app.py

import streamlit as st
import pandas as pd
import numpy as np
from prophet import Prophet
import plotly.express as px
import plotly.graph_objects as go
import seaborn as sns
import matplotlib.pyplot as plt
from datetime import date

# ---------------------------------------------
# CONFIG --------------------------------------
# ---------------------------------------------
DATA_PATH = "price_data.csv"  # CSV: date, item, price
MACRO_START, MACRO_END = "1996-01-01", "2030-12-31"
MICRO_START, MICRO_END = "2020-01-01", "2026-12-31"

st.set_page_config(page_title="품목별 가격 예측", page_icon="📈", layout="wide")

# ---------------------------------------------
# UTILITIES -----------------------------------
# ---------------------------------------------
@st.cache_data(show_spinner=False)
def load_data(path: str) -> pd.DataFrame:
    df = pd.read_csv(path, parse_dates=["date"])
    df.sort_values("date", inplace=True)
    return df

@st.cache_data(show_spinner=False)
def get_items(df: pd.DataFrame):
    return sorted(df["item"].unique())

@st.cache_data(show_spinner=False)
def fit_prophet(df: pd.DataFrame, horizon_end: str):
    m = Prophet(yearly_seasonality=True, weekly_seasonality=False, daily_seasonality=False)
    m.fit(df.rename(columns={"date": "ds", "price": "y"}))
    future = m.make_future_dataframe(periods=(pd.Timestamp(horizon_end) - df["date"].max()).days, freq="D")
    forecast = m.predict(future)
    return m, forecast

# ---------------------------------------------
# LOAD ----------------------------------------
# ---------------------------------------------
raw_df = load_data(DATA_PATH)
selected_item = st.sidebar.selectbox("🔍 품목 선택", get_items(raw_df))
current_date = date.today()
st.sidebar.write(f"**오늘:** {current_date}")

item_df = raw_df[raw_df["item"] == selected_item].copy()
if item_df.empty:
    st.error("데이터가 없습니다.")
    st.stop()

# ---------------------------------------------
# MACRO FORECAST ------------------------------
# ---------------------------------------------
st.header(f"📈 {selected_item} 가격 전망")
macro_df = item_df[item_df["date"] >= MACRO_START]

m_macro, fc_macro = fit_prophet(macro_df, MACRO_END)
fig_macro = px.line(fc_macro, x="ds", y="yhat", title="Macro Forecast 1996–2030")
fig_macro.add_scatter(x=macro_df["date"], y=macro_df["price"], mode="lines", name="Actual")
st.plotly_chart(fig_macro, use_container_width=True)

# --- Metrics ↓
latest_price = macro_df.iloc[-1]["price"]
macro_last = fc_macro[fc_macro["ds"] == MACRO_END]["yhat"].iloc[0]
macro_diff = macro_last - latest_price
macro_pct = macro_diff / latest_price * 100
st.metric(label="2030 예측 가격", value=f"{macro_last:,.0f}", delta=f"{macro_pct:+.1f}% vs 최근")

# ---------------------------------------------
# MICRO FORECAST ------------------------------
# ---------------------------------------------
st.subheader("🔎 미시 예측 2024–2026")

micro_df = item_df[item_df["date"] >= MICRO_START]

m_micro, fc_micro = fit_prophet(micro_df, MICRO_END)
fig_micro = px.line(fc_micro, x="ds", y="yhat", title="Micro Forecast 2024–2026")
fig_micro.add_scatter(x=micro_df["date"], y=micro_df["price"], mode="lines", name="Actual")
st.plotly_chart(fig_micro, use_container_width=True)

micro_last = fc_micro[fc_micro["ds"] == MICRO_END]["yhat"].iloc[0]
micro_diff = micro_last - latest_price
micro_pct = micro_diff / latest_price * 100
st.metric(label="2026 예측 가격", value=f"{micro_last:,.0f}", delta=f"{micro_pct:+.1f}% vs 최근")

# ---------------------------------------------
# PATTERN & SEASONALITY -----------------------
# ---------------------------------------------
with st.expander("📆 시즈널리티 분석 및 패턴 해설"):
    comp_fig = m_micro.plot_components(fc_micro)
    st.pyplot(comp_fig)

    # 월별 seasonality summary
    month_season = (fc_micro[["ds", "yearly"]]
                    .assign(month=lambda d: d["ds"].dt.month)
                    .groupby("month")["yearly"].mean())
    peak_month = int(month_season.idxmax())
    trough_month = int(month_season.idxmin())

    st.markdown(f"**패턴 요약**  \n- __연간 피크__: {peak_month}월   \n- __연간 저점__: {trough_month}월  \n- 평균 변동폭(연): {month_season.max() - month_season.min():.1f} 단가")

# ---------------------------------------------
# CORRELATION HEATMAP -------------------------
# ---------------------------------------------
st.subheader("🧮 품목 간 상관관계 히트맵")

corr_df = (raw_df.assign(month=lambda d: d["date"].dt.to_period("M"))
                   .groupby(["month", "item"], as_index=False)["price"].mean()
                   .pivot(index="month", columns="item", values="price"))

corr = corr_df.corr()

fig, ax = plt.subplots(figsize=(12, 10))
mask = np.triu(np.ones_like(corr, dtype=bool))
sns.heatmap(corr, mask=mask, cmap="RdBu_r", center=0, linewidths=.5, ax=ax)
st.pyplot(fig)

st.markdown("""
**해석 가이드**  
- **빨간색(+)**: 두 품목 가격이 동조화 – 공급망/수요 연동 가능성.  
- **파란색(-)**: 대체재 관계.  
- 절댓값 ≥ 0.7 은 정책·재고 전략 설계 시 주의 깊게 볼 필요가 있습니다.
""")

# ---------------------------------------------
# EXTRA CHARTS -------------------------------
# ---------------------------------------------
st.subheader("📊 추가 인사이트: 30일 이동 변동성")

vol_df = (item_df.set_index("date")["price"].rolling(30).std().reset_index())
fig_vol = px.area(vol_df, x="date", y="price", title="30D Rolling Std Dev")
st.plotly_chart(fig_vol, use_container_width=True)

st.markdown("""
- 변동성 급등 구간은 **공급 충격·수요 이벤트** 가능성이 높습니다.  
- 최근 변동성이 낮아지면 **가격 안착**으로 해석할 수 있습니다.
""")

# ---------------------------------------------
# FOOTER --------------------------------------
# ---------------------------------------------
st.caption("데이터 출처: 내부 농수산물 가격 DB · Forecast by Prophet · Dashboard built with Streamlit")