clean commit: only app.py, README.md, requirements.txt

README.md

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+# Chronos Stock Forecast (Gradio on Hugging Face Spaces)
+
+- Zero-shot time series forecasting with amazon/chronos-bolt-*
+- UI: Gradio
+- Data: yfinance Close prices
+
+## Local run
+pip install -r requirements.txt
+python app.py

app.py

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+import os
+import datetime as dt
+import pandas as pd
+import torch
+import gradio as gr
+import yfinance as yf
+
+from chronos import BaseChronosPipeline  # from 'chronos-forecasting'
+
+# ---- 전역 캐시: 모델을 한 번만 로드해 재사용 ----
+_PIPELINE_CACHE = {}
+
+def get_pipeline(model_id: str, device: str = "cpu"):
+    key = (model_id, device)
+    if key not in _PIPELINE_CACHE:
+        _PIPELINE_CACHE[key] = BaseChronosPipeline.from_pretrained(
+            model_id,
+            device_map=device,  # "cpu" / "cuda" (Spaces 기본은 cpu)
+            torch_dtype=torch.float32 if device == "cpu" else torch.bfloat16,
+        )
+    return _PIPELINE_CACHE[key]
+
+# ---- 주가 데이터 로딩 (yfinance) ----
+def load_close_series(ticker: str, start: str, end: str, interval: str = "1d"):
+    # 한국 주식은 예: 005930.KS (삼성전자)
+    df = yf.download(ticker, start=start, end=end, interval=interval, progress=False)
+    if df.empty or "Close" not in df:
+        raise ValueError("데이터가 없거나 'Close' 열을 찾을 수 없습니다. 티커/날짜를 확인하세요.")
+    s = df["Close"].dropna().astype(float)
+    return s
+
+# ---- 예측 함수 (Gradio가 호출) ----
+def run_forecast(ticker, start_date, end_date, horizon, model_id, device, interval):
+    try:
+        series = load_close_series(ticker, start_date, end_date, interval)
+    except Exception as e:
+        return gr.Plot.update(None), pd.DataFrame(), f"데이터 로딩 오류: {e}"
+
+    pipe = get_pipeline(model_id, device)
+    H = int(horizon)
+
+    # Chronos 입력: 1D 텐서 (float)
+    context = torch.tensor(series.values, dtype=torch.float32)
+
+    # 출력: (num_series=1, num_quantiles=3, H)
+    #    보통 q=[0.1, 0.5, 0.9]
+    preds = pipe.predict(context=context, prediction_length=H)[0]
+    q10, q50, q90 = preds[0], preds[1], preds[2]
+
+    # 표 데이터
+    df_fcst = pd.DataFrame(
+        {"q10": q10.numpy(), "q50": q50.numpy(), "q90": q90.numpy()},
+        index=pd.RangeIndex(1, H + 1, name="step"),
+    )
+
+    # 그래프
+    import matplotlib.pyplot as plt
+    fig = plt.figure(figsize=(10, 4))
+    plt.plot(series.index, series.values, label="history")
+    # 미래 구간 x축 만들기: 종가가 일 단위라 'D' 주기 사용
+    future_index = pd.date_range(series.index[-1], periods=H + 1, freq="D")[1:]
+    plt.plot(future_index, q50.numpy(), label="forecast(q50)")
+    plt.fill_between(future_index, q10.numpy(), q90.numpy(), alpha=0.2, label="q10–q90")
+    plt.title(f"{ticker} forecast by Chronos-Bolt")
+    plt.legend()
+    plt.tight_layout()
+
+    note = "※ 데모 목적입니다. 투자 판단의 책임은 본인에게 있습니다."
+    return fig, df_fcst, note
+
+# ---- Gradio UI ----
+with gr.Blocks(title="Chronos Stock Forecast") as demo:
+    gr.Markdown("# Chronos 주가 예측 데모")
+    with gr.Row():
+        ticker = gr.Textbox(value="AAPL", label="티커 (예: AAPL, MSFT, 005930.KS)")
+        horizon = gr.Slider(5, 60, value=20, step=1, label="예측 길이 H (일)")
+    with gr.Row():
+        start = gr.Textbox(value=(dt.date.today()-dt.timedelta(days=365)).isoformat(), label="시작일 (YYYY-MM-DD)")
+        end = gr.Textbox(value=dt.date.today().isoformat(), label="종료일 (YYYY-MM-DD)")
+    with gr.Row():
+        model_id = gr.Dropdown(
+            choices=[
+                "amazon/chronos-bolt-tiny",
+                "amazon/chronos-bolt-mini",
+                "amazon/chronos-bolt-small",
+                "amazon/chronos-bolt-base",
+            ],
+            value="amazon/chronos-bolt-small",
+            label="모델"
+        )
+        device = gr.Dropdown(choices=["cpu"], value="cpu", label="디바이스")
+        interval = gr.Dropdown(choices=["1d"], value="1d", label="간격")
+    btn = gr.Button("예측 실행")
+
+    plot = gr.Plot(label="History + Forecast")
+    table = gr.Dataframe(label="예측 결과 (분위수)")
+    note = gr.Markdown()
+
+    btn.click(
+        fn=run_forecast,
+        inputs=[ticker, start, end, horizon, model_id, device, interval],
+        outputs=[plot, table, note]
+    )
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+gradio>=4.44
+pandas>=2.2
+yfinance>=0.2
+torch>=2.2 ; platform_system != "Darwin"
+chronos-forecasting>=1.0
+matplotlib>=3.8