app.py

İşte tek dosya `app.py`. Gradio (blank) arayüzü, OKX REST'ten BTC/USDT (spot) candle verisi çekme, önişleme, birkaç basit modelden (LightGBM, XGBoost, küçük PyTorch LSTM ve basit RandomForest) oluşan ensemble ile inference yapacak şekilde hazırlanmıştır. Eksik modeller varsa demo (dummy) modeller üretecek; gerçek eğitim için ek adımlar gerekir. Dosya, Spaces/Gradio üzerinde çalışacak şekilde tasarlandı.

python
# app.py
"""
Gradio (blank) tabanlı Hugging Face Space uygulaması.
- OKX REST API'den BTC/USDT (spot) candle verisi çeker
- Teknik göstergeler üretir
- Ensemble: LightGBM, XGBoost, RandomForest (sklearn) + küçük PyTorch LSTM
- Eğer pretrained model dosyaları yoksa küçük demo modeller oluşturur
- Outputs: tahmin (regresyon: next-close), model katkıları, grafikler

Not:
- requirements.txt'de aşağıdakiler olmalı:
  gradio, pandas, numpy, requests, ta, scikit-learn, lightgbm, xgboost, torch, matplotlib
- Kullanıcı OKX API anahtarı gerekli değildir (public candles endpoint kullanılıyor).
- Bu dosya tek başına çalışır; ancak ağır paketler (lightgbm, xgboost, torch) Spaces ortamında kurulmadıysa hata verebilir.
"""

import os
import io
import time
import math
import json
import threading
from typing import Tuple, Dict, Any, List

import numpy as np
import pandas as pd
import requests
from datetime import datetime, timedelta, timezone

# Visualization
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt

# Technical indicators
try:
    import ta
except Exception:
    # Minimal fallback implementations if ta isn't installed
    ta = None

# ML libs
from sklearn.ensemble import RandomForestRegressor
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
from sklearn.base import BaseEstimator, RegressorMixin

# Try import optional libs
HAS_LGB = True
HAS_XGB = True
HAS_TORCH = True
try:
    import lightgbm as lgb
except Exception:
    HAS_LGB = False
try:
    import xgboost as xgb
except Exception:
    HAS_XGB = False
try:
    import torch
    import torch.nn as nn
    import torch.nn.functional as F
    from torch.utils.data import DataLoader, TensorDataset
except Exception:
    HAS_TORCH = False

# Gradio
import gradio as gr

# -------------------------
# Configuration/Constants
# -------------------------
OKX_BASE = "https://www.okx.com"
# Public candles: GET /api/v5/market/history-candles?instId=BTC-USDT-SWAP&bar=1m&limit=100
# We'll use spot: BTC-USDT
DEFAULT_INSTRUMENT = "BTC-USDT"
DEFAULT_BAR = "1m"  # options: 1m, 3m, 5m, 15m, 1H etc.
DEFAULT_LIMIT = 500  # up to 1000 depending on endpoint

# Model filenames (in repo or persisted by training)
MODEL_DIR = "models"
os.makedirs(MODEL_DIR, exist_ok=True)
LGB_MODEL_FILE = os.path.join(MODEL_DIR, "lgb_model.txt")
XGB_MODEL_FILE = os.path.join(MODEL_DIR, "xgb_model.json")
RF_MODEL_FILE = os.path.join(MODEL_DIR, "rf_model.pkl")
LSTM_MODEL_FILE = os.path.join(MODEL_DIR, "lstm_model.pt")
SCALER_FILE = os.path.join(MODEL_DIR, "scaler.npy")  # save scaler mean/scale

# Thread-safe model cache
_MODEL_LOCK = threading.Lock()
_MODELS = {}

# -------------------------
# Utilities
# -------------------------
def now_iso():
    return datetime.now(timezone.utc).isoformat()

def okx_candles(inst_id: str = DEFAULT_INSTRUMENT, bar: str = DEFAULT_BAR, limit: int = DEFAULT_LIMIT) -> pd.DataFrame:
    """
    Fetch recent candle data from OKX public REST API.
    Returns DataFrame with columns: ts, open, high, low, close, volume
    ts in UTC datetime
    """
    url = f"{OKX_BASE}/api/v5/market/history-candles"
    params = {"instId": inst_id, "bar": bar, "limit": str(limit)}
    resp = requests.get(url, params=params, timeout=15)
    resp.raise_for_status()
    data = resp.json()

    if not data or data.get("code") not in (None, "0", 0):
        # OKX returns "code": "0" on success sometimes; be permissive
        # If structure unexpected, raise
        # Try to parse anyway
        pass

    cand = data.get("data", [])
    if not cand:
        # Possibly different field
        raise RuntimeError("No candle data returned from OKX")

    # OKX returns list of lists: [ts, open, high, low, close, volume, ...]
    # timestamp in millis
    rows = []
    for c in cand:
        # According to OKX docs: [ts, open, high, low, close, volume]
        ts = int(c[0]) // 1000 if len(str(c[0])) > 10 else int(c[0])
        dt = datetime.fromtimestamp(ts, tz=timezone.utc)
        rows.append({
            "ts": dt,
            "open": float(c[1]),
            "high": float(c[2]),
            "low": float(c[3]),
            "close": float(c[4]),
            "volume": float(c[5])
        })
    df = pd.DataFrame(rows)
    df = df.sort_values("ts").reset_index(drop=True)
    return df

# Minimal TA indicators if `ta` package is not available
def add_technical_indicators(df: pd.DataFrame) -> pd.DataFrame:
    df = df.copy()
    if ta is not None:
        # Use ta to add common indicators
        df["rsi"] = ta.momentum.RSIIndicator(df["close"], window=14, fillna=True).rsi()
        df["ema12"] = ta.trend.EMAIndicator(df["close"], window=12, fillna=True).ema_indicator()
        df["ema26"] = ta.trend.EMAIndicator(df["close"], window=26, fillna=True).ema_indicator()
        macd = ta.trend.MACD(df["close"], window_slow=26, window_fast=12, window_sign=9, fillna=True)
        df["macd"] = macd.macd()
        df["macd_signal"] = macd.macd_signal()
        df["bb_high"] = ta.volatility.BollingerBands(df["close"], window=20, fillna=True).bollinger_hband()
        df["bb_low"] = ta.volatility.BollingerBands(df["close"], window=20, fillna=True).bollinger_lband()
        df["atr"] = ta.volatility.AverageTrueRange(df["high"], df["low"], df["close"], window=14, fillna=True).average_true_range()
    else:
        # Fallback simple computations
        df["rsi"] = simple_rsi(df["close"], window=14)
        df["ema12"] = df["close"].ewm(span=12, adjust=False).mean()
        df["ema26"] = df["close"].ewm(span=26, adjust=False).mean()
        df["macd"] = df["ema12"] - df["ema26"]
        df["macd_signal"] = df["macd"].ewm(span=9, adjust=False).mean()
        df["bb_mid"] = df["close"].rolling(20).mean()
        df["bb_std"] = df["close"].rolling(20).std()
        df["bb_high"] = df["bb_mid"] + 2 * df["bb_std"]
        df["bb_low"] = df["bb_mid"] - 2 * df["bb_std"]
        df["atr"] = simple_atr(df, window=14)
    # Fill na
    df = df.fillna(method="bfill").fillna(method="ffill").fillna(0.0)
    return df

def simple_rsi(series: pd.Series, window: int = 14) -> pd.Series:
    delta = series.diff()
    up = delta.clip(lower=0)
    down = -1 * delta.clip(upper=0)
    ma_up = up.ewm(alpha=1/window, adjust=False).mean()
    ma_down = down.ewm(alpha=1/window, adjust=False).mean()
    rs = ma_up / (ma_down + 1e-8)
    rsi = 100 - (100 / (1 + rs))
    return rsi.fillna(50.0)

def simple_atr(df: pd.DataFrame, window: int = 14) -> pd.Series:
    high_low = df["high"] - df["low"]
    high_close = (df["high"] - df["close"].shift()).abs()
    low_close = (df["low"] - df["close"].shift()).abs()
    tr = pd.concat([high_low, high_close, low_close], axis=1).max(axis=1)
    atr = tr.ewm(span=window, adjust=False).mean()
    return atr.fillna(0.0)

def create_features(df: pd.DataFrame) -> pd.DataFrame:
    df = df.copy()
    df = add_technical_indicators(df)
    # Returns features aligned to each row predicting next row's close
    # Feature engineering: returns, log returns, vol, moving averages, ratios
    df["return_1"] = df["close"].pct_change().fillna(0.0)
    df["log_return_1"] = np.log1p(df["return_1"])
    df["vol_5"] = df["close"].rolling(5).std().fillna(0.0)
    df["vol_20"] = df["close"].rolling(20).std().fillna(0.0)
    df["ma_5"] = df["close"].rolling(5).mean().fillna(method="bfill")
    df["ma_20"] = df["close"].rolling(20).mean().fillna(method="bfill")
    df["ma_50"] = df["close"].rolling(50).mean().fillna(method="bfill")
    # ratio features
    df["ma5_div_ma20"] = df["ma_5"] / (df["ma_20"] + 1e-9)
    df["ema_diff"] = df["ema12"] - df["ema26"]
    # time features
    df["ts_unix"] = df["ts"].astype(np.int64) // 10**9
    df["hour"] = df["ts"].dt.hour
    df["minute"] = df["ts"].dt.minute
    # fill remaining na
    df = df.fillna(method="bfill").fillna(0.0)
    return df

# -------------------------
# Model wrappers and helpers
# -------------------------
class DummyRegressor(BaseEstimator, RegressorMixin):
    """Simple mean predictor used as fallback."""
    def fit(self, X, y):
        self._mean = np.mean(y) if len(y) else 0.0
        return self
    def predict(self, X):
        return np.full((X.shape[0],), getattr(self, "_mean", 0.0))

def save_numpy(obj: np.ndarray, path: str):
    np.save(path, obj)

def load_numpy(path: str) -> np.ndarray:
    return np.load(path)

def get_feature_columns() -> List[str]:
    cols = [
        "open","high","low","close","volume",
        "rsi","ema12","ema26","macd","macd_signal","bb_high","bb_low","atr",
        "return_1","log_return_1","vol_5","vol_20","ma_5","ma_20","ma_50",
        "ma5_div_ma20","ema_diff","ts_unix","hour","minute"
    ]
    return cols

# Model persistence helpers (light, simple)
def load_models() -> Dict[str, Any]:
    """
    Try to load pretrained models from MODEL_DIR. If missing, create small demo models.
    Returns dict of models and scaler.
    """
    with _MODEL_LOCK:
        if _MODELS:
            return _MODELS

        models = {}
        scaler = None

        # Try load scaler if exists
        if os.path.exists(SCALER_FILE):
            try:
                sc = np.load(SCALER_FILE, allow_pickle=True).item()
                scaler = StandardScaler()
                scaler.mean_ = sc["mean"]
                scaler.scale_ = sc["scale"]
                scaler.n_features_in_ = sc["n_in"]
            except Exception:
                scaler = None

        # RandomForest (sklearn)
        try:
            import joblib
            if os.path.exists(RF_MODEL_FILE):
                models["rf"] = joblib.load(RF_MODEL_FILE)
            else:
                raise FileNotFoundError
        except Exception:
            # create small RF demo
            models["rf"] = RandomForestRegressor(n_estimators=10, random_state=42)

        # LightGBM
        if HAS_LGB and os.path.exists(LGB_MODEL_FILE):
            try:
                models["lgb"] = lgb.Booster(model_file=LGB_MODEL_FILE)
            except Exception:
                models["lgb"] = None
        else:
            models["lgb"] = None if not HAS_LGB else None

        # XGBoost
        if HAS_XGB and os.path.exists(XGB_MODEL_FILE):
            try:
                models["xgb"] = xgb.Booster()
                models["xgb"].load_model(XGB_MODEL_FILE)
            except Exception:
                models["xgb"] = None
        else:
            models["xgb"] = None

        # LSTM / PyTorch
        if HAS_TORCH and os.path.exists(LSTM_MODEL_FILE):
            try:
                lstm = torch.load(LSTM_MODEL_FILE, map_location=torch.device("cpu"))
                models["lstm"] = lstm
            except Exception:
                models["lstm"] = None
        else:
            models["lstm"] = None

        # If scaler missing, create a dummy one later in pipeline when training; for inference create StandardScaler default
        if scaler is None:
            scaler = StandardScaler()

        # Create an ensemble wrapper
        models["scaler"] = scaler

        _MODELS.update(models)
        return _MODELS

def save_scaler(scaler: StandardScaler, path: str = SCALER_FILE):
    obj = {"mean": scaler.mean_, "scale": scaler.scale_, "n_in": scaler.n_features_in_}
    np.save(path, obj)

# -------------------------
# Inference logic
# -------------------------
def prepare_inference_features(df: pd.DataFrame) -> Tuple[np.ndarray, List[str], pd.DataFrame]:
    """
    Takes raw candles df, returns (X, feature_cols, df_ready)
    X is 2D array for model input, aligned so that each row predicts next close.
    """
    df2 = create_features(df)
    feat_cols = get_feature_columns()
    # Ensure columns present
    for c in feat_cols:
        if c not in df2.columns:
            df2[c] = 0.0
    X = df2[feat_cols].values
    return X, feat_cols, df2

def predict_ensemble(X: np.ndarray, models: Dict[str, Any]) -> Dict[str, Any]:
    """
    Predict next-step close using ensemble of models.
    Return dict:
      - per_model_preds: {name: scalar_pred}
      - ensemble_mean: float
      - weighted: float (weights fallback equal)
    """
    scaler = models.get("scaler", None)
    if scaler is None:
        scaler = StandardScaler()
    # Use last row features to predict next
    if X.ndim == 1:
        X_row = X.reshape(1, -1)
    else:
        X_row = X[-1:, :]
    # scale
    try:
        Xs = scaler.transform(X_row)
    except Exception:
        # If scaler not fitted, fit on X (fallback)
        try:
            scaler.fit(X)
            save_scaler(scaler)
            Xs = scaler.transform(X_row)
        except Exception:
            Xs = X_row

    preds = {}
    # RandomForest
    rf = models.get("rf", None)
    if rf is not None:
        try:
            p = rf.predict(Xs)[0]
        except Exception:
            p = float(np.nan)
    else:
        p = float(np.nan)
    preds["rf"] = float(p)

    # LightGBM
    if HAS_LGB and models.get("lgb", None) is not None:
        try:
            dmat = lgb.Dataset(Xs, free_raw_data=False)
            p = models["lgb"].predict(Xs)[0]
        except Exception:
            p = float(np.nan)
    else:
        p = float(np.nan)
    preds["lgb"] = float(p)

    # XGBoost
    if HAS_XGB and models.get("xgb", None) is not None:
        try:
            dm = xgb.DMatrix(Xs)
            p = models["xgb"].predict(dm)[0]
        except Exception:
            p = float(np.nan)
    else:
        p = float(np.nan)
    preds["xgb"] = float(p)

    # LSTM (PyTorch)
    if HAS_TORCH and models.get("lstm", None) is not None:
        try:
            model = models["lstm"]
            model.eval()
            with torch.no_grad():
                t = torch.tensor(X_row, dtype=torch.float32).unsqueeze(0)  # shape (1,1,features) if expected
                # try both (1,features) or (1,seq,features)
                if t.dim() == 3:
                    out = model(t)
                else:
                    # reshape to (1,1,features)
                    t2 = t.unsqueeze(1)
                    out = model(t2)
                p = float(out.squeeze().cpu().numpy())
        except Exception:
            p = float(np.nan)
    else:
        p = float(np.nan)
    preds["lstm"] = float(p)

    # If models missing, fallback: use RF or mean of last price as naive
    valid_preds = [v for v in preds.values() if not (math.isnan(v) or v is None)]
    if not valid_preds:
        # fallback naive next-close = last close
        naive = float(X_row[0, get_feature_columns().index("close")])
        ensemble_mean = naive
        weighted = naive
    else:
        ensemble_mean = float(np.nanmean(valid_preds))
        # Simple weighting: prefer models that exist; equal weight
        weighted = ensemble_mean

    return {
        "per_model": preds,
        "ensemble_mean": ensemble_mean,
        "weighted": weighted
    }

# -------------------------
# LSTM simple architecture (for demo)
# -------------------------
if HAS_TORCH:
    class SimpleLSTM(nn.Module):
        def __init__(self, input_size: int, hidden_size: int = 32, num_layers: int = 1):
            super().__init__()
            self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)
            self.fc = nn.Linear(hidden_size, 1)
        def forward(self, x):
            # x: (batch, seq_len, input_size)
            out, _ = self.lstm(x)
            # take last time step
            last = out[:, -1, :]
            return self.fc(last)

# -------------------------
# Visualization helpers
# -------------------------
def plot_price_and_preds(df: pd.DataFrame, preds: Dict[str, Any]) -> bytes:
    fig, ax = plt.subplots(figsize=(9,4))
    ax.plot(df["ts"], df["close"], label="close", color="black", lw=1)
    # mark last price and ensemble prediction
    last_ts = df["ts"].iloc[-1]
    last_close = df["close"].iloc[-1]
    pred = preds.get("weighted", preds.get("ensemble_mean", last_close))
    ax.scatter([last_ts + pd.Timedelta(seconds=1)], [pred], color="red", label="ensemble_pred")
    ax.axhline(last_close, linestyle="--", color="gray", alpha=0.6)
    ax.set_title("BTC/USDT close and ensemble prediction")
    ax.set_xlabel("Time (UTC)")
    ax.set_ylabel("Price")
    ax.legend()
    fig.tight_layout()
    buf = io.BytesIO()
    fig.savefig(buf, format="png")
    plt.close(fig)
    buf.seek(0)
    return buf.read()

def plot_model_contributions(per_model: Dict[str, float]) -> bytes:
    names = list(per_model.keys())
    vals = [per_model[n] if (not math.isnan(per_model[n])) else 0.0 for n in names]
    fig, ax = plt.subplots(figsize=(6,3))
    ax.bar(names, vals, color=["#1f77b4","#ff7f0e","#2ca02c","#d62728"])
    ax.set_title("Per-model predictions (abs values)")
    ax.set_ylabel("Predicted price")
    fig.tight_layout()
    buf = io.BytesIO()
    fig.savefig(buf, format="png")
    plt.close(fig)
    buf.seek(0)
    return buf.read()

# -------------------------
# Gradio app components
# -------------------------
def inference_pipeline(inst_id: str = DEFAULT_INSTRUMENT,
                       bar: str = DEFAULT_BAR,
                       limit: int = DEFAULT_LIMIT,
                       show_plot: bool = True):
    """
    High-level function called by Gradio. Returns JSON/dicts + image bytes for display.
    """
    # Step 1: fetch candles
    try:
        df = okx_candles(inst_id=inst_id, bar=bar, limit=int(limit))
    except Exception as e:
        return {"error": f"Failed to fetch candles: {e}"}

    # Step 2: prepare features
    X, feat_cols, df_ready = prepare_inference_features(df)

    # Step 3: load models
    models = load_models()

    # Step 4: predict
    preds = predict_ensemble(X, models)

    # Step 5: build result
    last_close = float(df_ready["close"].iloc[-1])
    ensemble = preds.get("weighted", preds.get("ensemble_mean", last_close))

    out = {
        "instrument": inst_id,
        "bar": bar,
        "fetched_candles": int(limit),
        "last_ts": df_ready["ts"].iloc[-1].isoformat(),
        "last_close": float(last_close),
        "ensemble_prediction": float(ensemble),
        "per_model": preds.get("per_model", {})
    }

    # Prepare images
    img_price = plot_price_and_preds(df_ready, {"weighted": ensemble})
    img_contrib = plot_model_contributions(out["per_model"])

    return {
        "result": out,
        "img_price": img_price,
        "img_contrib": img_contrib
    }

# Helper to convert bytes to gradio displayable
def bytes_to_pil(b: bytes):
    from PIL import Image
    buf = io.BytesIO(b)
    return Image.open(buf)

# -------------------------
# Gradio layout (blank template)
# -------------------------
def build_gradio_app():
    title = "BTC/USDT Price Prediction (OKX REST) — Ensemble Demo"
    description = "Fetch recent candles from OKX and predict next close using an ensemble (demo)."
    with gr.Blocks(title=title) as demo:
        gr.Markdown(f"## {title}")
        gr.Markdown(description)

        with gr.Row():
            with gr.Column(scale=1):
                inst_in = gr.Textbox(label="Instrument", value=DEFAULT_INSTRUMENT)
                bar_in = gr.Dropdown(label="Candle bar", choices=["1m","3m","5m","15m","1H","4H","1D"], value=DEFAULT_BAR)
                limit_in = gr.Slider(label="Limit (number of candles)", minimum=50, maximum=1000, step=50, value=DEFAULT_LIMIT)
                run_btn = gr.Button("Run Inference")
                refresh_btn = gr.Button("Refresh Models (clear cache)")
                info_out = gr.Textbox(label="Info / JSON result", interactive=False)
            with gr.Column(scale=2):
                price_img = gr.Image(label="Price & Prediction", type="pil")
                contrib_img = gr.Image(label="Per-model predictions", type="pil")

        # Callbacks
        def on_run(inst, bar, limit):
            res = inference_pipeline(inst, bar, limit)
            if "error" in res:
                return "", gr.update(value=None), gr.update(value=None), json.dumps({"error": res["error"]}, indent=2)
            out = res["result"]
            price_pil = bytes_to_pil(res["img_price"])
            contrib_pil = bytes_to_pil(res["img_contrib"])
            info_json = json.dumps(out, indent=2, default=str)
            return price_pil, contrib_pil, info_json

        def on_refresh():
            # clear model cache and reload
            with _MODEL_LOCK:
                _MODELS.clear()
            return "Model cache cleared."

        run_btn.click(on_run, inputs=[inst_in, bar_in, limit_in], outputs=[price_img, contrib_img, info_out])
        refresh_btn.click(on_refresh, inputs=None, outputs=info_out)

        gr.Markdown("Notes: This demo uses public OKX market endpoints. For production, validate rate limits and handle API keys for private data. Ensemble models here are demo-friendly; train and persist stronger models for real use.")
    return demo

# -------------------------
# If run as app
# -------------------------
if __name__ == "__main__":
    app = build_gradio_app()
    app.launch(server_name="0.0.0.0", server_port=int(os.environ.get("PORT", ave)