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Update app.py
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app.py
CHANGED
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@@ -18,64 +18,65 @@ if api_key:
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client = Groq(api_key=api_key)
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# ==========================================
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# 📡 2.
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# ==========================================
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#
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"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 "
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"(KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36",
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"Accept": "application/json",
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"Accept-Language": "en-US,en;q=0.9",
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}
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def
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"""
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for attempt in range(3):
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try:
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r = requests.get(
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f"{
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params={"
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headers=
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timeout=15
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)
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r.raise_for_status()
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d = r.json()
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return {
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"last": float(d["
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"open24h": float(d["
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"high24h": float(d["
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"low24h": float(d["
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"vol24h": float(d["
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"change_pct": float(d["
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}
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except Exception as e:
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print(f"
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if attempt < 2:
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time.sleep(2)
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return None
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def
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"""
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for attempt in range(3):
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try:
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r = requests.get(
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f"{
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params={"
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headers=
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timeout=15
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)
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r.raise_for_status()
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raw = r.json()
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df = pd.DataFrame(raw, columns=
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for
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df[
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df["ts"] = pd.to_datetime(df["ts"].astype(int), unit="ms")
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return df
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except Exception as e:
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print(f"
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if attempt < 2:
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time.sleep(2)
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return None
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@@ -90,9 +91,9 @@ def compute_rsi(series, period=14):
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rs = gain / loss
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return 100 - (100 / (1 + rs))
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def
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"""
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df =
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if df is None or len(df) < 50:
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return None, None, None, None
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@@ -123,11 +124,11 @@ def train_models_from_mexc():
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return xgb_model, knn_model, scaler, features
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# Başlangıçta modelleri
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xgb_model, knn_model, scaler, feature_cols =
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if xgb_model is None:
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print("
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dummy_X = np.random.rand(50, 5)
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dummy_y = np.random.randint(0, 2, 50)
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xgb_model = xgb.XGBClassifier(n_estimators=10, max_depth=2)
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@@ -135,17 +136,17 @@ if xgb_model is None:
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scaler = StandardScaler()
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knn_model = KNeighborsClassifier(n_neighbors=5)
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knn_model.fit(scaler.fit_transform(dummy_X), dummy_y)
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feature_cols = ["ma_5","ma_20","rsi","macd","vol"]
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# ==========================================
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# 🧠 4. ANALİZ MOTORU (XGB + KNN Ensemble)
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# ==========================================
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def predict_and_analyze():
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ticker =
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candles =
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if ticker is None or candles is None:
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return "❌
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last_price = ticker["last"]
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change_pct = ticker["change_pct"]
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@@ -176,7 +177,7 @@ def predict_and_analyze():
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prompt = f"""
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Sen uzman bir kripto para analisti ve teknik analiz uzmanısın.
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📡
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- Son Fiyat: ${last_price:,.2f}
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- 24s Değişim: %{change_pct:+.2f}
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- 24s Hacim: {vol24h:,.0f} BTC
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@@ -216,7 +217,7 @@ def predict_and_analyze():
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analysis_result = "GROQ_API_KEY bulunamadı! Hugging Face Secrets'a ekleyin."
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header = (
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f"**
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f"MA5: ${ma5:,.0f} | MA20: ${ma20:,.0f}\n\n"
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f"**XGB:** %{xgb_prob_up*100:.1f} | **KNN:** %{knn_prob_up*100:.1f} | "
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f"**Ensemble:** %{ensemble_prob_up*100:.1f}\n\n---\n\n"
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@@ -229,17 +230,17 @@ def predict_and_analyze():
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with gr.Blocks(theme=gr.themes.Soft(primary_hue="orange", secondary_hue="slate")) as demo:
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gr.Markdown(
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"""
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# ₿ BTC/USD XGBoost + KNN & Groq AI Tahmin Radarı (
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Fiyat ve mum verileri **
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"""
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)
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with gr.Row():
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with gr.Column():
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analyze_btn = gr.Button("🔄
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gr.Markdown("**Not**:
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with gr.Column():
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prob_output = gr.Label(label="Model Tahminleri (XGB + KNN)")
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analysis_output = gr.Markdown(value="Butona basarak
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analyze_btn.click(fn=predict_and_analyze, inputs=[], outputs=[analysis_output, prob_output])
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gr.Markdown("---\n⚠️ **Yatırım Tavsiyesi Değildir**: Bu uygulama eğitim amaçlıdır.")
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client = Groq(api_key=api_key)
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# ==========================================
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# 📡 2. OKX PUBLIC API (openapi alan adı)
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# ==========================================
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# HF'de www.okx.com engellenmiş olabilir; openapi.okx.com küresel REST alan adıdır
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OKX_BASE = "https://openapi.okx.com"
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# Cloudflare istemci imzası engellemesini aşmak için tarayıcı benzeri başlıklar
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OKX_HEADERS = {
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"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 "
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"(KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36",
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"Accept": "application/json",
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"Accept-Language": "en-US,en;q=0.9",
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}
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def fetch_okx_ticker(inst_id="BTC-USDT"):
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"""OKX anlık fiyat. 3 kez dener."""
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for attempt in range(3):
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try:
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r = requests.get(
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f"{OKX_BASE}/api/v5/market/ticker",
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params={"instId": inst_id},
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headers=OKX_HEADERS,
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timeout=15
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)
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r.raise_for_status()
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d = r.json()["data"][0]
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return {
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"last": float(d["last"]),
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"open24h": float(d["open24h"]),
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"high24h": float(d["high24h"]),
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"low24h": float(d["low24h"]),
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"vol24h": float(d["vol24h"]),
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"change_pct": (float(d["last"]) / float(d["open24h"]) - 1) * 100,
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}
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except Exception as e:
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print(f"OKX ticker attempt {attempt+1} failed: {e}")
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if attempt < 2:
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time.sleep(2)
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return None
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def fetch_okx_candles(inst_id="BTC-USDT", bar="1H", limit=100):
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"""OKX mum verisi. 3 kez dener."""
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for attempt in range(3):
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try:
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r = requests.get(
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f"{OKX_BASE}/api/v5/market/candles",
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params={"instId": inst_id, "bar": bar, "limit": limit},
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headers=OKX_HEADERS,
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timeout=15
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)
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r.raise_for_status()
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raw = r.json()["data"]
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# OKX formatı: [ts, open, high, low, close, vol, ...]
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df = pd.DataFrame(raw, columns=["ts", "open", "high", "low", "close", "vol"])
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for col in ["open", "high", "low", "close", "vol"]:
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df[col] = df[col].astype(float)
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df["ts"] = pd.to_datetime(df["ts"].astype(int), unit="ms")
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return df.sort_values("ts").reset_index(drop=True)
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except Exception as e:
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print(f"OKX candles attempt {attempt+1} failed: {e}")
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if attempt < 2:
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time.sleep(2)
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return None
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rs = gain / loss
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return 100 - (100 / (1 + rs))
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def train_models_from_okx():
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"""OKX mum verisiyle XGBoost ve KNN eğitir."""
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df = fetch_okx_candles(limit=500)
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if df is None or len(df) < 50:
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return None, None, None, None
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return xgb_model, knn_model, scaler, features
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# Başlangıçta modelleri OKX verisiyle eğit
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xgb_model, knn_model, scaler, feature_cols = train_models_from_okx()
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if xgb_model is None:
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print("OKX erişilemedi, dummy modeller kullan��lıyor.")
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dummy_X = np.random.rand(50, 5)
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dummy_y = np.random.randint(0, 2, 50)
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xgb_model = xgb.XGBClassifier(n_estimators=10, max_depth=2)
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scaler = StandardScaler()
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knn_model = KNeighborsClassifier(n_neighbors=5)
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knn_model.fit(scaler.fit_transform(dummy_X), dummy_y)
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feature_cols = ["ma_5", "ma_20", "rsi", "macd", "vol"]
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# ==========================================
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# 🧠 4. ANALİZ MOTORU (XGB + KNN Ensemble)
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# ==========================================
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def predict_and_analyze():
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ticker = fetch_okx_ticker()
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candles = fetch_okx_candles(limit=30)
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if ticker is None or candles is None:
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return "❌ OKX API'sine erişilemedi (openapi alan adı + tarayıcı UA denendi). Lütfen sonra tekrar deneyin.", {}
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last_price = ticker["last"]
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change_pct = ticker["change_pct"]
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prompt = f"""
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Sen uzman bir kripto para analisti ve teknik analiz uzmanısın.
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📡 OKX Canlı Verileri (BTC/USDT):
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- Son Fiyat: ${last_price:,.2f}
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- 24s Değişim: %{change_pct:+.2f}
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- 24s Hacim: {vol24h:,.0f} BTC
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analysis_result = "GROQ_API_KEY bulunamadı! Hugging Face Secrets'a ekleyin."
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header = (
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f"**OKX Canlı: ${last_price:,.2f}** (%{change_pct:+.2f}) | RSI: {rsi:.1f} | "
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f"MA5: ${ma5:,.0f} | MA20: ${ma20:,.0f}\n\n"
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f"**XGB:** %{xgb_prob_up*100:.1f} | **KNN:** %{knn_prob_up*100:.1f} | "
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f"**Ensemble:** %{ensemble_prob_up*100:.1f}\n\n---\n\n"
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with gr.Blocks(theme=gr.themes.Soft(primary_hue="orange", secondary_hue="slate")) as demo:
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gr.Markdown(
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"""
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# ₿ BTC/USD XGBoost + KNN & Groq AI Tahmin Radarı (OKX Canlı)
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Fiyat ve mum verileri **OKX halka açık API**'sinden (`openapi.okx.com`) otomatik çekilir. **XGBoost + KNN** ensemble tahmini yapar, **Groq** analiz raporu üretir.
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"""
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)
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with gr.Row():
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with gr.Column():
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analyze_btn = gr.Button("🔄 OKX'ten Veri Çek ve Analiz Et", variant="primary", size="lg")
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gr.Markdown("**Not**: `openapi.okx.com` + tarayıcı UA + 3 deneme kullanılıyor.")
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with gr.Column():
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prob_output = gr.Label(label="Model Tahminleri (XGB + KNN)")
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analysis_output = gr.Markdown(value="Butona basarak OKX'ten canlı veri çekip analizi başlatın.")
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analyze_btn.click(fn=predict_and_analyze, inputs=[], outputs=[analysis_output, prob_output])
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gr.Markdown("---\n⚠️ **Yatırım Tavsiyesi Değildir**: Bu uygulama eğitim amaçlıdır.")
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