437 lines
15 KiB
Python
437 lines
15 KiB
Python
import requests
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import streamlit as st
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import joblib
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import pandas as pd
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import numpy as np
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from sklearn.preprocessing import MinMaxScaler
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from tensorflow.keras.models import load_model
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from datetime import date, timedelta
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import yfinance as yf
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from pathlib import Path
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import matplotlib.pyplot as plt
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import matplotlib.dates as mdates
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from matplotlib import rcParams
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# Set font untuk mendukung karakter Indonesia
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rcParams['font.family'] = 'DejaVu Sans'
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# --- Konfigurasi Halaman ---
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st.set_page_config(
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page_title="Prediksi Harga Ethereum (GRU)",
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page_icon="🪙"
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)
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# --- GLOBAL STYLES (UI only) ---
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st.markdown("""
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<style>
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/* font & warna dasar */
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html, body, [class*="css"] { font-family: "Inter", "DejaVu Sans", sans-serif; }
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:root {
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--card-bg: #ffffff;
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--muted: #6b7280;
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--ring: #e5e7eb;
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}
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/* gradient title */
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.app-title {
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text-align:center;
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font-weight:800;
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font-size: 32px;
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background: linear-gradient(90deg, #6EE7F9 0%, #7C3AED 50%, #F59E0B 100%);
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-webkit-background-clip: text;
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-webkit-text-fill-color: transparent;
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margin: 0.25rem 0 0.5rem 0;
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}
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/* subtitle */
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.app-subtitle {
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text-align:center;
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color: var(--muted);
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margin-bottom: 1.25rem;
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}
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/* card container */
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.card {
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background: var(--card-bg);
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border: 1px solid var(--ring);
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border-radius: 16px;
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padding: 1rem 1.25rem;
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box-shadow: 0 6px 20px rgba(0,0,0,0.05);
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margin-bottom: 1rem;
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}
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/* section heading */
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.h-section {
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font-weight:700;
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font-size: 20px;
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margin: 0 0 .5rem 0;
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}
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/* table tweaks */
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.dataframe tbody tr:hover { background-color: #fafafa; }
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/* footer */
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.footer {
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text-align:center;
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color: var(--muted);
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font-size: 13px;
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margin-top: 2rem;
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}
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</style>
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""", unsafe_allow_html=True)
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# --- Fungsi-fungsi Bantuan ---
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@st.cache_data(ttl="1h")
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def load_eth_data():
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ticker = "ETH-USD"
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df = None
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# 1. COBA ONLINE
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try:
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df = yf.download(
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ticker,
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start="2024-01-01",
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end=date.today() + timedelta(days=1),
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progress=False,
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auto_adjust=True,
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multi_level_index=False
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)
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if df is not None and not df.empty:
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# Bersihkan Index & Kolom
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df = df.reset_index()
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new_cols = []
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for col in df.columns:
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col_name = col[0] if isinstance(col, tuple) else str(col)
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new_cols.append(col_name)
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df.columns = new_cols
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# Kolom pertama adalah Date
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if 'Date' not in df.columns:
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df = df.rename(columns={df.columns[0]: 'Date'})
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# Hapus Timezone
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df['Date'] = pd.to_datetime(df['Date']).dt.tz_localize(None)
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# Simpan Backup (Timpa file lama agar fresh)
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try:
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df.to_csv("eth_backup.csv", index=False)
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except:
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pass
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return df, "online"
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except Exception as e:
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print(f"Gagal Online: {e}")
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# 2. COBA BACKUP (JIKA ONLINE GAGAL)
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try:
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df_backup = pd.read_csv("eth_backup.csv")
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# Bersihkan kolom sampah jika ada
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if "Unnamed: 0" in df_backup.columns:
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df_backup = df_backup.drop(columns=["Unnamed: 0"])
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# Pastikan kolom Date dikenali
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if "Date" in df_backup.columns:
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df_backup["Date"] = pd.to_datetime(df_backup["Date"])
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elif df_backup.columns[0].lower() == "date":
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df_backup = df_backup.rename(columns={df_backup.columns[0]: "Date"})
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df_backup["Date"] = pd.to_datetime(df_backup["Date"])
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return df_backup, "backup"
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except FileNotFoundError:
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# 3. GAGAL TOTAL
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return None, "error"
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def validate_scaler(scaler):
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"""Validasi scaler agar konsisten dengan training."""
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issues = []
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warnings = []
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if not isinstance(scaler, MinMaxScaler):
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warnings.append(f"Scaler bukan MinMaxScaler (terdeteksi: {type(scaler).__name__}). Pastikan ini scaler training.")
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required_attrs = ["n_features_in_", "data_min_", "data_max_", "scale_", "min_"]
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for a in required_attrs:
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if not hasattr(scaler, a):
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issues.append(f"Scaler belum ter-fit atau tidak valid (atribut '{a}' tidak ada).")
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if hasattr(scaler, "n_features_in_"):
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if int(scaler.n_features_in_) != 1:
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issues.append(f"Scaler mengharapkan {scaler.n_features_in_} fitur, tapi aplikasi hanya pakai 1 fitur ('Close').")
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ok = (len(issues) == 0)
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return ok, issues, warnings
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def validate_model_input(model):
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"""Pastikan input model bentuknya (None, time_step, 1)."""
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issues = []
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try:
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shape = model.input_shape # biasanya (None, time_step, 1)
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if not (isinstance(shape, (list, tuple)) and len(shape) == 3):
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issues.append(f"input_shape tidak sesuai harapan: {shape} (harus 3 dimensi).")
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else:
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if shape[2] != 1:
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issues.append(f"Jumlah fitur input model = {shape[2]}, tapi aplikasi membentuk fitur=1.")
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if shape[1] is None or int(shape[1]) <= 1:
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issues.append(f"time_step tidak valid: {shape[1]}.")
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except Exception as e:
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issues.append(f"Gagal membaca input_shape model: {e}")
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ok = (len(issues) == 0)
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return ok, issues
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@st.cache_resource
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def load_gru_assets():
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"""Load model & scaler hasil training (wajib pakai scaler yang sama)."""
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model_path = Path("gru_model.h5")
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scaler_path = Path("scaler_gru.pkl")
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if not model_path.exists() or not scaler_path.exists():
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st.warning(
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f"File tidak ditemukan.\n"
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f"Model: {model_path.resolve().name} ada? {model_path.exists()}\n"
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f"Scaler: {scaler_path.resolve().name} ada? {scaler_path.exists()}"
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)
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return None, None
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try:
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model = load_model(model_path, compile=False)
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scaler = joblib.load(scaler_path)
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ok_scaler, scaler_issues, scaler_warnings = validate_scaler(scaler)
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for w in scaler_warnings:
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st.warning("⚠️ " + w)
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if not ok_scaler:
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st.error("❌ Scaler tidak kompatibel:\n- " + "\n- ".join(scaler_issues))
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return None, None
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ok_model, model_issues = validate_model_input(model)
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if not ok_model:
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st.error("❌ Model input tidak kompatibel:\n- " + "\n- ".join(model_issues))
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return None, None
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return model, scaler
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except Exception as e:
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st.error(f"Gagal load aset: {e}")
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return None, None
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def predict_from_sequence_pure(model, initial_sequence_scaled, n_days):
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seq = np.array(initial_sequence_scaled, dtype="float32").reshape(-1) # (time_step,)
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preds = []
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for _ in range(n_days):
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x = seq.reshape(1, -1, 1) # (1, time_step, 1)
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y = float(model.predict(x, verbose=0)[0, 0]) # output model (scaled)
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preds.append(y)
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seq = np.append(seq[1:], y) # autoregressive (murni)
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return np.array(preds, dtype="float32").reshape(-1, 1)
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def create_combined_chart(df, start_date, future_dates, future_predictions):
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"""Membuat grafik gabungan dengan gaya yang sama seperti referensi."""
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context_start_date = start_date - timedelta(days=60)
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recent_df = df[(df["Date"] >= context_start_date) & (df["Date"] <= start_date)].copy()
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window_size = 7
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recent_df["Trend_Aktual"] = recent_df["Close"].rolling(window=window_size).mean()
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predictions_flat = future_predictions.flatten()
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fig, ax = plt.subplots(figsize=(14, 8))
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ax.plot(
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recent_df["Date"], recent_df["Trend_Aktual"],
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color="#1f77b4", linewidth=2.5, label="Tren Harga Aktual", alpha=0.9
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)
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ax.plot(
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future_dates, predictions_flat,
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color="#d62728", linewidth=2.5, label="Harga Prediksi (GRU)",
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alpha=0.9, marker="o", markersize=4
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)
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ax.scatter(
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recent_df["Date"], recent_df["Close"],
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color="black", s=15, alpha=0.6, zorder=5, label="Data Harian Aktual"
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)
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# garis penghubung trend terakhir ke prediksi pertama
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try:
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last_trend_date = recent_df["Date"].iloc[-1]
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last_trend_price = recent_df["Trend_Aktual"].dropna().iloc[-1]
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first_prediction_date = future_dates[0]
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first_prediction_price = predictions_flat[0]
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ax.plot(
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[last_trend_date, first_prediction_date],
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[last_trend_price, first_prediction_price],
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color="#d62728", linestyle="--", linewidth=2.0, alpha=0.7
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)
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except Exception:
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pass
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ax.set_xlabel("Waktu", fontsize=12, fontweight="bold")
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ax.set_ylabel("Harga (USD)", fontsize=12, fontweight="bold")
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ax.set_title("Analisis Tren Historis dan Prediksi Harga Ethereum", fontsize=16, fontweight="bold", pad=20)
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ax.xaxis.set_major_formatter(mdates.DateFormatter("%b %d, %Y"))
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plt.xticks(rotation=45)
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ax.grid(True, alpha=0.3, linestyle="--")
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handles, labels = ax.get_legend_handles_labels()
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order = [2, 0, 1, 3]
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if len(handles) == 4:
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ax.legend([handles[idx] for idx in order], [labels[idx] for idx in order], loc="upper left", fontsize=11)
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else:
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ax.legend(loc="upper left", fontsize=11)
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plt.tight_layout()
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ax.set_facecolor("#f8f9fa")
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fig.patch.set_facecolor("white")
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return fig
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# --- Tampilan Antarmuka Aplikasi ---
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st.markdown("<div class='app-title'>Prediksi Harga Ethereum (GRU)</div>", unsafe_allow_html=True)
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st.markdown("<div class='app-subtitle'>Data historis & prediksi ETH-USD berbasis GRU</div>", unsafe_allow_html=True)
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df, data_source = load_eth_data()
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model, scaler = load_gru_assets()
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# LOGIC UI (TOAST/WARNING) DITARUH DI SINI (DILUAR CACHE)
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if data_source == "backup":
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st.toast("Koneksi Yahoo lambat. Menggunakan data backup lokal.", icon="⚠️")
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elif data_source == "error":
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st.error("❌ Gagal memuat data (Online gagal & Backup tidak ada).")
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if df is not None and model is not None and scaler is not None:
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# Menampilkan tabel data historis
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st.markdown("<div class='card'>", unsafe_allow_html=True)
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st.markdown("<div class='h-section'>📚 Data Historis Harga Ethereum</div>", unsafe_allow_html=True)
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st.dataframe(df, height=300, use_container_width=True)
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st.markdown("</div>", unsafe_allow_html=True)
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# Menampilkan visualisasi line chart
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st.markdown("<div class='card'>", unsafe_allow_html=True)
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st.markdown("<div class='h-section'>📈 Visualisasi Harga Historis</div>", unsafe_allow_html=True)
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st.line_chart(df.rename(columns={"Date": "index"}).set_index("index")["Close"])
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st.markdown("</div>", unsafe_allow_html=True)
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st.markdown("<div class='card'>", unsafe_allow_html=True)
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st.markdown("<div class='h-section'>🎯 Mulai Prediksi Berdasarkan Tanggal</div>", unsafe_allow_html=True)
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time_step = int(model.input_shape[1])
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min_selectable_date = (df["Date"].min() + timedelta(days=time_step)).date()
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max_selectable_date = df["Date"].max().date()
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col1, col2 = st.columns(2)
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with col1:
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selected_date = st.date_input(
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"Pilih tanggal mulai prediksi:",
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value=max_selectable_date,
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min_value=min_selectable_date,
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max_value=max_selectable_date,
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help=f"Pilih tanggal antara {min_selectable_date} dan {max_selectable_date}"
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)
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with col2:
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prediction_days = st.slider(
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"Pilih jumlah hari untuk prediksi (1-30 hari):",
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min_value=1, max_value=30, value=15, step=1
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)
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if st.button("Buat Prediksi", key="predict_button"):
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if prediction_days < 1:
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st.warning("Jumlah hari prediksi minimal **1**. Silakan geser slidernya dulu. 🙂")
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st.stop()
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with st.spinner("Memproses dan menjalankan prediksi..."):
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# scaling (mengikuti scaler training)
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close_values = df[["Close"]].values.astype(float)
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scaled_data = scaler.transform(close_values)
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# Cocokkan berdasarkan tanggal (robust)
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selected_date_dt = pd.to_datetime(selected_date)
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mask = df["Date"].dt.date == selected_date_dt.date()
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idxs = np.where(mask.to_numpy())[0]
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if len(idxs) == 0:
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st.error(f"Tanggal {selected_date_dt.strftime('%Y-%m-%d')} tidak ditemukan dalam dataset.")
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st.stop()
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start_index = int(idxs[0])
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if start_index - time_step < 0:
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st.error(
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f"Tanggal terlalu awal untuk diprediksi. "
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f"Butuh {time_step} hari data sebelumnya. Pilih tanggal setelah "
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f"{(df['Date'].min() + timedelta(days=time_step)).strftime('%Y-%m-%d')}."
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)
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st.stop()
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initial_sequence = scaled_data[start_index - time_step: start_index] # (time_step,1)
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# ✅ Prediksi MURNI dari model
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future_predictions_scaled = predict_from_sequence_pure(model, initial_sequence, prediction_days)
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# ✅ Inverse transform MURNI dari scaler
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future_predictions = scaler.inverse_transform(future_predictions_scaled)
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# sanity-check
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if not np.isfinite(future_predictions).all():
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st.error("❌ Hasil prediksi mengandung NaN/Inf. Periksa kompatibilitas model/scaler.")
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st.stop()
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future_dates = [selected_date_dt + timedelta(days=i) for i in range(1, prediction_days + 1)]
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st.markdown("<div class='card'>", unsafe_allow_html=True)
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st.subheader("📊 Grafik Prediksi (Historis + Prediksi)")
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fig = create_combined_chart(df, selected_date_dt, future_dates, future_predictions)
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st.pyplot(fig)
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st.markdown("</div>", unsafe_allow_html=True)
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st.markdown("<div class='card'>", unsafe_allow_html=True)
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st.subheader("🧾 Tabel Detail Prediksi")
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prediction_table_df = pd.DataFrame({
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"Tanggal": [d.strftime("%Y-%m-%d") for d in future_dates],
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"Harga Prediksi (USD)": [f"USD {price[0]:,.0f}" for price in future_predictions]
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})
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prediction_table_df.index = prediction_table_df.index + 1
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st.dataframe(prediction_table_df, use_container_width=True)
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st.markdown("</div>", unsafe_allow_html=True)
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last_price = float(df.loc[start_index, "Close"])
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max_prediction = float(np.max(future_predictions))
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min_prediction = float(np.min(future_predictions))
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st.info(f"""
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📊 **Informasi Prediksi:**
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- Harga terakhir pada tanggal {selected_date.strftime('%Y-%m-%d')}: USD {last_price:,.0f}
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- Harga prediksi tertinggi: USD {max_prediction:,.0f}
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- Harga prediksi terendah: USD {min_prediction:,.0f}
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""")
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st.markdown("</div>", unsafe_allow_html=True)
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st.markdown("<div class='footer'>Dibuat oleh Muhammad Gilman Nadhif Azmi</div>", unsafe_allow_html=True)
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elif model is None:
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st.error("Gagal memuat model. Pastikan file 'gru_model.h5' ada di folder yang sama dengan aplikasi.")
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elif scaler is None:
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st.error("Gagal memuat scaler. Pastikan file 'scaler_gru.pkl' ada di folder yang sama dengan aplikasi.")
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else:
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st.error("Gagal memuat data. Coba cek koneksi internet atau sumber data yfinance.")
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