass
+            
+            return df, "online"
             
     except Exception as e:
-            st.sidebar.error(f"❌ Gagal update online: {e}")
+        print(f"Error yfinance: {e}")
-            df_final = df_base 
-    else:
-        st.sidebar.info("✅ Data CSV sudah paling update.")
-        df_final = df_base
 
-    # --- FINALISASI ---
+    # FALLBACK: JIKA DOWNLOAD GAGAL, BACA BACKUP LAMA
-    if df_final is not None and not df_final.empty:
+    try:
-        df_final = df_final.drop_duplicates(subset="Date", keep="last")
+        df_backup = pd.read_csv("eth_backup.csv")
-        df_final = df_final.sort_values("Date").reset_index(drop=True)
+        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"})
-        target_cols = ['Date', 'Open', 'High', 'Low', 'Close', 'Volume']
+        df_backup["Date"] = pd.to_datetime(df_backup["Date"]).dt.tz_localize(None)
-        available = [c for c in target_cols if c in df_final.columns]
-        df_final = df_final[available]
-
-        return df_final, "mixed"
         
+        return df_backup, "backup"
+    except:
         return None, "error"
 
+
+# --- Fungsi Helper Lainnya (Model & Scaler) ---
 def validate_scaler(scaler):
-    """Validasi scaler agar konsisten dengan training."""
+    issues, warnings = [], []
-    issues = []
+    if not isinstance(scaler, MinMaxScaler): warnings.append(f"Scaler bukan MinMaxScaler.")
-    warnings = []
+    return (len(issues)==0), 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)
+        shape = model.input_shape
-        if not (isinstance(shape, (list, tuple)) and len(shape) == 3):
+        if shape[2] != 1: issues.append(f"Fitur model = {shape[2]}, input = 1.")
-            issues.append(f"input_shape tidak sesuai harapan: {shape} (harus 3 dimensi).")
+    except: pass
-        else:
+    return (len(issues)==0), issues
-            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)                    # (1, time_step, 1)
+        x = seq.reshape(1, -1, 1)
-        y = float(model.predict(x, verbose=0)[0, 0]) # output model (scaled)
+        y = float(model.predict(x, verbose=0)[0, 0])
         preds.append(y)
-        seq = np.append(seq[1:], y)                  # autoregressive (murni)
+        seq = np.append(seq[1:], y)
-
     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 = start_date - timedelta(days=60)
-    context_start_date = start_date - timedelta(days=60)
+    recent_df = df[(df["Date"] >= context_start) & (df["Date"] <= start_date)].copy()
-    recent_df = df[(df["Date"] >= context_start_date) & (df["Date"] <= start_date)].copy()
+    recent_df["Trend"] = recent_df["Close"].rolling(window=7).mean()
-
-    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", 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")
     
-    ax.plot(
+    if not recent_df.empty:
-        recent_df["Date"], recent_df["Close"],
+        ax.plot([recent_df["Date"].iloc[-1], future_dates[0]], 
-        color="gray", linewidth=1.0, label="Harga Aktual (Harian)", alpha=0.5
+                [recent_df["Trend"].dropna().iloc[-1], future_predictions.flatten()[0]], 
-    )
+                color="#d62728", linestyle="--", alpha=0.7)
-    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.set_title("Analisis Tren & Prediksi Ethereum")
     ax.xaxis.set_major_formatter(mdates.DateFormatter("%b %d, %Y"))
     plt.xticks(rotation=45)
-    ax.grid(True, alpha=0.3, linestyle="--")
+    ax.legend()
-
+    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
 
 
-# --- Tampilan Antarmuka Aplikasi ---
+# --- MAIN UI ---
 
 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()
+# LOAD 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()
 
-# 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
+    
+    # INFO UPDATE DATA
+    last_date = df["Date"].max()
+    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, height=300, use_container_width=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.markdown("</div>", unsafe_allow_html=True)
 
-    # Menampilkan visualisasi line chart
+    # 2. Chart Historis
     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.line_chart(df.set_index("Date")["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 Berdasarkan Tanggal</div>", unsafe_allow_html=True)
+    st.markdown("<div class='h-section'>🎯 Mulai Prediksi</div>", unsafe_allow_html=True)
     
     time_step = int(model.input_shape[1])
-    min_selectable_date = (df["Date"].min() + timedelta(days=time_step)).date()
+    min_date = (df["Date"].min() + timedelta(days=time_step)).date()
-    max_selectable_date = df["Date"].max().date()
+    max_date = df["Date"].max().date()
 
-    col1, col2 = st.columns(2)
+    c1, c2 = st.columns(2)
-    with col1:
+    with c1:
-        selected_date = st.date_input(
+        # Default value ke hari ini (max_date)
-            "Pilih tanggal mulai prediksi:",
+        start_date = st.date_input("Mulai tanggal:", value=max_date, min_value=min_date, max_value=max_date)
-            value=max_selectable_date,
+    with c2:
-            min_value=min_selectable_date,
+        days = st.slider("Jumlah hari:", 1, 30, 15)
-            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 st.button("Buat Prediksi"):
-        if prediction_days < 1:
+        with st.spinner("Memproses..."):
-            st.warning("Jumlah hari prediksi minimal **1**. Silakan geser slidernya dulu. 🙂")
+            vals = df[["Close"]].values.astype(float)
-            st.stop()
+            scaled = scaler.transform(vals)
             
-        with st.spinner("Memproses dan menjalankan prediksi..."):
+            check_date = pd.to_datetime(start_date)
-            # scaling (mengikuti scaler training)
+            # Cari index tanggal
-            close_values = df[["Close"]].values.astype(float)
+            idx = df[df["Date"].dt.date == check_date.date()].index
-            scaled_data = scaler.transform(close_values)
             
-            # Cocokkan berdasarkan tanggal (robust)
+            if len(idx) > 0:
-            selected_date_dt = pd.to_datetime(selected_date)
+                idx = idx[0]
-            mask = df["Date"].dt.date == selected_date_dt.date()
+                if idx < time_step:
-            idxs = np.where(mask.to_numpy())[0]
+                     st.error(f"Data tidak cukup (butuh {time_step} hari sebelumnya).")
+                else:
+                    seq = scaled[idx-time_step : idx]
+                    pred_scaled = predict_from_sequence_pure(model, seq, days)
+                    pred_real = scaler.inverse_transform(pred_scaled)
                     
-            if len(idxs) == 0:
+                    f_dates = [check_date + timedelta(days=i) for i in range(1, days+1)]
-                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, check_date, f_dates, pred_real)
-            fig = create_combined_chart(df, selected_date_dt, future_dates, future_predictions)
                     st.pyplot(fig)
+                    
+                    res_df = pd.DataFrame({"Tanggal": [d.strftime("%Y-%m-%d") for d in f_dates], "Harga (USD)": pred_real.flatten()})
+                    st.dataframe(res_df, use_container_width=True)
                     st.markdown("</div>", unsafe_allow_html=True)
-
+            else:
-            st.markdown("<div class='card'>", unsafe_allow_html=True)
+                st.error("Tanggal tidak ditemukan dalam dataset.")
-            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.")
+    st.error("Gagal memuat Model/Scaler. Pastikan file .h5 dan .pkl ada.")
-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.")