From bb8fa425850adf3ea2f7dec60dd8a0b14f81745d Mon Sep 17 00:00:00 2001 From: Azmikun1 Date: Wed, 17 Dec 2025 15:30:38 +0700 Subject: [PATCH] new --- app.py | 483 +++++++++++++++++++++++++++++++++---------------- eth_backup.csv | 7 + 2 files changed, 332 insertions(+), 158 deletions(-) diff --git a/app.py b/app.py index c3545b2..cad3c80 100644 --- a/app.py +++ b/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="๐Ÿช™") +st.set_page_config( + page_title="Prediksi Harga Ethereum (GRU)", + page_icon="๐Ÿช™" +) -# --- GLOBAL STYLES --- +# --- GLOBAL STYLES (UI only) --- st.markdown(""" """, unsafe_allow_html=True) -# --- TOMBOL RESET CACHE (PENTING!) --- -with st.sidebar: - st.header("โš™๏ธ Kontrol Data") - if st.button("๐Ÿ”„ 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 - start_date = "2024-01-01" - end_date = date.today() + timedelta(days=1) - - st.toast("Sedang menghubungi server Yahoo Finance...", icon="โณ") - + # 1. COBA ONLINE try: - # DOWNLOAD ONLINE + df = yf.download( ticker, - start=start_date, - end=end_date, + start="2024-01-01", + 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 = [], [] - if not isinstance(scaler, MinMaxScaler): warnings.append(f"Scaler bukan MinMaxScaler.") - return (len(issues)==0), issues, warnings + """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 - if shape[2] != 1: issues.append(f"Fitur model = {shape[2]}, input = 1.") - except: pass - return (len(issues)==0), issues + 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(): 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("โš ๏ธ " + w) + if not ok_scaler: + st.error("โŒ 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("โŒ 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) - y = float(model.predict(x, verbose=0)[0, 0]) + 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) + 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) + 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 -# --- MAIN UI --- +# --- Tampilan Antarmuka Aplikasi --- st.markdown("
Prediksi Harga Ethereum (GRU)
", unsafe_allow_html=True) st.markdown("
Data historis & prediksi ETH-USD berbasis GRU
", unsafe_allow_html=True) -# LOAD DATA -df, status = load_eth_data() - -# Notifikasi Status -if status == "backup": - st.toast("Koneksi Yahoo Gagal. Pakai Backup.", icon="โš ๏ธ") -elif status == "error": - st.error("โŒ Gagal memuat data (Library gagal & Backup tidak ada).") - st.stop() - -# LOAD MODEL +df, data_source = load_eth_data() 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="โš ๏ธ") +elif data_source == "error": + st.error("โŒ Gagal memuat data (Online gagal & Backup tidak ada).") - # INFO UPDATE DATA - last_date = df["Date"].max() - st.info(f"๐Ÿ“… Data Terupdate sampai: **{last_date.strftime('%d %B %Y')}**") - - # 1. Tampilkan Data +if df is not None and model is not None and scaler is not None: + # Menampilkan tabel data historis st.markdown("
", unsafe_allow_html=True) st.markdown("
๐Ÿ“š Data Historis Harga Ethereum
", 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("
", unsafe_allow_html=True) - # 2. Chart Historis + # Menampilkan visualisasi line chart st.markdown("
", unsafe_allow_html=True) st.markdown("
๐Ÿ“ˆ Visualisasi Harga Historis
", 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("
", unsafe_allow_html=True) - # 3. Prediksi st.markdown("
", unsafe_allow_html=True) - st.markdown("
๐ŸŽฏ Mulai Prediksi
", unsafe_allow_html=True) - + st.markdown("
๐ŸŽฏ Mulai Prediksi Berdasarkan Tanggal
", unsafe_allow_html=True) + time_step = int(model.input_shape[1]) - min_date = (df["Date"].min() + timedelta(days=time_step)).date() - max_date = df["Date"].max().date() + min_selectable_date = (df["Date"].min() + timedelta(days=time_step)).date() + max_selectable_date = df["Date"].max().date() - c1, c2 = st.columns(2) - with c1: - # Default value ke hari ini (max_date) - start_date = st.date_input("Mulai tanggal:", value=max_date, min_value=min_date, max_value=max_date) - with c2: - days = st.slider("Jumlah hari:", 1, 30, 15) + 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"): - with st.spinner("Memproses..."): - vals = df[["Close"]].values.astype(float) - scaled = scaler.transform(vals) - - check_date = pd.to_datetime(start_date) - # Cari index tanggal - idx = df[df["Date"].dt.date == check_date.date()].index - - if len(idx) > 0: - idx = idx[0] - if idx < time_step: - 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) - - f_dates = [check_date + timedelta(days=i) for i in range(1, days+1)] - - st.markdown("
", unsafe_allow_html=True) - fig = create_combined_chart(df, check_date, f_dates, pred_real) - 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("
", unsafe_allow_html=True) - else: - st.error("Tanggal tidak ditemukan dalam dataset.") + if st.button("Buat Prediksi", key="predict_button"): + if prediction_days < 1: + st.warning("Jumlah hari prediksi minimal **1**. Silakan geser slidernya dulu. ๐Ÿ™‚") + 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) + + # โœ… Prediksi MURNI dari model + future_predictions_scaled = predict_from_sequence_pure(model, initial_sequence, prediction_days) + + # โœ… Inverse transform MURNI dari scaler + future_predictions = scaler.inverse_transform(future_predictions_scaled) + + # sanity-check + if not np.isfinite(future_predictions).all(): + st.error("โŒ 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("
", unsafe_allow_html=True) + st.subheader("๐Ÿ“Š Grafik Prediksi (Historis + Prediksi)") + fig = create_combined_chart(df, selected_date_dt, future_dates, future_predictions) + st.pyplot(fig) + st.markdown("
", unsafe_allow_html=True) + + st.markdown("
", unsafe_allow_html=True) + st.subheader("๐Ÿงพ 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("
", 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""" +๐Ÿ“Š **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("
", unsafe_allow_html=True) st.markdown("
Dibuat oleh Muhammad Gilman Nadhif Azmi
", unsafe_allow_html=True) elif model is None: - st.error("Gagal memuat Model/Scaler. Pastikan file .h5 dan .pkl ada.") \ No newline at end of file + 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.") diff --git a/eth_backup.csv b/eth_backup.csv index 93f7dc6..af64da3 100644 --- a/eth_backup.csv +++ b/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