import yfinance as yf
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import pandas as pd
import gradio as gr
import os

def fetch_data(ticker, period):
    data = yf.download(ticker, period=period)
    return data

def compute_rsi(data, window=14):
    delta = data['Close'].diff()
    gain = (delta.where(delta > 0, 0)).rolling(window=window).mean()
    loss = (-delta.where(delta < 0, 0)).rolling(window=window).mean()

    rs = gain / loss
    rsi = 100 - (100 / (1 + rs))
    return rsi

def compute_macd(data, slow=26, fast=12, signal=9):
    exp1 = data['Close'].ewm(span=fast, adjust=False).mean()
    exp2 = data['Close'].ewm(span=slow, adjust=False).mean()
    macd = exp1 - exp2
    signal_line = macd.ewm(span=signal, adjust=False).mean()
    histogram = macd - signal_line
    return macd, signal_line, histogram

def buy_sell_signals(data, rsi, macd, signal_line):
    buy_signals = []
    sell_signals = []
    for i in range(2, len(data)):
        # RSI Buy Signal (RSI < 30)
        if rsi[i-1] < 30 and rsi[i] >= 30:
            buy_signals.append(data['Close'][i])
            sell_signals.append(float('nan'))
        # RSI Sell Signal (RSI > 70)
        elif rsi[i-1] > 70 and rsi[i] <= 70:
            sell_signals.append(data['Close'][i])
            buy_signals.append(float('nan'))
        # MACD Buy Signal (MACD crosses above Signal)
        elif macd[i-1] < signal_line[i-1] and macd[i] >= signal_line[i]:
            buy_signals.append(data['Close'][i])
            sell_signals.append(float('nan'))
        # MACD Sell Signal (MACD crosses below Signal)
        elif macd[i-1] > signal_line[i-1] and macd[i] <= signal_line[i]:
            sell_signals.append(data['Close'][i])
            buy_signals.append(float('nan'))
        else:
            buy_signals.append(float('nan'))
            sell_signals.append(float('nan'))
    return buy_signals, sell_signals


def plot_data(data, buy_signals, sell_signals, filename="plot.png"):
    fig = make_subplots(rows=2, cols=1)
    fig.add_trace(go.Candlestick(x=data.index, open=data['Open'], high=data['High'], low=data['Low'], close=data['Close'], name='Candlesticks'), row=1, col=1)
    fig.add_trace(go.Scatter(x=data.index, y=buy_signals, mode='markers', marker_symbol='triangle-up', marker_color='green', name='Buy Signal'), row=1, col=1)
    fig.add_trace(go.Scatter(x=data.index, y=sell_signals, mode='markers', marker_symbol='triangle-down', marker_color='red', name='Sell Signal'), row=1, col=1)
    fig.update_layout(title='Candlestick Chart with Buy and Sell Signals', xaxis_rangeslider_visible=False)
    fig.write_image(filename)
    return filename

def main_interface(ticker, period):
    data = fetch_data(ticker, period)
    rsi = compute_rsi(data)
    macd, signal_line, _ = compute_macd(data)
    buy_signals, sell_signals = buy_sell_signals(data, rsi, macd, signal_line)
    plot_filename = plot_data(data, buy_signals, sell_signals)
    return plot_filename

# Step 7: Set Up Gradio Interface with corrected type parameter
iface = gr.Interface(fn=main_interface,
                     inputs=[gr.Textbox(label="Asset Ticker"), gr.Textbox(label="Period: (e.g., 1y, 2y, 5y)")],
                     outputs=gr.Image(type="filepath"),  # Corrected type parameter
                     title="Stock Analysis Tool",
                     description="Enter a stock ticker and period to analyze buy/sell signals based on RSI and MACD.")


iface.launch()