from fastapi import FastAPI
from fastapi.staticfiles import StaticFiles
from fastapi.responses import FileResponse
import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression
import httpx  # 新增：用於呼叫政府 Open API

app = FastAPI()

# --- 現有的 AI 分析邏輯與數據 (保持不變) ---
data_sources = {
    "housing": pd.DataFrame({
        "year": [2018, 2019, 2020, 2021, 2022, 2023],
        "hk_island_avg_sqft": [16000, 16500, 16200, 16800, 15500, 15000],
        "kowloon_avg_sqft": [13000, 13500, 13300, 13700, 12800, 12500],
        "nt_avg_sqft": [11000, 11400, 11200, 11600, 10800, 10500]
    }),
    "traffic": pd.DataFrame({
        "month": [1, 2, 3, 4, 5, 6],
        "mass_transit": [4500, 4200, 4600, 4700, 4800, 4850],
        "bus_co": [3800, 3500, 3900, 3950, 4000, 4100],
        "ferry": [120, 110, 130, 135, 140, 145]
    }),
    "population": pd.DataFrame({
        "year": [2018, 2019, 2020, 2021, 2022, 2023],
        "total_population": [7451000, 7500700, 7481800, 7401500, 7346900, 7498100]
    })
}

def train_model(X, y):
    model = LinearRegression()
    model.fit(X, y)
    return model

@app.get("/api/analyze/{category}")
def analyze_data(category: str):
    if category == "housing":
        df = data_sources["housing"]
        X = df[['year']]
        y = df['nt_avg_sqft']
        model = train_model(X, y)
        pred_2024 = model.predict([[2024]])[0]
        return {"category": "房價分析 (地政總署數據結構)", "prediction": f"2024 年新界區預測平均呎價: ${pred_2024:.2f}", "status": "模型訓練完成 (Linear Regression)"}
    elif category == "traffic":
        df = data_sources["traffic"]
        X = df[['month']]
        y = df['mass_transit']
        model = train_model(X, y)
        pred_next_month = model.predict([[7]])[0]
        return {"category": "交通流量 (運輸署數據結構)", "prediction": f"7月份集體運輸系統預測日均客流: {pred_next_month:.0f} 千人次", "status": "分析涵蓋集體運輸、巴士與渡輪清算數據"}
    elif category == "population":
        df = data_sources["population"]
        X = df[['year']]
        y = df['total_population']
        model = train_model(X, y)
        pred_2024 = model.predict([[2024]])[0]
        return {"category": "人口趨勢 (統計處數據結構)", "prediction": f"2024 年預測總人口: {int(pred_2024)} 人", "status": "趨勢分析完成"}
    return {"error": "未知的分類"}

# --- 新增：KMB 路線數據代理 API ---
@app.get("/api/kmb/routes")
async def get_kmb_routes():
    url = "https://data.etabus.gov.hk/v1/transport/kmb/route/"
    async with httpx.AsyncClient() as client:
        try:
            response = await client.get(url, timeout=10.0)
            return response.json()
        except Exception as e:
            return {"error": f"無法獲取開放數據: {str(e)}", "data": []}

# 掛載靜態檔案
app.mount("/", StaticFiles(directory="static", html=True), name="static")