# %%
# Install necessary packages if not already installed
# pip install gradio yfinance prophet plotly matplotlib

import gradio as gr
import pandas as pd
import yfinance as yf
from datetime import datetime
import plotly.express as px
import plotly.graph_objects as go
import matplotlib.pyplot as plt
import numpy as np

# Functions for calculating indicators (SMA, RSI, etc.) and generating trading signals
# (Reuse the code you've already written for technical indicators and forecasting)

def calculate_sma(df, window):
    return df['Close'].rolling(window=window).mean()

def calculate_macd(df):
    short_ema = df['Close'].ewm(span=12, adjust=False).mean()
    long_ema = df['Close'].ewm(span=26, adjust=False).mean()
    macd = short_ema - long_ema
    signal = macd.ewm(span=9, adjust=False).mean()
    return macd, signal

def calculate_rsi(df):
    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
    rsi = 100 - (100 / (1 + rs))
    return rsi

def calculate_bollinger_bands(df):
    middle_bb = df['Close'].rolling(window=20).mean()
    upper_bb = middle_bb + 2 * df['Close'].rolling(window=20).std()
    lower_bb = middle_bb - 2 * df['Close'].rolling(window=20).std()
    return middle_bb, upper_bb, lower_bb

def calculate_stochastic_oscillator(df):
    lowest_low = df['Low'].rolling(window=14).min()
    highest_high = df['High'].rolling(window=14).max()
    slowk = ((df['Close'] - lowest_low) / (highest_high - lowest_low)) * 100
    slowd = slowk.rolling(window=3).mean()
    return slowk, slowd

def generate_trading_signals(df):

    # Calculate Simple Moving Averages (SMA)
    df['SMA_50'] = calculate_sma(df, 50)
    df['SMA_200'] = calculate_sma(df, 200)

    # Calculate other technical indicators
    df['RSI'] = calculate_rsi(df)
    df['MiddleBB'], df['UpperBB'], df['LowerBB'] = calculate_bollinger_bands(df)
    df['SlowK'], df['SlowD'] = calculate_stochastic_oscillator(df)

    # Generate trading signals
    df['SMA_Signal'] = np.where(df['SMA_50'] > df['SMA_200'], 1, 0)
    
    macd, signal = calculate_macd(df)
    df['MACD_Signal'] = np.where((macd > signal.shift(1)) & (macd.shift(1) < signal), 1, 0)
    
    df['RSI_Signal'] = np.where(df['RSI'] < 30, 1, 0)
    df['RSI_Signal'] = np.where(df['RSI'] > 70, -1, df['RSI_Signal'])
    
    df['BB_Signal'] = np.where(df['Close'] < df['LowerBB'], 1, 0)
    df['BB_Signal'] = np.where(df['Close'] > df['UpperBB'], -1, df['BB_Signal'])
    
    df['Stochastic_Signal'] = np.where((df['SlowK'] < 20) & (df['SlowD'] < 20), 1, 0)
    df['Stochastic_Signal'] = np.where((df['SlowK'] > 80) & (df['SlowD'] > 80), -1, df['Stochastic_Signal'])

    # Summing the values of each individual signal column
    df['Combined_Signal'] = df[['SMA_Signal', 'MACD_Signal', 'RSI_Signal', 'BB_Signal', 'Stochastic_Signal']].sum(axis=1)






# %%
import plotly.graph_objects as go

def plot_combined_signals(df, ticker):
    # Create a figure
    fig = go.Figure()

    # Add closing price trace
    fig.add_trace(go.Scatter(
        x=df.index, y=df['Close'], 
        mode='lines', 
        name='Closing Price', 
        line=dict(color='lightcoral', width=2)
    ))

    # Add buy signals
    buy_signals = df[df['Combined_Signal'] >= 2]
    fig.add_trace(go.Scatter(
        x=buy_signals.index, y=buy_signals['Close'], 
        mode='markers', 
        marker=dict(symbol='triangle-up', size=10, color='lightgreen'), 
        name='Buy Signal'
    ))

    # Add sell signals
    sell_signals = df[df['Combined_Signal'] <= -2]
    fig.add_trace(go.Scatter(
        x=sell_signals.index, y=sell_signals['Close'], 
        mode='markers', 
        marker=dict(symbol='triangle-down', size=10, color='lightsalmon'), 
        name='Sell Signal'
    ))

    # Add combined signal trace
    fig.add_trace(go.Scatter(
        x=df.index, y=df['Combined_Signal'], 
        mode='lines', 
        name='Combined Signal', 
        line=dict(color='deepskyblue', width=2), 
        yaxis='y2'
    ))

    # Update layout for secondary y-axis
    fig.update_layout(
        title=f'{ticker}: Stock Price and Combined Trading Signal (Last 60 Days)',
        xaxis=dict(title='Date', gridcolor='gray', gridwidth=0.5),
        yaxis=dict(title='Price', side='left', gridcolor='gray', gridwidth=0.5),
        yaxis2=dict(title='Combined Signal', overlaying='y', side='right', showgrid=False),
        plot_bgcolor='black',
        paper_bgcolor='black',
        font=dict(color='white'),
        legend=dict(x=0.01, y=0.99, bgcolor='rgba(0,0,0,0)'),
        hovermode='x unified'
    )

    return fig


# %%
def stock_analysis(ticker, start_date, end_date):
    # Download stock data from Yahoo Finance
    df = yf.download(ticker, start=start_date, end=end_date)
    
    # Run your existing trading signals and indicators here
    generate_trading_signals(df)
    
    # Last 60 days
    df_last_60 = df.tail(60)
    
    # Plot trading signals using the improved function
    fig_signals = plot_combined_signals(df_last_60, ticker)
    
    
    # Combine the figures into HTML output
    return fig_signals


# %%
# Define Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("## Stock Market Analysis App")

    ticker_input = gr.Textbox(label="Enter Stock Ticker (e.g., AAPL, NVDA)", value="NVDA")
    start_date_input = gr.Textbox(label="Start Date (YYYY-MM-DD)", value="2022-01-01")
    end_date_input = gr.Textbox(label="End Date (YYYY-MM-DD)", value=str(datetime.now().date()))

    # Create a submit button that runs the stock analysis function
    button = gr.Button("Analyze Stock")
    
    # Outputs: Display results, charts
    signals_output = gr.Plot(label="Trading Signals")

    # Link button to function
    button.click(stock_analysis, inputs=[ticker_input, start_date_input, end_date_input], outputs=[signals_output])

# Launch the interface
demo.launch()