Update app.py

app.py

@@ -1,5 +1,5 @@
 import os
-import time
+import io
 import requests
 import numpy as np
 import pandas as pd
@@ -9,81 +9,38 @@ 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 = None
-if api_key:
-    client = Groq(api_key=api_key)
-
-# ==========================================
-# 📡 2. OKX PUBLIC API (openapi alan adı)
-# ==========================================
-# HF'de www.okx.com engellenmiş olabilir; openapi.okx.com küresel REST alan adıdır
-OKX_BASE = "https://openapi.okx.com"
-
-# Cloudflare istemci imzası engellemesini aşmak için tarayıcı benzeri başlıklar
-OKX_HEADERS = {
-    "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 "
-                  "(KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36",
-    "Accept": "application/json",
-    "Accept-Language": "en-US,en;q=0.9",
-}
-
-def fetch_okx_ticker(inst_id="BTC-USDT"):
-    """OKX anlık fiyat. 3 kez dener."""
-    for attempt in range(3):
-        try:
-            r = requests.get(
-                f"{OKX_BASE}/api/v5/market/ticker",
-                params={"instId": inst_id},
-                headers=OKX_HEADERS,
-                timeout=15
-            )
-            r.raise_for_status()
-            d = r.json()["data"][0]
-            return {
-                "last": float(d["last"]),
-                "open24h": float(d["open24h"]),
-                "high24h": float(d["high24h"]),
-                "low24h": float(d["low24h"]),
-                "vol24h": float(d["vol24h"]),
-                "change_pct": (float(d["last"]) / float(d["open24h"]) - 1) * 100,
-            }
-        except Exception as e:
-            print(f"OKX ticker attempt {attempt+1} failed: {e}")
-            if attempt < 2:
-                time.sleep(2)
-    return None
-
-def fetch_okx_candles(inst_id="BTC-USDT", bar="1H", limit=100):
-    """OKX mum verisi. 3 kez dener."""
-    for attempt in range(3):
-        try:
-            r = requests.get(
-                f"{OKX_BASE}/api/v5/market/candles",
-                params={"instId": inst_id, "bar": bar, "limit": limit},
-                headers=OKX_HEADERS,
-                timeout=15
-            )
+client = Groq(api_key=api_key) if api_key else None
+
+def load_dataframe(file_obj, url):
+    try:
+        if url and url.strip():
+            r = requests.get(url.strip(), headers={"User-Agent": "Mozilla/5.0"}, timeout=30)
             r.raise_for_status()
-            raw = r.json()["data"]
-            # OKX formatı: [ts, open, high, low, close, vol, ...]
-            df = pd.DataFrame(raw, columns=["ts", "open", "high", "low", "close", "vol"])
-            for col in ["open", "high", "low", "close", "vol"]:
-                df[col] = df[col].astype(float)
-            df["ts"] = pd.to_datetime(df["ts"].astype(int), unit="ms")
-            return df.sort_values("ts").reset_index(drop=True)
-        except Exception as e:
-            print(f"OKX candles attempt {attempt+1} failed: {e}")
-            if attempt < 2:
-                time.sleep(2)
-    return None
-
-# ==========================================
-# 📊 3. YARDIMCI FONKSİYONLAR
-# ==========================================
+            text = r.text
+        elif file_obj is not None:
+            text = open(file_obj.name, "r", encoding="utf-8", errors="ignore").read()
+        else:
+            return None, "Lütfen dosya yükleyin veya link girin."
+
+        df = pd.read_csv(io.StringIO(text), sep="\t", header=None)
+        if df.shape[1] < 6:
+            df = pd.read_csv(io.StringIO(text), 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}"
+
+
 def compute_rsi(series, period=14):
     delta = series.diff()
     gain = delta.clip(lower=0).rolling(period).mean()
@@ -91,158 +48,122 @@ def compute_rsi(series, period=14):
     rs = gain / loss
     return 100 - (100 / (1 + rs))
 
-def train_models_from_okx():
-    """OKX mum verisiyle XGBoost ve KNN eğitir."""
-    df = fetch_okx_candles(limit=500)
-    if df is None or len(df) < 50:
-        return None, None, None, None
 
+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
+        return None, None, None, None, "Yetersiz veri."
 
-    features = ["ma_5", "ma_20", "rsi", "macd", "vol"]
+    features = ["ma_5", "ma_20", "rsi", "macd", "volume"]
     X = df_model[features].values
     y = df_model["target"].values
 
     xgb_model = xgb.XGBClassifier(
-        n_estimators=50, max_depth=3, learning_rate=0.1,
+        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=5)
+    knn_model = KNeighborsClassifier(n_neighbors=7)
     knn_model.fit(X_scaled, y)
 
-    return xgb_model, knn_model, scaler, features
-
-# Başlangıçta modelleri OKX verisiyle eğit
-xgb_model, knn_model, scaler, feature_cols = train_models_from_okx()
-
-if xgb_model is None:
-    print("OKX erişilemedi, dummy modeller kullanılıyor.")
-    dummy_X = np.random.rand(50, 5)
-    dummy_y = np.random.randint(0, 2, 50)
-    xgb_model = xgb.XGBClassifier(n_estimators=10, max_depth=2)
-    xgb_model.fit(dummy_X, dummy_y)
-    scaler = StandardScaler()
-    knn_model = KNeighborsClassifier(n_neighbors=5)
-    knn_model.fit(scaler.fit_transform(dummy_X), dummy_y)
-    feature_cols = ["ma_5", "ma_20", "rsi", "macd", "vol"]
-
-# ==========================================
-# 🧠 4. ANALİZ MOTORU (XGB + KNN Ensemble)
-# ==========================================
-def predict_and_analyze():
-    ticker = fetch_okx_ticker()
-    candles = fetch_okx_candles(limit=30)
-
-    if ticker is None or candles is None:
-        return "❌ OKX API'sine erişilemedi (openapi alan adı + tarayıcı UA denendi). Lütfen sonra tekrar deneyin.", {}
-
-    last_price = ticker["last"]
-    change_pct = ticker["change_pct"]
-    vol24h = ticker["vol24h"]
+    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
 
-    close = candles["close"]
-    ma5 = close.rolling(5).mean().iloc[-1]
-    ma20 = close.rolling(20).mean().iloc[-1]
-    rsi = compute_rsi(close, 14).iloc[-1]
-    macd = (close.ewm(span=12).mean() - close.ewm(span=26).mean()).iloc[-1]
 
-    feat_arr = np.array([[ma5, ma20, rsi, macd, vol24h]])
+def predict_and_analyze(file_obj, url):
+    df, err = load_dataframe(file_obj, url)
+    if err:
+        return err, {}
 
-    xgb_prob_up = float(xgb_model.predict_proba(feat_arr)[0][1])
-    knn_prob_up = float(knn_model.predict_proba(scaler.transform(feat_arr))[0][1])
+    xgb_model, knn_model, scaler, metrics, err = train_models(df)
+    if err:
+        return err, {}
 
-    ensemble_prob_up = (xgb_prob_up + knn_prob_up) / 2
-    ensemble_prob_down = 1 - ensemble_prob_up
-    direction = "YUKARI 📈" if ensemble_prob_up > 0.5 else "AŞAĞI 📉"
-    confidence = max(ensemble_prob_up, ensemble_prob_down) * 100
+    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_prob_up,
-        "KNN Yükseliş": knn_prob_up,
-        "Ensemble Yükseliş (Ortalama)": ensemble_prob_up,
+        "XGBoost Yükseliş": xgb_up,
+        "KNN Yükseliş": knn_up,
+        "Ensemble Yükseliş": ens_up,
     }
 
     prompt = f"""
-    Sen uzman bir kripto para analisti ve teknik analiz uzmanısın.
-
-    📡 OKX Canlı Verileri (BTC/USDT):
-    - Son Fiyat: ${last_price:,.2f}
-    - 24s Değişim: %{change_pct:+.2f}
-    - 24s Hacim: {vol24h:,.0f} BTC
-    - 24s Yüksek/Düşük: ${ticker['high24h']:,.2f} / ${ticker['low24h']:,.2f}
-
-    📊 Teknik Göstergeler (1H):
-    - RSI (14): {rsi:.2f}
-    - MA5: ${ma5:,.2f} | MA20: ${ma20:,.2f}
-    - MACD: {macd:.4f}
-
-    🤖 Model Sonuçları:
-    - XGBoost Yükseliş: %{xgb_prob_up*100:.1f}
-    - KNN Yükseliş: %{knn_prob_up*100:.1f}
-    - Ensemble Yükseliş: %{ensemble_prob_up*100:.1f}
-    - Tahmin Yönü: {direction} (Güven: %{confidence:.1f})
-
-    Türkçe bir teknik analiz raporu hazırla. Başlıklar:
-    1. 🎯 Model Yorumu (XGBoost + KNN karşılaştırması)
-    2. 📈 Teknik Analiz (RSI, MA, MACD; destek/direnç)
-    3. 💡 İşlem Stratejisi (Long/Short, stop-loss, take-profit)
-
-    Yatırım tavsiyesi olmadığını vurgula.
+    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:
-            response = client.chat.completions.create(
+            resp = client.chat.completions.create(
                 model="openai/gpt-oss-120b",
                 messages=[{"role": "user", "content": prompt}],
                 temperature=0.3,
                 max_tokens=1024
             )
-            analysis_result = response.choices[0].message.content
+            analysis = resp.choices[0].message.content
         except Exception as e:
-            analysis_result = f"Groq API Hatası: {e}"
+            analysis = f"Groq API Hatası: {e}"
     else:
-        analysis_result = "GROQ_API_KEY bulunamadı! Hugging Face Secrets'a ekleyin."
+        analysis = "GROQ_API_KEY bulunamadı!"
 
     header = (
-        f"**OKX Canlı: ${last_price:,.2f}** (%{change_pct:+.2f}) | RSI: {rsi:.1f} | "
-        f"MA5: ${ma5:,.0f} | MA20: ${ma20:,.0f}\n\n"
-        f"**XGB:** %{xgb_prob_up*100:.1f} | **KNN:** %{knn_prob_up*100:.1f} | "
-        f"**Ensemble:** %{ensemble_prob_up*100:.1f}\n\n---\n\n"
+        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} | **Ens:** %{ens_up*100:.1f}\n\n---\n\n"
     )
-    return header + analysis_result, prob_dict
+    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(
         """
-        # ₿ BTC/USD XGBoost + KNN & Groq AI Tahmin Radarı (OKX Canlı)
-        Fiyat ve mum verileri **OKX halka açık API**'sinden (`openapi.okx.com`) otomatik çekilir. **XGBoost + KNN** ensemble tahmini yapar, **Groq** analiz raporu üretir.
+        # ₿ BTC/USD XGBoost + KNN & Groq AI Tahmin Radarı
+        **Başlıksız** TSV/CSV yükleyin (Date, Open, High, Low, Close, Volume) **veya** link girin.
         """
     )
     with gr.Row():
         with gr.Column():
-            analyze_btn = gr.Button("🔄 OKX'ten Veri Çek ve Analiz Et", variant="primary", size="lg")
-            gr.Markdown("**Not**: `openapi.okx.com` + tarayıcı UA + 3 deneme kullanılıyor.")
+            file_input = gr.File(label="Dosya Yükle (.csv / .tsv / .txt)", file_types=[".csv", ".tsv", ".txt"])
+            url_input = gr.Textbox(label="veya Link (URL)", placeholder="https://.../BTCUSD30.csv")
+            submit_btn = gr.Button("Analiz Et", variant="primary")
         with gr.Column():
-            prob_output = gr.Label(label="Model Tahminleri (XGB + KNN)")
-            analysis_output = gr.Markdown(value="Butona basarak OKX'ten canlı veri çekip analizi başlatın.")
-    analyze_btn.click(fn=predict_and_analyze, inputs=[], outputs=[analysis_output, prob_output])
-    gr.Markdown("---\n⚠️ **Yatırım Tavsiyesi Değildir**: Bu uygulama eğitim amaçlıdır.")
+            prob_output = gr.Label(label="Model Tahminleri")
+            analysis_output = gr.Markdown(value="Veri yükleyip butona basın.")
+
+    submit_btn.click(
+        fn=predict_and_analyze,
+        inputs=[file_input, url_input],
+        outputs=[analysis_output, prob_output]
+    )
 
 if __name__ == "__main__":
     demo.launch()