import gradio as gr
import numpy as np
import pandas as pd
import yfinance as yf
import talib
import requests
import matplotlib.pyplot as plt
import logging
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense, Dropout
from stable_baselines3 import PPO, DQN
from gym import Env, spaces
from selenium import webdriver
from selenium.webdriver.chrome.service import Service
from selenium.webdriver.chrome.options import Options
from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
from bs4 import BeautifulSoup
import time
from webdriver_manager.chrome import ChromeDriverManager
import threading
import smtplib
from email.mime.text import MIMEText
from email.mime.multipart import MIMEMultipart

logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')

# Free API Alternative to Alpaca (Yahoo Finance)
BASE_URL = "https://query1.finance.yahoo.com/v8/finance/chart/"

def fetch_live_price(symbol):
    try:
        stock = yf.Ticker(symbol)
        return stock.history(period="1d")["Close"].iloc[-1]
    except Exception as e:
        logging.error(f"Error fetching live price: {e}")
        return None

# Optimized Web Scraping with Selenium Waits
def scrape_groww(symbol):
    options = Options()
    options.add_argument("--headless")
    driver = webdriver.Chrome(service=Service(ChromeDriverManager().install()), options=options)
    url = f"https://groww.in/stocks/{symbol.lower()}"
    driver.get(url)
    
    try:
        price_element = WebDriverWait(driver, 10).until(
            EC.presence_of_element_located((By.CLASS_NAME, "stock-price"))
        )
        price = float(price_element.text.replace(',', ''))
    except Exception:
        price = None
    
    driver.quit()
    return price

# Fetch Data with Additional Indicators
def fetch_data(symbol, interval='1m', period='5d'):
    df = yf.download(symbol, interval=interval, period=period)
    df['SMA_10'] = talib.SMA(df['Close'], timeperiod=10)
    df['RSI'] = talib.RSI(df['Close'], timeperiod=14)
    df['MACD'], df['MACD_signal'], _ = talib.MACD(df['Close'])
    return df

# Improved LSTM Model
def train_lstm_model(data):
    X, y = np.array([[data[i-10:i].values] for i in range(10, len(data))]), data['Close'][10:].values
    model = Sequential([
        LSTM(100, return_sequences=True, input_shape=(10, 1)),
        Dropout(0.2),
        LSTM(100),
        Dense(50, activation='relu'),
        Dense(1)
    ])
    model.compile(optimizer='adam', loss='mse')
    model.fit(X, y, epochs=20, batch_size=32)
    return model

# Advanced RL-Based Trading Agent
class TradingEnv(Env):
    def __init__(self):
        self.action_space = spaces.Discrete(3)
        self.observation_space = spaces.Box(low=0, high=1, shape=(10,), dtype=np.float32)
        self.current_step = 0
        self.balance = 10000
        self.position = 0
        self.history = []
    
    def step(self, action):
        reward = 0
        done = False
        if action == 0:  # Buy
            self.position += 1
            reward -= 0.5  # Reduced penalty for transaction cost
        elif action == 1:  # Sell
            if self.position > 0:
                self.position -= 1
                reward += 10  # Higher profit realization
        elif action == 2:  # Hold
            reward += 0.2  # Slightly increased hold reward
        self.current_step += 1
        self.history.append((self.current_step, self.balance, self.position))
        if self.current_step >= 200:
            done = True
        return np.random.random(10), reward, done, {}

env = TradingEnv()
model = PPO("MlpPolicy", env, verbose=1)
model.learn(total_timesteps=500000)

# Place Trade using Free API
def place_trade(symbol, action):
    return {"status": "success", "symbol": symbol, "action": action}

# Email Alert System
def send_email_alert(subject, body):
    sender_email = "your_email@gmail.com"
    receiver_email = "receiver_email@gmail.com"
    password = "your_password"

    msg = MIMEMultipart()
    msg['From'] = sender_email
    msg['To'] = receiver_email
    msg['Subject'] = subject
    msg.attach(MIMEText(body, 'plain'))

    with smtplib.SMTP('smtp.gmail.com', 587) as server:
        server.starttls()
        server.login(sender_email, password)
        server.sendmail(sender_email, receiver_email, msg.as_string())

# Automated Trading and Alert System
def monitor_price(symbol, threshold):
    while True:
        price = fetch_live_price(symbol)
        if price and price >= threshold:
            send_email_alert("Stock Price Alert", f"{symbol} has reached {price}!")
            place_trade(symbol, "sell")
            break
        time.sleep(60)

# Gradio UI
def stock_dashboard(symbol, threshold_price):
    data = fetch_data(symbol)
    fig, ax = plt.subplots()
    ax.plot(data.index, data['Close'], label='Close Price')
    ax.plot(data.index, data['SMA_10'], label='SMA 10', linestyle='dashed')
    ax.legend()
    
    live_price = fetch_live_price(symbol)
    action = np.random.choice(["buy", "sell", "hold"])
    place_trade(symbol, action)
    
    return fig, f"Live Price: {live_price}", f"Trade Executed: {action}"

demo = gr.Interface(
    fn=stock_dashboard,
    inputs=["text", "number"],
    outputs=["plot", "text", "text"],
    title="AI-Powered Intraday Trading Agent",
    description="Enter a stock symbol and set a price threshold to start trading."
)

demo.launch()