from nsepython import *
import seaborn as sns
import pandas as pd
import streamlit as st
from datetime import datetime
from time import gmtime, strftime
from IPython.display import clear_output 
import matplotlib.pyplot as plt
from pytz import timezone 






def get_data():
    print('Hello')
    a=nse_fno("BANKNIFTY")
    print(a)
    last_prices=round(nse_quote_ltp("BANKNIFTY"))
    exp=list(set(a['expiryDates']))
    exp.sort(key = lambda date: datetime.strptime(date, '%d-%b-%Y')) 
    # print(exp)
    if last_prices%100>50:
        x=(last_prices-last_prices%100+100)
        strike=[x-200,x-100,x,x+100,x+200]
    elif last_prices%100<50:
        x=(last_prices-last_prices%100)
        strike=[x-200,x-100,x,x+100,x+200]
    d={'call change op':[],
        'call vwap':[],
        '% change op':[],
        'strike':[],
        'put change op':[],
        'put vwap':[],
        '% change op put':[]
        }
    for i in a['stocks']:
        for sp in strike: 
            if i['metadata']['expiryDate']==exp[0] and i['metadata']['optionType']=='Call' and i['metadata']['strikePrice']==sp:
                d['strike'].append(sp)
                d['call change op'].append(i['marketDeptOrderBook']['tradeInfo']['changeinOpenInterest'])
                d['% change op'].append(i['marketDeptOrderBook']['tradeInfo']['pchangeinOpenInterest'])
                d['call vwap'].append(i['marketDeptOrderBook']['tradeInfo']['vmap'])

            elif i['metadata']['expiryDate']==exp[0] and i['metadata']['optionType']=='Put' and i['metadata']['strikePrice']==sp:
                d['put change op'].append(i['marketDeptOrderBook']['tradeInfo']['changeinOpenInterest'])
                d['% change op put'].append(i['marketDeptOrderBook']['tradeInfo']['pchangeinOpenInterest'])
                d['put vwap'].append(i['marketDeptOrderBook']['tradeInfo']['vmap'])

    out=pd.json_normalize(d)
    out=out.explode(list(out.columns)).reset_index(drop = True)
    out.fillna(0,inplace=True)
    x=out.astype(float).round(2)
    return x
# def get_info(dataset):
#     df= pd.DataFrame(columns=['value', 'pcr', 'cal_per','put_per'])
#     value= dataset['put change op'].sum() - dataset['call change op'].sum()
#     pcr= dataset['put change op'].sum()/dataset['call change op'].sum()
#     cal_per= dataset['% change op'].mean()
#     put_per= dataset['% change op put'].mean()
#     new_row={'time':datetime.now(timezone("Asia/Kolkata")).strftime('%I.%M %p'),'value':value, 'pcr':round(pcr,2), 'cal_per':round(cal_per,2), 'put_per':round(put_per,2)}
#     df = df.append(new_row,ignore_index=True, verify_integrity=False, sort=None)
#     return df  
# def ploting():
#         try:
#             global final
#         except:
#              df = pd.DataFrame(columns=['value', 'pcr', 'cal_per','put_per'])
#         dataset= get_data()
#         main= get_info(dataset)
#         final =final.append(main,ignore_index=True, verify_integrity=False, sort=None)
#         return dataset,final

# final = pd.DataFrame(columns=['value', 'pcr', 'cal_per','put_per','time'])
    

# if __name__=='__main__':
    
#     st.title('WELCOME BULLS CARTEL')
#     today_date =strftime("%d %b %Y", gmtime()),datetime.now(timezone("Asia/Kolkata")).strftime('%I.%M %p')
#     st.markdown(f"as at {today_date}")
#     option= st.selectbox(
#     'How would you like to be contacted?',
#     ('5', '10', '15')) 
#     st.write('You selected:', option)
#     st.header('Important Information')
#     st.markdown(""" CALL % INCREASE MEANS MARKET GOES DOWN  
#              PUT % INCREASE MEANS MARKET GOES UP
#              """)    
#     while True:
#         dataset,final=ploting()
#         p1=st.empty()
#         p2=st.empty()
#         p3=st.empty()
#         p1.dataframe(dataset.style.highlight_max(['% change op put','% change op'],axis=0)) #Column hightlight 
#         p2.dataframe(final.style.highlight_max(['cal_per','put_per'],axis=1)) # row highlight
#         fig, ax = plt.subplots(figsize=(6, 2)) 
#         ax.plot(final['time'],final['pcr'])
#         ax.axhline(y=0, color='black', linestyle='solid') # 0 line graph
#         fig.autofmt_xdate(rotation=70)
#         p3.pyplot(fig)
#         time.sleep(3*60) # how to the start again code check upper condition min * sec
#         p1.empty() # then clean all data frame 
#         p2.empty()
#         p3.empty()
                   

import streamlit as st
import pandas as pd
st.title('A Simple Streamlit Web App')
option= st.selectbox(
    'How would you like to be contacted?',
    ('5', '10', '15'))
st.write('You selected:', option)
st.header('Important Information')
st.markdown(""" CALL % INCREASE MEANS MARKET GOES DOWN  
             PUT % INCREASE MEANS MARKET GOES UP
             """)
x = st.slider('Select an integer x', 0, 10, 1)
y = st.slider('Select an integer y', 0, 10, 1)
df = pd.DataFrame({'x': [x], 'y': [y] , 'x + y': [x + y]}, index = ['addition row'])
# if st.button('add'):
#     result = get_data()
#     st.dataframe(result)
# df = (get_data())
st.write(df)
# result = get_data()
print('banknifty')
a=(nse_quote_ltp("BANKNIFTY") ,'bank  ')
st.write(a)
# st.write(result)