from fastapi import FastAPI, Request
from fastapi.responses import HTMLResponse
from fastapi.templating import Jinja2Templates

app = FastAPI()

templates = Jinja2Templates(directory="templates")

@app.get("/", response_class=HTMLResponse)
async def read_root(request: Request):
    return templates.TemplateResponse("hello.html", {"request": request})


# from fastapi import FastAPI, Request, HTTPException
# from fastapi.middleware.cors import CORSMiddleware
# import warnings
# import yfinance as yf
# import pandas as pd
# import requests

# warnings.simplefilter(action='ignore', category=FutureWarning)
# warnings.filterwarnings('ignore')

# df_logo = pd.read_csv("https://raw.githubusercontent.com/jarvisx17/nifty500/main/Nifty500.csv")

# async def calculate_profit(ltp, share, entry):
#   tgt1 = entry + (0.02 * entry)
#   tgt2 = entry + (0.04 * entry)
#   if ltp > tgt2:
#       profit = round((share / 3 * (tgt1-entry)) + (share / 3 * (tgt2-entry)) + (share / 3 * (ltp-entry)), 2)
#   elif ltp > tgt1 and ltp < tgt2:
#       profit = round((share / 3 * (tgt1-entry)) + ((share / 3) * 2 * (ltp-entry)), 2)
#   elif ltp > tgt1:
#       profit = round(share * (ltp-entry), 2)   
#   else:
#       profit = round(share * (ltp-entry), 2)
#   return profit

# async def info(ticker):
#     data = df_logo[df_logo['Symbol'] == ticker]
#     logo = data.logo.values[0]
#     Industry = data.Industry.values[0]
#     return logo, Industry

# async def calculate_percentage_loss(buying_price, ltp):
#     percentage_loss = ((ltp - buying_price) / buying_price) * 100
#     return f"{percentage_loss:.2f}%"

# async def latestprice(ticker):
#     ticker = ticker.split(".")[0]
#     url = f"https://stock-market-lo24myw5sq-el.a.run.app/currentprice?ticker={ticker}"
#     response = requests.get(url)
#     if response.status_code == 200:
#         data = response.json()
#         return data['ltp']
#     else:
#         return "N/A"

# async def process_dataframe(df):

#     def get_rsi(close, lookback):
#         ret = close.diff()
#         up = []
#         down = []
#         for i in range(len(ret)):
#             if ret[i] < 0:
#                 up.append(0)
#                 down.append(ret[i])
#             else:
#                 up.append(ret[i])
#                 down.append(0)
#         up_series = pd.Series(up)
#         down_series = pd.Series(down).abs()
#         up_ewm = up_series.ewm(com=lookback - 1, adjust=False).mean()
#         down_ewm = down_series.ewm(com=lookback - 1, adjust=False).mean()
#         rs = up_ewm / down_ewm
#         rsi = 100 - (100 / (1 + rs))
#         rsi_df = pd.DataFrame(rsi).rename(columns={0: 'RSI'}).set_index(close.index)
#         rsi_df = rsi_df.dropna()
#         return rsi_df[3:]

#     df['RSI'] = get_rsi(df['Close'], 14)
#     df['SMA20'] = df['Close'].rolling(window=20).mean()
#     df.drop(['Adj Close'], axis=1, inplace=True)
#     df = df.dropna()

#     return df

# async def fin_data(ticker, startdate):

#   ltp = await latestprice(ticker)
#   df=yf.download(ticker, period="36mo", progress=False)
#   df = await process_dataframe(df)
#   df.reset_index(inplace=True)
#   df['Prev_RSI'] = df['RSI'].shift(1).round(2)
#   df = df.dropna()
#   df.reset_index(drop=True, inplace=True)
#   df[['Open', 'High', 'Low', 'Close',"RSI","SMA20"]] = df[['Open', 'High', 'Low', 'Close',"RSI", "SMA20"]].round(2)
#   df = df[200:]
#   df['Target1'] = df['High'] + (df['High'] * 0.02)
#   df['Target1'] = df['Target1'].round(2)
#   df['Target2'] = df['High'] + (df['High'] * 0.04)
#   df['Target2'] = df['Target2'].round(2)
#   df['Target3'] = "will announced"
#   df['SL'] = df['Low']
#   df['LTP'] = ltp
#   date_index = df.loc[df['Date'] == startdate].index[0]
#   df = df.loc[date_index-1:]
#   df['Date'] = pd.to_datetime(df['Date'])
#   df.reset_index(drop=True,inplace=True)

#   return df

# async def eqt(ticker, startdate, share_qty = 90):

#   df = await fin_data(ticker, startdate)
#   logo, Industry = await info(ticker)
#   entry = False
#   trading = False
#   shares_held = 0
#   buy_price = 0
#   target1 = False
#   target2 = False
#   target3 = False
#   tgt1 = 0
#   tgt2 = 0
#   tgt3 = 0
#   total_profit = 0
#   profits = []
#   stop_loss = 0
#   capital_list = []
#   data = {}
#   totalshares = share_qty
#   ltp = await latestprice(ticker)

#   for i in range(1, len(df)-1):
#       try:
#           if df.at[i, 'RSI'] > 60 and df.at[i - 1, 'RSI'] < 60 and df.at[i, 'High'] < df.at[i + 1, 'High'] and not entry and not trading:
#               buy_price = df.at[i, 'High']
#               stop_loss = df.at[i, 'Low']
#               capital = buy_price * share_qty
#               capital_list.append(capital)
#               shares_held = share_qty
#               entdate = df.at[i+1, 'Date']
#               entry_info = {"Date": pd.to_datetime(df.at[i+1, 'Date']).strftime('%d-%m-%Y'), "Note": "Entry Successful", "SL": stop_loss}
#               entryStock_info = df.iloc[i: i+1].reset_index(drop=True).to_dict(orient='records')[0] # Entry info
#               entryStock_info['Date'] = str(pd.to_datetime(df.at[i, 'Date']).strftime('%d-%m-%Y'))
#               data['StockInfo'] = {}
#               data['StockInfo']['Stock'] = {}
#               data['StockInfo']['Stock']['Name'] = ticker
#               data['StockInfo']['Stock']['Industry'] = Industry
#               data['StockInfo']['Stock']['Logo'] = logo
#               data['StockInfo']['Stock']['Status'] = "Active"
#               data['StockInfo']['Stock']['Levels'] = "Entry"
#               data['StockInfo']['Stock']['Values'] = entryStock_info
#               buying_price = entryStock_info['High']
#               ltp = entryStock_info['LTP']
#               data['StockInfo']['Stock']['Values']['Share QTY'] = share_qty
#               data['StockInfo']['Stock']['Values']['Invested Amount'] = (share_qty * buy_price).round(2)
#               data['StockInfo']['Stock']['Values']['Percentage'] = await calculate_percentage_loss(buying_price, ltp)
#               data['StockInfo']['Stock']['Values']['Total P/L'] = await calculate_profit(ltp, totalshares, buy_price)
#               data['StockInfo']['Entry'] = entry_info
#               entry = True
#               trading = True

#           if trading and not target1:
#               if (df.at[i + 1, 'High'] - buy_price) >= 0.02 * buy_price:
#                   stop_loss = buy_price
#                   target1 = True
#                   tgt1 = 0.02 * buy_price * (share_qty / 3)
#                   shares_held -= (share_qty / 3)
#                   total_profit = round(tgt1,2)
#                   target1_info = {"Date" : pd.to_datetime(df.at[i+1, 'Date']).strftime('%d-%m-%Y'), "Profit" : round(tgt1,2), "Remaining Shares": shares_held,"Note" : "TGT1 Achieved Successfully", "SL" : stop_loss}
#                   data['StockInfo']['TGT1'] = target1_info
#                   data['StockInfo']['Stock']['Values']['SL'] = stop_loss
#                   data['StockInfo']['Stock']['Levels'] = data['StockInfo']['Stock']['Levels'] + " TGT1"
#                   data['StockInfo']['Stock']['Values']['Total P/L'] = await calculate_profit(ltp, totalshares, buy_price)
#                   data['StockInfo']['Entry']['Trade Status'] = "Trading is ongoing...."

#           if trading and target1 and not target2:
#               if (df.at[i + 1, 'High'] - buy_price) >= 0.04 * buy_price:
#                   target2 = True
#                   tgt2 = 0.04 * buy_price * (share_qty / 3)
#                   total_profit += round(tgt2,2)
#                   shares_held -= (share_qty / 3)
#                   data['StockInfo']['Stock']['Levels'] = data['StockInfo']['Stock']['Levels'] + " TGT2"
#                   data['StockInfo']['Stock']['Values']['Total P/L'] = await calculate_profit(ltp, totalshares, buy_price)
#                   target2_info = {"Date" : pd.to_datetime(df.at[i+1, 'Date']).strftime('%d-%m-%Y'), "Profit" : round(tgt2,2), "Remaining Shares": shares_held,"Note" : "TGT2 Achieved Successfully", "SL" : stop_loss}
#                   data['StockInfo']['TGT2'] = target2_info
#                   data['StockInfo']['Entry']['Trade Status']  = "Trading is ongoing...."

#           if trading and target2 and not target3:
#               if (df.at[i + 1, 'Open'] < df.at[i + 1, 'SMA20'] < df.at[i + 1, 'Close']) or (df.at[i + 1, 'Open'] > df.at[i + 1, 'SMA20'] > df.at[i + 1, 'Close']):
#                   stop_loss = df.at[i + 1, 'Low']
#                   data['StockInfo']['Stock']['Values']['SL'] = stop_loss
#                   if df.at[i + 2, 'Low'] < stop_loss:
#                       target3 = True
#                       tgt3 = stop_loss * (share_qty / 3)
#                       shares_held -= (share_qty / 3)
#                       total_profit += round(tgt3,2)
#                       target3_info = {"Date" : pd.to_datetime(df.at[i+1, 'Date']).strftime('%d-%m-%Y'), "Profit" : round(tgt3,2), "Remaining Shares": shares_held,"Note" : "TGT3 Achieved Successfully", "SL" : stop_loss}
#                       data['StockInfo']['Stock']['Values']['Target3'] = tgt3
#                       data['StockInfo']['TGT3'] = target3_info
#                       data['StockInfo']['Stock']['Levels'] = data['StockInfo']['Stock']['Levels'] +" TGT3"
#                       data['StockInfo']['Stock']['Values']['Total P/L'] = await calculate_profit(ltp, totalshares, buy_price)
#                       data['StockInfo']['TotalProfit'] = {}
#                       data['StockInfo']['TotalProfit']['Profit'] = total_profit
#                       data['StockInfo']['Entry']['Trade Status']  = "Trade closed successfully...."
#                       data['StockInfo']['TotalProfit']['Trade Status']  = "Trade closed successfully...."
#                       break

#           if (df.at[i + 1, 'Low'] < stop_loss and trading and entdate != df.at[i + 1, 'Date']) or stop_loss > ltp:
#               profit_loss = (shares_held * stop_loss) - (shares_held * buy_price)
#               total_profit += profit_loss
#               profits.append(total_profit)
#               shares_held = 0
#               if data['StockInfo']['Stock']['Values']['Target3'] == "will announced" :
#                 data['StockInfo']['Stock']['Values']['Target3'] = "-"
#               data['StockInfo']['Stock']['Status'] = "Closed"
#               data['StockInfo']['Stock']['Levels'] = data['StockInfo']['Stock']['Levels'] +" SL"
#               stoploss_info = {"Date" : pd.to_datetime(df.at[i+1, 'Date']).strftime('%d-%m-%Y'), "Profit" : total_profit, "SL" : stop_loss, "Remaining Shares": shares_held,"Note" : "SL Hit Successfully"}
#               data['StockInfo']['SL'] = stoploss_info
#               data['StockInfo']['TotalProfit'] = {}
#               data['StockInfo']['TotalProfit']['Profit'] = round(total_profit, 2)
#               data['StockInfo']['Stock']['Values']['Total P/L'] = round(total_profit, 2)
#               data['StockInfo']['Entry']['Trade Status']  = "Trade closed successfully...."
#               data['StockInfo']['TotalProfit']['Trade Status']  = "Trade closed successfully...."
#               buy_price = 0
#               entry = False
#               trading = False
#               target1 = target2 = target3 = False
#               tgt1 = tgt2 = tgt3 = 0
#               total_profit = 0
#               break

#       except IndexError:
#           continue

#   if capital_list and profits:

#     return data

#   else:
#     if data:

#       return data

#     else:
#       data['StockInfo'] = {}
#       data['StockInfo']['Stock'] = {}
#       data['StockInfo']['Stock']['Name']  = ticker
#       data['StockInfo']['Stock']['Industry'] = Industry
#       data['StockInfo']['Stock']['Logo'] = logo
#       data['StockInfo']['Stock']['Status'] = "Waiting for entry"
#       entryStock_info = df.iloc[1: 2].reset_index(drop=True).to_dict(orient='records')[0] # Entry info
#       entryStock_info['Date'] = str(pd.to_datetime(df.at[1, 'Date']).strftime('%d-%m-%Y'))
#       data['StockInfo']['Stock']['Values'] = entryStock_info
#       data['StockInfo']['Stock']['Values']['Target3'] = "-"
#       data['StockInfo']['Info'] = "Don't buy stock right now...."

#       return data


# app = FastAPI()

# origins = ["*"]

# app.add_middleware(
#     CORSMiddleware,
#     allow_origins=origins,
#     allow_credentials=True,
#     allow_methods=["*"],
#     allow_headers=["*"],
# )

# @app.get('/')
# def index():
#     return {"message": "welcome to Investify"}

# # @app.post('/process_stock_details')
# # async def process_stock_details(request: Request):
# #     data = await request.json()
# #     processed_data = {
# #         'symbol': data['symbol'],
# #         'date': data['date'],
# #         'share': data['share']
# #     }
# #     return processed_data

# @app.get('/data')
# async def get_data(ticker: str, date: str, qty: int):
#     try:
#         response = await eqt(ticker, date, qty)
#         return response
#     except:
#         return {"Timeout" : "Error"}
        

# # @app.post('/data')
# # async def post_data(request: Request):
# #     data = await request.json()
# #     ticker = data.get('ticker')
# #     date = data.get('date')
# #     share_qty = data.get('qty')
# #     response = data_manager.get_equity_data(ticker, date, share_qty)
# #     return response
    
# # if __name__ == "__main__":
# #     import uvicorn
# #     uvicorn.run(app, host="0.0.0.0", port=8000)