import gradio as gr
import pandas as pd
import yfinance as yf
from datetime import datetime, timedelta
import requests
from bs4 import BeautifulSoup
from concurrent.futures import ThreadPoolExecutor, as_completed
from pattern_finder import score_downward_trend, score_candle, calculate_risk_reward
import urllib3
urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)


def load_sp500_tickers():
    """Load S&P 500 tickers from Wikipedia."""
    url = "https://en.wikipedia.org/wiki/List_of_S%26P_500_companies"
    response = requests.get(url, verify=False)
    soup = BeautifulSoup(response.content, 'html.parser')
    table = soup.find('table', {'id': 'constituents'})
    tickers = []
    if table:
        for row in table.find_all('tr')[1:]:
            cells = row.find_all('td')
            if cells:
                ticker = cells[0].text.strip()
                tickers.append(ticker)
    return tickers


def load_data(ticker):
    """Load stock data using yfinance."""
    end_date = datetime.today()
    start_date = end_date - timedelta(days=365)  # Get 1 year of data
    data = yf.download(ticker, start=start_date, end=end_date)
    return data


def calculate_sma(data, window):
    """Calculate the Simple Moving Average (SMA) for a given window."""
    return data['Close'].rolling(window=window).mean()


def calculate_ema(data, window):
    """Calculate the Exponential Moving Average (EMA) for a given window."""
    return data['Close'].ewm(span=window, adjust=False).mean()


def average_downtrend(data, method, window=4):
    """Calculate the average difference between consecutive prices for the last 'window' candles."""
    if len(data) < window:
        return 0.0
    price_diffs = data[method].diff().iloc[-window:]
    avg_diff = price_diffs.mean()
    return avg_diff if avg_diff < 0 else 0.0


def score_candle(candle, prev_candle, trend_strength):
    """Score a single candle based on its characteristics and previous candle."""
    # [Keep the existing scoring logic]
    # ...
    return score


def score_today_candle(data, window=4):
    """Score today's candle based on the downtrend from the past 'window' days."""
    if len(data) < window + 1:
        return 0  # Not enough data

    today_candle = data.iloc[-1]
    prev_candle = data.iloc[-2]
    
    close_price = today_candle['Close']
    previous_data = data.iloc[-(window+1):-1]
    down_High = average_downtrend(previous_data, method="High", window=window) + average_downtrend(previous_data, method="High", window=7) / 2
    down_Close = average_downtrend(previous_data, method="Close", window=window) + average_downtrend(previous_data, method="Close", window=7) / 2
    avg_downtrend = (down_High + down_Close) / 2
  
    if avg_downtrend == 0.0:
        return -1

    sma_50 = calculate_sma(data, window=50).iloc[-1]
    sma_200 = calculate_sma(data, window=200).iloc[-1]
    sma_20 = calculate_sma(data, window=20).iloc[-1]
    ema_10 = calculate_ema(data, window=10).iloc[-1]
    
    if (close_price < ema_10) or (close_price < sma_20) or  (close_price < sma_50) or (close_price < sma_200):
        return -1

    return score_candle(today_candle, prev_candle, abs(avg_downtrend))


def scan_ticker(ticker):
    """Load data for a ticker and calculate its score."""
    data = load_data(ticker)
    if not data.empty:
        score = score_today_candle(data)
        if score > 0:
            return ticker, score
    return None


def scan_sp500(top_n=25, progress=gr.Progress()):
    tickers = load_sp500_tickers()
    tickers.append("QQQ")

    scores = []
    with ThreadPoolExecutor(max_workers=10) as executor:
        futures = {executor.submit(scan_ticker, ticker): ticker for ticker in tickers}
        for i, future in enumerate(as_completed(futures), 1):
            result = future.result()
            if result:
                scores.append(result)
            progress(i / len(tickers), desc="Processing Tickers")
    
    scores = sorted(scores, key=lambda x: x[1], reverse=True)
    return scores[:top_n]


def gradio_scan_sp500(top_n, progress=gr.Progress()):
    progress(0, desc="Downloading Data")
    tickers = load_sp500_tickers()
    tickers.append("QQQ")
    
    progress(0.3, desc="Running Scanner")
    results = scan_sp500(top_n, progress)
    
    last_data = load_data(results[0][0])
    last_date = last_data.index[-1].date()
    next_market_day = next_business_day(last_date)
    date_created = next_market_day.strftime("%Y-%m-%d")
    
    output = f"Scan Results for Market Open on: {date_created}\n\n"
    output += "Top {} stocks based on pattern finder score:\n\n".format(top_n)
    for ticker, score in results:
        output += "{}: Total Score = {:.2f}\n".format(ticker, score)
    return output

iface = gr.Interface(
    fn=gradio_scan_sp500,
    inputs=gr.Slider(minimum=1, maximum=100, step=1, label="Number of top stocks to display", value=25),
    outputs="text",
    title="S&P 500 Stock Scanner",
    description="Scan S&P 500 stocks and display top N stocks based on today's candle score.",
    allow_flagging="never",
)

if __name__ == "__main__":
    iface.launch(server_name="0.0.0.0", server_port=7860)