Upload app.py

app.py

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+"""Hugging Face Space - BTC Prediction API"""
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+import pandas as pd
+import yfinance as yf
+import torch
+import numpy as np
+import random
+from chronos import ChronosPipeline
+from datetime import date, timedelta
+from typing import Optional
+
+# ตั้ง seed
+SEED = 42
+random.seed(SEED)
+np.random.seed(SEED)
+torch.manual_seed(SEED)
+
+app = FastAPI(title="BTC Prediction API", version="1.0.0")
+
+# โหลด model ตอน startup
+model_pipeline = None
+
+@app.on_event("startup")
+async def load_model():
+    global model_pipeline
+    print("🤖 Loading Chronos model...")
+    model_pipeline = ChronosPipeline.from_pretrained(
+        "amazon/chronos-t5-tiny",
+        device_map="cpu",
+        torch_dtype=torch.float32
+    )
+    print("✅ Model loaded successfully")
+
+
+class PredictionRequest(BaseModel):
+    start_date: str = "2020-01-01"
+    window_size: int = 256
+
+
+class BatchPredictionRequest(BaseModel):
+    """สำหรับทำนายหลายวัน (ใช้ใน strategy filter)"""
+    prices: list[float]  # ราคาที่ต้องการทำนาย
+    window_size: int = 256
+
+
+def get_btc_data(start: str) -> pd.DataFrame:
+    """ดึงข้อมูล BTC"""
+    end = (date.today() + timedelta(days=1)).strftime("%Y-%m-%d")
+    btc = yf.download("BTC-USD", start=start, end=end, progress=False)
+    
+    if isinstance(btc.columns, pd.MultiIndex):
+        btc.columns = btc.columns.get_level_values(0)
+    
+    df = btc[["Close"]].copy()
+    df = df.ffill().dropna()
+    return df
+
+
+def predict_price(data: pd.DataFrame, window_size: int = 256) -> Optional[float]:
+    """ทำนายราคา"""
+    if model_pipeline is None:
+        raise RuntimeError("Model not loaded")
+    
+    if len(data) < window_size:
+        context = data['Close'].values.tolist()
+    else:
+        context = data['Close'].values[-window_size:].tolist()
+    
+    context_tensor = torch.tensor([context])
+    
+    torch.manual_seed(SEED)
+    
+    with torch.no_grad():
+        forecast = model_pipeline.predict(
+            context_tensor,
+            prediction_length=1,
+            num_samples=1
+        )
+    
+    predicted_price = forecast[0, 0, 0].item()
+    return float(predicted_price)
+
+
+@app.get("/")
+def root():
+    return {
+        "service": "BTC Prediction API",
+        "model": "amazon/chronos-t5-tiny",
+        "status": "ready" if model_pipeline else "loading"
+    }
+
+
+@app.get("/health")
+def health():
+    return {
+        "status": "ok",
+        "model_loaded": model_pipeline is not None
+    }
+
+
+@app.head("/health")
+def health_head():
+    """HEAD endpoint for uptime monitoring."""
+    return
+
+
+@app.post("/predict")
+def predict(req: PredictionRequest):
+    """ทำนายราคา BTC วันถัดไป"""
+    try:
+        if model_pipeline is None:
+            raise HTTPException(status_code=503, detail="Model is still loading")
+        
+        # ดึงข้อมูล
+        data = get_btc_data(req.start_date)
+        
+        if len(data) < 30:
+            raise HTTPException(status_code=400, detail="Not enough data")
+        
+        # ทำนาย
+        predicted_price = predict_price(data, req.window_size)
+        
+        if predicted_price is None:
+            raise HTTPException(status_code=500, detail="Prediction failed")
+        
+        # คำนวณผลลัพธ์
+        last_close = float(data["Close"].iloc[-1])
+        last_date = data.index[-1]
+        next_date = last_date + pd.Timedelta(days=1)
+        change_pct = ((predicted_price / last_close) - 1) * 100
+        
+        return {
+            "symbol": "BTC-USD",
+            "last_date": str(last_date.date()),
+            "next_date": str(next_date.date()),
+            "last_close": last_close,
+            "predicted_close": predicted_price,
+            "predicted_change_pct": float(change_pct),
+            "model": "amazon/chronos-t5-tiny",
+            "window_size": req.window_size
+        }
+        
+    except HTTPException:
+        raise
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@app.post("/predict_from_data")
+def predict_from_data(req: BatchPredictionRequest):
+    """ทำนายราคาจากข้อมูลที่ส่งมา (สำหรับ strategy filter)"""
+    try:
+        if model_pipeline is None:
+            raise HTTPException(status_code=503, detail="Model is still loading")
+        
+        # ลดข้อกำหนดจาก 30 เป็น 10 วัน
+        if len(req.prices) < 10:
+            raise HTTPException(status_code=400, detail="Not enough data (need at least 10 prices)")
+        
+        # ใช้���าคาที่ส่งมา
+        if len(req.prices) < req.window_size:
+            context = req.prices
+        else:
+            context = req.prices[-req.window_size:]
+        
+        context_tensor = torch.tensor([context])
+        
+        torch.manual_seed(SEED)
+        
+        with torch.no_grad():
+            forecast = model_pipeline.predict(
+                context_tensor,
+                prediction_length=1,
+                num_samples=1
+            )
+        
+        predicted_price = forecast[0, 0, 0].item()
+        
+        return {
+            "predicted_price": float(predicted_price),
+            "input_length": len(req.prices),
+            "window_size": req.window_size
+        }
+        
+    except HTTPException:
+        raise
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=7860)