import gradio as gr
import pandas as pd
import numpy as np
import yfinance as yf
import datetime
import matplotlib.pyplot as plt

# Function to fetch stock data
def get_stock_data(ticker, start_date, end_date):
    stock_data = yf.download(ticker, start=start_date, end=end_date)
    stock_data.reset_index(inplace=True)
    return stock_data

# Function to calculate Simple Moving Average (SMA)
def calculate_sma(stock_data, window=30):
    stock_data['SMA'] = stock_data['Close'].rolling(window=window).mean()
    return stock_data

# Function to predict future stock price using SMA
def predict_future_sma(stock_data, periods=30):
    last_sma = stock_data['SMA'].iloc[-1]
    future_dates = pd.date_range(start=stock_data['Date'].iloc[-1], periods=periods + 1, closed='right')
    future_sma = np.full(periods, last_sma)
    return future_dates, future_sma

# Function to calculate percentage change
def calculate_percentage_change(stock_data):
    start_value = stock_data['Close'].iloc[0]
    end_value = stock_data['Close'].iloc[-1]
    percentage_change = ((end_value - start_value) / start_value) * 100
    return percentage_change

# Function to get stock statistics (high/low)
def get_stock_stats(stock_data):
    high_value = stock_data['Close'].max()
    low_value = stock_data['Close'].min()
    return high_value, low_value

# Function to plot stock performance
def plot_stock_performance(stock_data, predicted_dates, predicted_sma):
    plt.figure(figsize=(10, 5))
    plt.plot(stock_data['Date'], stock_data['Close'], label="Historical Data", color="blue")
    plt.plot(stock_data['Date'], stock_data['SMA'], label="SMA (30 days)", color="green")
    plt.plot(predicted_dates, predicted_sma, label="Predicted SMA", color="orange", linestyle='--')
    plt.xlabel("Date")
    plt.ylabel("Stock Price")
    plt.title("Stock Performance (Historical vs Predicted)")
    plt.legend()
    plt.grid(True)
    plt.tight_layout()
    plt.savefig("stock_performance.png")
    return "stock_performance.png"

# Main function for UI
def stock_prediction(ticker, start_date, end_date):
    stock_data = get_stock_data(ticker, start_date, end_date)
    
    # Get statistics
    percentage_change = calculate_percentage_change(stock_data)
    high_value, low_value = get_stock_stats(stock_data)
    
    # Calculate Simple Moving Average
    stock_data = calculate_sma(stock_data)
    
    # Predict future stock performance using SMA
    predicted_dates, predicted_sma = predict_future_sma(stock_data)
    
    # Generate buy/sell suggestion (simple rule-based)
    suggestion = "Buy" if percentage_change < 0 else "Sell"
    
    # Plot stock performance
    plot_file = plot_stock_performance(stock_data, predicted_dates, predicted_sma)
    
    return plot_file, percentage_change, high_value, low_value, suggestion

# UI setup
with gr.Blocks() as app:
    gr.Markdown("# Stock Prediction App using Simple Moving Average (SMA)")
    
    stock_ticker = gr.Dropdown(
        label="Select Stock Ticker", 
        choices=["AAPL", "GOOGL", "MSFT", "AMZN", "TSLA", "NFLX", "FB", "NVDA", "IBM", "ORCL"]
    )
    
    start_date = gr.DatePicker(label="Start Date", value=datetime.date(2023, 1, 1))
    end_date = gr.DatePicker(label="End Date", value=datetime.date.today())
    
    plot_output = gr.Image(label="Stock Performance (Historical vs Predicted)")
    percentage_change_output = gr.Textbox(label="Percentage Change")
    high_value_output = gr.Textbox(label="Highest Stock Value")
    low_value_output = gr.Textbox(label="Lowest Stock Value")
    suggestion_output = gr.Textbox(label="Buy/Sell Suggestion")
    
    # Button to make predictions
    predict_button = gr.Button("Predict")
    
    predict_button.click(
        stock_prediction, 
        inputs=[stock_ticker, start_date, end_date], 
        outputs=[plot_output, percentage_change_output, high_value_output, low_value_output, suggestion_output]
    )

app.launch()