import streamlit as st
import yfinance as yf
import pandas as pd
from prophet import Prophet
import plotly.graph_objs as go
import google.generativeai as genai
import numpy as np

# Streamlit app details
st.set_page_config(page_title="TechyTrade", layout="wide")

# Custom CSS
st.markdown("""
    <style>
        @import url('https://fonts.googleapis.com/css2?family=Montserrat:wght@300;400;700&display=swap');

        body {
            background-color: #f4f4f9;
            color: #333;
            font-family: 'Montserrat', sans-serif;
        }
        .sidebar .sidebar-content {
            background-color: #2c3e50;
            color: white;
        }
        h1, h2, h3 {
            color: #2980b9;
        }
        .css-1v3fvcr {
            color: #2980b9 !important;
        }
        .css-17eq0hr {
            font-family: 'Montserrat', sans-serif !important;
        }
        .css-2trqyj {
            font-family: 'Montserrat', sans-serif !important;
        }
    </style>
""", unsafe_allow_html=True)

# Sidebar
with st.sidebar:
    st.title("📊 TechyTrade")
    ticker = st.text_input("Enter a stock ticker (e.g. AAPL) 🏷️", "AAPL")
    period = st.selectbox("Enter a time frame ⏳", ("1D", "5D", "1M", "6M", "YTD", "1Y", "5Y"), index=2)
    forecast_period = st.slider("Select forecast period (days) 🔮", min_value=1, max_value=365, value=30)
    st.write("Select Technical Indicators:")
    sma_checkbox = st.checkbox("Simple Moving Average (SMA)")
    ema_checkbox = st.checkbox("Exponential Moving Average (EMA)")
    rsi_checkbox = st.checkbox("Relative Strength Index (RSI)")
    macd_checkbox = st.checkbox("Moving Average Convergence Divergence (MACD)")
    bollinger_checkbox = st.checkbox("Bollinger Bands")
    google_api_key = st.text_input("Enter your Google API Key 🔑", type="password")
    button = st.button("Submit 🚀")

# Load generative model
@st.cache_resource
def load_model(api_key):
    genai.configure(api_key=api_key)
    return genai.GenerativeModel('gemini-1.5-flash')

# Function to generate reasons using the generative model
def generate_reasons(fig, stock_info, price_info, biz_metrics, api_key):
    model = load_model(api_key)
    prompt = f"Based on the following stock price graph description:\n\n{fig}\n\n and the tables:\n\n{stock_info}\n\n and\n\n{price_info}\n\n and\n\n{biz_metrics}\n\n and analyze the trends and give recommendations and insights."
    response = model.generate_content(prompt)
    return response.text

# Function to format large numbers
def format_value(value):
    suffixes = ["", "K", "M", "B", "T"]
    suffix_index = 0
    while value >= 1000 and suffix_index < len(suffixes) - 1:
        value /= 1000
        suffix_index += 1
    return f"${value:.1f}{suffixes[suffix_index]}"

# Technical Indicators Functions
def calculate_sma(data, window):
    return data.rolling(window=window).mean()

def calculate_ema(data, window):
    return data.ewm(span=window, adjust=False).mean()

def calculate_rsi(data, window):
    delta = data.diff()
    gain = (delta.where(delta > 0, 0)).rolling(window=window).mean()
    loss = (-delta.where(delta < 0, 0)).rolling(window=window).mean()
    rs = gain / loss
    return 100 - (100 / (1 + rs))

def calculate_macd(data, short_window=12, long_window=26, signal_window=9):
    short_ema = calculate_ema(data, short_window)
    long_ema = calculate_ema(data, long_window)
    macd = short_ema - long_ema
    signal = calculate_ema(macd, signal_window)
    return macd, signal

def calculate_bollinger_bands(data, window):
    sma = calculate_sma(data, window)
    std = data.rolling(window=window).std()
    upper_band = sma + (std * 2)
    lower_band = sma - (std * 2)
    return upper_band, lower_band

# If Submit button is clicked
if button:
    if not ticker.strip():
        st.error("Please provide a valid stock ticker.")
    elif not google_api_key.strip():
        st.error("Please provide a valid Google API Key.")
    else:
        try:
            with st.spinner('Please wait...'):
                # Retrieve stock data
                stock = yf.Ticker(ticker)
                info = stock.info

                st.subheader(f"{ticker} - {info.get('longName', 'N/A')}")

                # Plot historical stock price data
                if period == "1D":
                    history = stock.history(period="1d", interval="1h")
                elif period == "5D":
                    history = stock.history(period="5d", interval="1d")
                elif period == "1M":
                    history = stock.history(period="1mo", interval="1d")
                elif period == "6M":
                    history = stock.history(period="6mo", interval="1wk")
                elif period == "YTD":
                    history = stock.history(period="ytd", interval="1mo")
                elif period == "1Y":
                    history = stock.history(period="1y", interval="1mo")
                elif period == "5Y":
                    history = stock.history(period="5y", interval="3mo")

                # Create a plotly figure
                fig = go.Figure()
                fig.add_trace(go.Scatter(x=history.index, y=history['Close'], mode='lines', name='Close Price'))

                # Add Technical Indicators
                if sma_checkbox:
                    sma = calculate_sma(history['Close'], window=20)
                    fig.add_trace(go.Scatter(x=history.index, y=sma, mode='lines', name='SMA (20)'))
                
                if ema_checkbox:
                    ema = calculate_ema(history['Close'], window=20)
                    fig.add_trace(go.Scatter(x=history.index, y=ema, mode='lines', name='EMA (20)'))

                if rsi_checkbox:
                    rsi = calculate_rsi(history['Close'], window=14)
                    fig.add_trace(go.Scatter(x=history.index, y=rsi, mode='lines', name='RSI (14)', yaxis='y2'))
                    fig.update_layout(yaxis2=dict(title='RSI', overlaying='y', side='right'))

                if macd_checkbox:
                    macd, signal = calculate_macd(history['Close'])
                    fig.add_trace(go.Scatter(x=history.index, y=macd, mode='lines', name='MACD'))
                    fig.add_trace(go.Scatter(x=history.index, y=signal, mode='lines', name='Signal Line'))

                if bollinger_checkbox:
                    upper_band, lower_band = calculate_bollinger_bands(history['Close'], window=20)
                    fig.add_trace(go.Scatter(x=history.index, y=upper_band, mode='lines', name='Upper Band'))
                    fig.add_trace(go.Scatter(x=history.index, y=lower_band, mode='lines', name='Lower Band'))

                fig.update_layout(
                    title=f"Historical Stock Prices for {ticker}",
                    xaxis_title="Date",
                    yaxis_title="Close Price",
                    hovermode="x unified"
                )
                st.plotly_chart(fig, use_container_width=True)

                col1, col2, col3 = st.columns(3)

                # Display stock information as a dataframe
                country = info.get('country', 'N/A')
                sector = info.get('sector', 'N/A')
                industry = info.get('industry', 'N/A')
                market_cap = info.get('marketCap', 'N/A')
                ent_value = info.get('enterpriseValue', 'N/A')
                employees = info.get('fullTimeEmployees', 'N/A')

                stock_info = [
                    ("Stock Info", "Value"),
                    ("Country ", country),
                    ("Sector ", sector),
                    ("Industry ", industry),
                    ("Market Cap ", format_value(market_cap)),
                    ("Enterprise Value ", format_value(ent_value)),
                    ("Employees ", employees)
                ]
                
                df = pd.DataFrame(stock_info[1:], columns=stock_info[0])
                col1.dataframe(df, width=400, hide_index=True)
                
                # Display price information as a dataframe
                current_price = info.get('currentPrice', 'N/A')
                prev_close = info.get('previousClose', 'N/A')
                day_high = info.get('dayHigh', 'N/A')
                day_low = info.get('dayLow', 'N/A')
                ft_week_high = info.get('fiftyTwoWeekHigh', 'N/A')
                ft_week_low = info.get('fiftyTwoWeekLow', 'N/A')
                
                price_info = [
                    ("Price Info", "Value"),
                    ("Current Price ", f"${current_price:.2f}"),
                    ("Previous Close ", f"${prev_close:.2f}"),
                    ("Day High ", f"${day_high:.2f}"),
                    ("Day Low ", f"${day_low:.2f}"),
                    ("52 Week High ", f"${ft_week_high:.2f}"),
                    ("52 Week Low ", f"${ft_week_low:.2f}")
                ]
                
                df = pd.DataFrame(price_info[1:], columns=price_info[0])
                col2.dataframe(df, width=400, hide_index=True)

                # Display business metrics as a dataframe
                forward_eps = info.get('forwardEps', 'N/A')
                forward_pe = info.get('forwardPE', 'N/A')
                peg_ratio = info.get('pegRatio', 'N/A')
                dividend_rate = info.get('dividendRate', 'N/A')
                dividend_yield = info.get('dividendYield', 'N/A')
                recommendation = info.get('recommendationKey', 'N/A')
                
                biz_metrics = [
                    ("Business Metrics", "Value"),
                    ("EPS (FWD) ", f"{forward_eps:.2f}"),
                    ("P/E (FWD) ", f"{forward_pe:.2f}"),
                    ("PEG Ratio ", f"{peg_ratio:.2f}"),
                    ("Div Rate (FWD) ", f"${dividend_rate:.2f}"),
                    ("Div Yield (FWD) ", f"{dividend_yield * 100:.2f}%"),
                    ("Recommendation ", recommendation.capitalize())
                ]
                
                df = pd.DataFrame(biz_metrics[1:], columns=biz_metrics[0])
                col3.dataframe(df, width=400, hide_index=True)

                # Forecasting
                st.subheader("Stock Price Forecast 🔮")
                df_forecast = history.reset_index()[['Date', 'Close']]
                df_forecast['Date'] = pd.to_datetime(df_forecast['Date']).dt.tz_localize(None)  # Remove timezone information
                df_forecast.columns = ['ds', 'y']

                m = Prophet(daily_seasonality=True)
                m.fit(df_forecast)

                future = m.make_future_dataframe(periods=forecast_period)
                forecast = m.predict(future)

                fig2 = go.Figure()
                fig2.add_trace(go.Scatter(x=forecast['ds'], y=forecast['yhat'], mode='lines', name='Forecast'))
                fig2.add_trace(go.Scatter(x=forecast['ds'], y=forecast['yhat_upper'], mode='lines', name='Upper Confidence Interval', line=dict(dash='dash')))
                fig2.add_trace(go.Scatter(x=forecast['ds'], y=forecast['yhat_lower'], mode='lines', name='Lower Confidence Interval', line=dict(dash='dash')))
                fig2.update_layout(
                    title=f"Stock Price Forecast for {ticker}",
                    xaxis_title="Date",
                    yaxis_title="Predicted Close Price",
                    hovermode="x unified"
                )
                st.plotly_chart(fig2, use_container_width=True)

                # Generate reasons based on forecast
                graph_description = f"The stock price forecast graph for {ticker} shows the predicted close prices along with the upper and lower confidence intervals for the next {forecast_period} days."
                reasons = generate_reasons(fig, stock_info, price_info, biz_metrics, google_api_key)

                st.subheader("Investment Analysis")
                st.write(reasons)

        except Exception as e:
            st.exception(f"An error occurred: {e}")