import streamlit as st
import pandas as pd
import plotly.graph_objects as go
import matplotlib.pyplot as plt
from prophet import Prophet

# --- Page Configuration ---
st.set_page_config(
    page_title="Forecast",
    page_icon="📈",
    layout="centered",
    initial_sidebar_state="expanded"
)

# --- Title and Description ---
st.title("📈Climate Forecasting")
st.markdown(
    """
    Upload your historical weather CSV (columns: `date`, `meantemp`, `humidity`, 
    `wind_speed`, `meanpressure`) and choose which forecasts to view.
    """
)

# --- Sidebar Feature Selection ---
st.sidebar.header("Application Features")
FEATURES = {
    "Temperature Forecast": "meantemp",
    "Humidity Forecast": "humidity",
    "Wind Speed Forecast": "wind_speed",
    "Mean Pressure Forecast": "meanpressure"
}
selected = {label: st.sidebar.checkbox(label, value=True) for label in FEATURES}
periods = st.sidebar.slider("Days to Forecast", 30, 365, 180, step=30)

# --- Color Palette ---
FEATURE_COLORS = {
    "Temperature Forecast": "#EF553B",   # red
    "Humidity Forecast": "#00CC96",      # green
    "Wind Speed Forecast": "#636EFA",    # blue
    "Mean Pressure Forecast": "#AB63FA", # purple
}

# --- File Upload ---
uploaded_file = st.file_uploader("Upload CSV", type=["csv"])
if not uploaded_file:
    st.info("Awaiting CSV upload...")
    st.stop()

# --- Load & Preprocess Data ---
df = pd.read_csv(uploaded_file)
df.rename(columns={"date": "ds"}, inplace=True)
df["ds"] = pd.to_datetime(df["ds"])
st.subheader("Data Preview")
st.dataframe(df.head())

# --- Forecast Function ---
def make_and_plot(df: pd.DataFrame, col: str, label: str):
    data = df[["ds", col]].dropna().rename(columns={col: "y"})
    if data.empty:
        st.warning(f"No data found for {label}. Skipping.")
        return

    model = Prophet()
    model.fit(data)

    future = model.make_future_dataframe(periods=periods)
    forecast = model.predict(future)

    # Split historical vs future
    last_actual_date = data["ds"].max()
    historical = forecast[forecast["ds"] <= last_actual_date]
    predicted = forecast[forecast["ds"] > last_actual_date]

    color_hist = "#1f77b4"
    color_fore = FEATURE_COLORS.get(label, "#FF7F0E")

    # --- Plot ---
    st.subheader(f"{label}")
    fig = go.Figure()
    fig.add_trace(go.Scatter(
        x=historical["ds"], y=historical["yhat"],
        mode="lines",
        name="Historical",
        line=dict(color=color_hist)
    ))
    fig.add_trace(go.Scatter(
        x=predicted["ds"], y=predicted["yhat"],
        mode="lines",
        name="Forecast",
        line=dict(color=color_fore, dash="dash")
    ))
    fig.update_layout(
        title=f"{label} (Historical vs Forecasted)",
        xaxis_title="Date",
        yaxis_title=label,
        legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1)
    )
    st.plotly_chart(fig, use_container_width=True)

    # --- Forecast Table ---
    st.write("Latest Forecast Values")
    st.dataframe(predicted[["ds", "yhat", "yhat_lower", "yhat_upper"]].tail(),
                 column_config={
                     "ds": "Date",
                     "yhat": "Forecasted",
                     "yhat_lower": "Lower Bound",
                     "yhat_upper": "Upper Bound"
                 })

    # --- Trend and Seasonality ---
    st.write("Trend and Seasonality Components")
    components = model.plot_components(forecast)
    st.pyplot(components)

# --- Run Forecasts Based on User Selection ---
for label, col in FEATURES.items():
    if selected[label]:
        make_and_plot(df, col, label)

# --- Footer ---
st.markdown("---")
st.caption("Built with ❤️ using Streamlit & Prophet • © 2025")