import gradio as gr
import pandas as pd
import numpy as np
from prophet import Prophet
from mysql import connector
import json
import random
from prophet.serialize import model_from_json

def get_data():
    _conn = connector.connect(
        host='110.238.111.32',
        user = 'outsider',
        password='Hack2024',
        database = 'TheSimp'
    )
    _cursor = _conn.cursor()
    _query = "SELECT name , province ,a_type ,genre ,close ,hour ,link FROM places"
    _cursor.execute(_query)
    _data = _cursor.fetchall()
    _cursor.close()
    _conn.close()
    _df = pd.DataFrame(_data,columns=['name','province','a_type','genre','close','hour','link'])
    return _df
def get_dataset():
    _conn = connector.connect(
        host='110.238.111.32',
        user = 'outsider',
        password='Hack2024',
        database = 'TheSimp'
    )
    _cursor = _conn.cursor()
    _query = "SELECT id , ds ,a_temp ,m_temp ,n_temp ,y ,a_pres ,m_pres ,a_ws ,m_ws ,a_humi ,m_humi ,n_humi ,a_vis flat,m_vis ,n_vis  FROM weather_new"
    _cursor.execute(_query)
    _data = _cursor.fetchall()
    _cursor.close()
    _conn.close()
    _df = pd.DataFrame(_data,columns=['id','ds','a_temp','m_temp','n_temp','y','a_pres','m_pres','a_ws','m_ws','a_humi','m_humi','n_humi' ,'a_vis','m_vis' ,'n_vis']).drop('id',axis=1)
    return _df
    



province_mapping = {
    'Bangkok': 'กรุงเทพฯ',
    'Nakohn Pathom': 'นครปฐม',
    'Pathum Thani': 'ปทุมธานี',
    'Nakohn Nayok': 'นครนายก',
    'Nonthaburi': 'นนทบุรี',
    'Samut Songkhram': 'สมุทรสงคราม'
}


with open('prophet_model.json', 'r') as fin:
   prophet_model = model_from_json(json.load(fin))


def will_rain(year, month, date):
    _date = pd.to_datetime(f'{year}-{month}-{date}')
    _df = pd.DataFrame({'ds': [_date]})
    _prediction = prophet_model.predict(_df)
    _prediction = float(_prediction['yhat'])
    return _prediction >= 0.5

def get_advice(province, activity, purpose, year, month, date):
    is_rain = will_rain(year, month, date)
    activity = 'indoor' if is_rain else activity.lower()

    province = province_mapping[province]
    places = get_data()
    places.replace({'indoor ': 'indoor', 'outdoor ': 'outdoor'}, inplace=True)
    places = places[(places['province'] == province) & (places['a_type'] == activity) & (places['genre'] == purpose.lower())]
    
    
    random_idx = np.random.randint(0, len(places))
    place_name = places.iloc[random_idx]['name']
    close_day = places.iloc[random_idx]['close']
    open_hour = places.iloc[random_idx]['hour']
    
    advice = f"It might rain on {year}-{month}-{date}, so we suggest you to go indoor place such as {place_name}." if is_rain else f"It might not rain on {year}-{month}-{date}, so we suggest you to go to place such as {place_name}."
    
    return advice, place_name, close_day, open_hour

iface = gr.Interface(
    fn=get_advice,
    inputs=[
        gr.Dropdown(["Bangkok", 'Nakohn Pathom', 'Pathum Thani', 'Nakohn Nayok', 'Nonthaburi', 'Samut Songkhram'], label="Province",),
        gr.Dropdown(["indoor", "outdoor"], label="Activity"),
        gr.Dropdown(["shopping", "relax", 'education', 'culture', 'nature'], label="Purpose"),
        gr.Dropdown([2024], label="Year"),
        gr.Dropdown([i for i in range(1, 13)], label="Month"),
        gr.Dropdown([i for i in range(1, 32)], label="Date"),
    ],
    outputs=[
        gr.components.Textbox(label='Advice'),
        gr.components.Textbox(label="Place Name"),
        gr.components.Textbox(label="Close day"),
        gr.components.Textbox(label="Open hour"),
    ],
    live=True,
    title="Right place, Right day",
    description="Get the weather forecast for a place you would like to go.",
    theme="default"
)

iface.launch()