Delete app.py

app.py

@@ -1,187 +0,0 @@
-import os
-import numpy as np
-import pandas as pd
-import gradio as gr
-import xgboost as xgb
-from sklearn.neighbors import KNeighborsClassifier
-from sklearn.preprocessing import StandardScaler
-from groq import Groq
-
-# ==========================================
-# 🔌 1. GROQ CLIENT
-# ==========================================
-api_key = os.environ.get("GROQ_API_KEY")
-client = Groq(api_key=api_key) if api_key else None
-
-# ==========================================
-# 📊 2. CSV DOSYASINI OKUMA (Ana dizinden)
-# ==========================================
-CSV_PATH = "BTCUSD_M30.csv"
-
-def load_local_csv(path=CSV_PATH):
-    """Space ana dizinindeki CSV'yi okur (başlıksız, tab/virgül)."""
-    try:
-        if not os.path.exists(path):
-            return None, f"Dosya bulunamadı: {path}"
-
-        df = pd.read_csv(path, sep="\t", header=None)
-        if df.shape[1] < 6:
-            df = pd.read_csv(path, sep=",", header=None)
-
-        if df.shape[1] < 6:
-            return None, f"6 sütun bekleniyordu, {df.shape[1]} bulundu."
-
-        df = df.iloc[:, :6]
-        df.columns = ["date", "open", "high", "low", "close", "volume"]
-
-        for c in ["open", "high", "low", "close", "volume"]:
-            df[c] = pd.to_numeric(df[c], errors="coerce")
-
-        df = df.dropna(subset=["close"]).reset_index(drop=True)
-        if len(df) < 60:
-            return None, f"En az 60 satır gerekli, {len(df)} var."
-
-        return df, None
-    except Exception as e:
-        return None, f"Veri okunamadı: {e}"
-
-# ==========================================
-# 🧮 3. TEKNİK GÖSTERGELER + MODEL
-# ==========================================
-def compute_rsi(series, period=14):
-    delta = series.diff()
-    gain = delta.clip(lower=0).rolling(period).mean()
-    loss = -delta.clip(upper=0).rolling(period).mean()
-    rs = gain / loss
-    return 100 - (100 / (1 + rs))
-
-def train_models(df):
-    df = df.copy()
-    df["ma_5"] = df["close"].rolling(5).mean()
-    df["ma_20"] = df["close"].rolling(20).mean()
-    df["rsi"] = compute_rsi(df["close"], 14)
-    df["macd"] = df["close"].ewm(span=12).mean() - df["close"].ewm(span=26).mean()
-    df["target"] = (df["close"].shift(-1) > df["close"]).astype(int)
-
-    df_model = df.dropna().reset_index(drop=True)
-    if len(df_model) < 20:
-        return None, None, None, None, "Göstergelerden sonra yetersiz veri."
-
-    features = ["ma_5", "ma_20", "rsi", "macd", "volume"]
-    X = df_model[features].values
-    y = df_model["target"].values
-
-    xgb_model = xgb.XGBClassifier(
-        n_estimators=60, max_depth=3, learning_rate=0.1,
-        objective="binary:logistic", eval_metric="logloss", random_state=42
-    )
-    xgb_model.fit(X, y)
-
-    scaler = StandardScaler()
-    X_scaled = scaler.fit_transform(X)
-    knn_model = KNeighborsClassifier(n_neighbors=7)
-    knn_model.fit(X_scaled, y)
-
-    latest = df_model.iloc[-1]
-    metrics = {
-        "last_close": float(latest["close"]),
-        "last_date": str(latest["date"]),
-        "ma5": float(latest["ma_5"]),
-        "ma20": float(latest["ma_20"]),
-        "rsi": float(latest["rsi"]),
-        "macd": float(latest["macd"]),
-        "volume": float(latest["volume"]),
-    }
-    return xgb_model, knn_model, scaler, metrics, None
-
-# ==========================================
-# 🧠 4. ANALİZ FONKSİYONU
-# ==========================================
-def predict_and_analyze():
-    df, err = load_local_csv()
-    if err:
-        return err, {}
-
-    xgb_model, knn_model, scaler, metrics, err = train_models(df)
-    if err:
-        return err, {}
-
-    feat = np.array([[metrics["ma5"], metrics["ma20"], metrics["rsi"],
-                      metrics["macd"], metrics["volume"]]])
-
-    xgb_up = float(xgb_model.predict_proba(feat)[0][1])
-    knn_up = float(knn_model.predict_proba(scaler.transform(feat))[0][1])
-    ens_up = (xgb_up + knn_up) / 2
-    ens_down = 1 - ens_up
-    direction = "YUKARI 📈" if ens_up > 0.5 else "AŞAĞI 📉"
-    conf = max(ens_up, ens_down) * 100
-
-    prob_dict = {
-        "XGBoost Yükseliş": xgb_up,
-        "KNN Yükseliş": knn_up,
-        "Ensemble Yükseliş": ens_up,
-    }
-
-    prompt = f"""
-    BTC/USD teknik analiz raporu (Türkçe).
-    Son Kapanış ({metrics['last_date']}): ${metrics['last_close']:,.2f}
-    RSI: {metrics['rsi']:.2f}, MA5: ${metrics['ma5']:,.2f}, MA20: ${metrics['ma20']:,.2f}, MACD: {metrics['macd']:.4f}, Hacim: {metrics['volume']:,.2f}
-    XGBoost Yükseliş: %{xgb_up*100:.1f}
-    KNN Yükseliş: %{knn_up*100:.1f}
-    Ensemble: %{ens_up*100:.1f} -> {direction} (Güven %{conf:.1f})
-    Başlıklar: 1. Model Yorumu 2. Teknik Analiz 3. İşlem Stratejisi. Yatırım tavsiyesi değildir.
-    """
-
-    if client:
-        try:
-            resp = client.chat.completions.create(
-                model="openai/gpt-oss-120b",
-                messages=[{"role": "user", "content": prompt}],
-                temperature=0.3,
-                max_tokens=1024
-            )
-            analysis = resp.choices[0].message.content
-        except Exception as e:
-            analysis = f"Groq API Hatası: {e}"
-    else:
-        analysis = "GROQ_API_KEY bulunamadı!"
-
-    header = (
-        f"**Son: ${metrics['last_close']:,.2f}** | RSI: {metrics['rsi']:.1f} | "
-        f"MA5: ${metrics['ma5']:,.0f} | MA20: ${metrics['ma20']:,.0f}\n\n"
-        f"**XGB:** %{xgb_up*100:.1f} | **KNN:** %{knn_up*100:.1f} | "
-        f"**Ens:** %{ens_up*100:.1f}\n\n---\n\n"
-    )
-    return header + analysis, prob_dict
-
-# ==========================================
-# 🎨 5. GRADIO ARAYÜZÜ
-# ==========================================
-with gr.Blocks(theme=gr.themes.Soft(primary_hue="orange", secondary_hue="slate")) as demo:
-    gr.Markdown(
-        f"""
-        # ₿ BTC/USD XGBoost + KNN & Groq AI Tahmin Radarı
-        Veri kaynağı: **`{CSV_PATH}`** (Space ana dizini)
-        """
-    )
-
-    with gr.Row():
-        with gr.Column():
-            analyze_btn = gr.Button("📊 CSV'yi Oku ve Analiz Et", variant="primary", size="lg")
-            gr.Markdown(
-                """
-                **Not**: Model, `BTCUSD_M30.csv` dosyasını her butona basışta yeniden okur ve eğitir.
-                """
-            )
-        with gr.Column():
-            prob_output = gr.Label(label="Model Tahminleri")
-            analysis_output = gr.Markdown(value="Butona basarak analizi başlatın.")
-
-    analyze_btn.click(
-        fn=predict_and_analyze,
-        inputs=[],
-        outputs=[analysis_output, prob_output]
-    )
-
-if __name__ == "__main__":
-    demo.launch()