import gradio as gr
import pandas as pd
import requests
from prophet import Prophet
import plotly.graph_objs as go
import math
import numpy as np

# Constants for API endpoints
OKX_TICKERS_ENDPOINT = "https://www.okx.com/api/v5/market/tickers?instType=SPOT"
OKX_CANDLE_ENDPOINT = "https://www.okx.com/api/v5/market/candles"

TIMEFRAME_MAPPING = {
    "1m": "1m",
    "5m": "5m",
    "15m": "15m",
    "30m": "30m",
    "1h": "1H",
    "2h": "2H",
    "4h": "4H",
    "6h": "6H",
    "12h": "12H",
    "1d": "1D",
    "1w": "1W",
}

# Function to calculate technical indicators
def calculate_technical_indicators(df):
    # RSI Calculation
    delta = df['close'].diff()
    gain = (delta.where(delta > 0, 0)).rolling(window=14).mean()
    loss = (-delta.where(delta < 0, 0)).rolling(window=14).mean()
    rs = gain / loss
    df['RSI'] = 100 - (100 / (1 + rs))
    
    # MACD Calculation
    exp1 = df['close'].ewm(span=12, adjust=False).mean()
    exp2 = df['close'].ewm(span=26, adjust=False).mean()
    df['MACD'] = exp1 - exp2
    df['Signal_Line'] = df['MACD'].ewm(span=9, adjust=False).mean()
    
    # Bollinger Bands Calculation
    df['MA20'] = df['close'].rolling(window=20).mean()
    df['BB_upper'] = df['MA20'] + 2 * df['close'].rolling(window=20).std()
    df['BB_lower'] = df['MA20'] - 2 * df['close'].rolling(window=20).std()
    
    return df

# Function to create technical analysis charts
def create_technical_charts(df):
    # Price and Bollinger Bands Chart
    fig1 = go.Figure()
    fig1.add_trace(go.Candlestick(
        x=df['timestamp'],
        open=df['open'],
        high=df['high'],
        low=df['low'],
        close=df['close'],
        name='Price'
    ))
    fig1.add_trace(go.Scatter(x=df['timestamp'], y=df['BB_upper'], name='Upper BB', line=dict(color='gray', dash='dash')))
    fig1.add_trace(go.Scatter(x=df['timestamp'], y=df['BB_lower'], name='Lower BB', line=dict(color='gray', dash='dash')))
    fig1.update_layout(title='Price and Bollinger Bands', xaxis_title='Date', yaxis_title='Price')

    # RSI Chart
    fig2 = go.Figure()
    fig2.add_trace(go.Scatter(x=df['timestamp'], y=df['RSI'], name='RSI'))
    fig2.add_hline(y=70, line_dash="dash", line_color="red")
    fig2.add_hline(y=30, line_dash="dash", line_color="green")
    fig2.update_layout(title='RSI Indicator', xaxis_title='Date', yaxis_title='RSI')

    # MACD Chart
    fig3 = go.Figure()
    fig3.add_trace(go.Scatter(x=df['timestamp'], y=df['MACD'], name='MACD'))
    fig3.add_trace(go.Scatter(x=df['timestamp'], y=df['Signal_Line'], name='Signal Line'))
    fig3.update_layout(title='MACD', xaxis_title='Date', yaxis_title='Value')

    return fig1, fig2, fig3

# Fetch available symbols from OKX API
def fetch_okx_symbols():
    try:
        resp = requests.get(OKX_TICKERS_ENDPOINT)
        data = resp.json().get("data", [])
        symbols = [item["instId"] for item in data if item.get("instType") == "SPOT"]
        return ["BTC-USDT"] + symbols if symbols else ["BTC-USDT"]
    except Exception as e:
        return ["BTC-USDT"]

# Fetch historical candle data from OKX API
def fetch_okx_candles(symbol, timeframe="1H", total=2000):
    calls_needed = math.ceil(total / 300)
    all_data = []
    
    for _ in range(calls_needed):
        params = {"instId": symbol, "bar": timeframe, "limit": 300}
        resp = requests.get(OKX_CANDLE_ENDPOINT, params=params)
        data = resp.json().get("data", [])
        
        if not data:
            break
        
        columns = ["ts", "o", "h", "l", "c"]
        df_chunk = pd.DataFrame(data, columns=columns)
        df_chunk.rename(columns={"ts": "timestamp", "o": "open", 
                                  "h": "high", "l": "low", 
                                  "c": "close"}, inplace=True)
        all_data.append(df_chunk)

        if len(data) < 300:
            break
    
    if not all_data:
        return pd.DataFrame()

    df_all = pd.concat(all_data)
    
   # Convert timestamps to datetime and calculate indicators
   ...