Delete app.py

app.py

@@ -1,187 +0,0 @@
-"""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.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")
-        
-        if len(req.prices) < 30:
-            raise HTTPException(status_code=400, detail="Not enough data (need at least 30 prices)")
-        
-        # ใช้ราคาที่ส่งมา
-        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)