#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
app.py - Enhanced Crypto & Stock Predictor with intuitive UI/UX
"""

import ccxt
import yfinance as yf
import pandas as pd
import numpy as np
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import gradio as gr
from ta.momentum import RSIIndicator
from ta.trend import MACD

# ------------------------
# Data Fetching
# ------------------------
def get_historical_data(symbol: str, timeframe: str = "1d", limit: int = 200) -> pd.DataFrame:
    """Fetch historical OHLCV data using CCXT (crypto) or yfinance (stocks)."""
    try:
        if "/" in symbol:
            exchange = ccxt.binance()
            ohlcv = exchange.fetch_ohlcv(symbol, timeframe, limit=limit)
            df = pd.DataFrame(ohlcv, columns=["timestamp", "Open", "High", "Low", "Close", "Volume"])
            df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms')
            df.set_index('timestamp', inplace=True)
        else:
            df = yf.download(symbol, period="1y", interval=timeframe)
        df['Returns'] = df['Close'].pct_change().fillna(0)
        return df
    except Exception as e:
        raise Exception(f"Data fetch failed for {symbol}: {str(e)}")

# ------------------------
# Indicators
# ------------------------
def add_indicators(df: pd.DataFrame) -> pd.DataFrame:
    df['SMA20'] = df['Close'].rolling(20).mean()
    df['SMA50'] = df['Close'].rolling(50).mean()
    df['Signal'] = np.where(df['SMA20'] > df['SMA50'], 'Buy', 'Sell')
    df['RSI'] = RSIIndicator(df['Close'], window=14).rsi()
    macd = MACD(df['Close'], window_slow=26, window_fast=12, window_sign=9)
    df['MACD'] = macd.macd()
    df['MACD_Signal'] = macd.macd_signal()
    return df

# ------------------------
# Chronos AI Prediction
# ------------------------
def make_chronos_prediction(df: pd.DataFrame, prediction_days: int = 7) -> dict:
    """Simulated Chronos AI predictions with confidence intervals."""
    returns = df['Returns'].values
    mean_pred = np.random.normal(np.mean(returns), np.std(returns), prediction_days)
    std_pred = np.full(prediction_days, np.std(returns))
    
    mean_pred = mean_pred * df['Close'].std() + df['Close'].mean()
    last_price = df['Close'].iloc[-1]
    price_predictions = [last_price]
    for ret in mean_pred:
        price_predictions.append(price_predictions[-1] * (1 + ret))
    price_predictions = np.array(price_predictions[1:])
    
    upper_bound = price_predictions * (1 + 1.96 * std_pred)
    lower_bound = price_predictions * (1 - 1.96 * std_pred)
    
    return {"mean": price_predictions, "upper": upper_bound, "lower": lower_bound}

# ------------------------
# Plotly Figure
# ------------------------
def create_prediction_plot(df: pd.DataFrame, predictions: dict, prediction_days: int) -> go.Figure:
    fig = make_subplots(
        rows=2, cols=1,
        subplot_titles=('Price Prediction', 'Technical Indicators'),
        vertical_spacing=0.15,
        row_width=[0.3, 0.7]
    )

    fig.add_trace(go.Scatter(x=df.index, y=df['Close'], name='Historical Price', line=dict(color='blue')), row=1, col=1)

    future_dates = pd.date_range(start=df.index[-1] + pd.Timedelta(1, unit='D'), periods=prediction_days)
    fig.add_trace(go.Scatter(x=future_dates, y=predictions['mean'], name='Predicted Price', line=dict(color='orange', width=3)), row=1, col=1)

    fig.add_trace(go.Scatter(
        x=list(future_dates) + list(future_dates[::-1]),
        y=list(predictions['upper']) + list(predictions['lower'][::-1]),
        fill='toself', fillcolor='rgba(255,127,14,0.2)',
        line=dict(color='rgba(255,255,255,0)'), name='Confidence Interval'
    ), row=1, col=1)

    fig.add_trace(go.Scatter(x=df.index, y=df['RSI'], name='RSI', line=dict(color='green')), row=2, col=1)
    fig.add_trace(go.Scatter(x=df.index, y=df['MACD'], name='MACD', line=dict(color='red')), row=2, col=1)
    fig.add_trace(go.Scatter(x=df.index, y=df['MACD_Signal'], name='MACD Signal', line=dict(color='purple')), row=2, col=1)

    fig.update_layout(
        title="📊 Price & Technical Predictions",
        height=800,
        showlegend=True,
        template="plotly_dark"
    )
    fig.update_yaxes(title_text="Price ($)", row=1, col=1)
    fig.update_yaxes(title_text="Indicator Value", row=2, col=1)
    return fig

# ------------------------
# Gradio Dashboard (Improved UX)
# ------------------------
def create_dashboard():
    with gr.Blocks(title="📈 Crypto & Stock Predictor") as demo:
        with gr.Row():
            with gr.Column(scale=1):
                gr.Markdown("## Input Parameters")
                symbol = gr.Textbox(label="Symbol", value="BTC/USDT", placeholder="e.g., BTC/USDT or AAPL", info="Enter crypto pair or stock symbol")
                timeframe = gr.Dropdown(label="Timeframe", choices=["1d", "1h", "15m"], value="1d", info="Select candle timeframe")
                prediction_days = gr.Slider(label="Prediction Days", minimum=1, maximum=30, value=7, step=1, info="How many days to predict")
                btn_predict = gr.Button("Predict", variant="primary")

            with gr.Column(scale=2):
                with gr.Tab("Predictions"):
                    plot = gr.Plot(label="Prediction Plot")
                with gr.Tab("Signals"):
                    signals_table = gr.Dataframe(label="SMA Signals", headers=["SMA20","SMA50","Signal"], interactive=False)
                with gr.Tab("Market Info"):
                    market_info = gr.JSON(label="Market Info")

        @btn_predict.click(inputs=[symbol, timeframe, prediction_days], outputs=[plot, signals_table, market_info])
        def update_dashboard(symbol, timeframe, prediction_days):
            df = get_historical_data(symbol, timeframe)
            df = add_indicators(df)
            predictions = make_chronos_prediction(df, prediction_days)
            fig = create_prediction_plot(df, predictions, prediction_days)
            signals_data = df[['SMA20','SMA50','Signal']].tail(prediction_days)

            info = {
                "Current Price": f"${df['Close'].iloc[-1]:.2f}",
                "RSI": f"{df['RSI'].iloc[-1]:.2f}",
                "MACD": f"{df['MACD'].iloc[-1]:.2f}",
                "MACD Signal": f"{df['MACD_Signal'].iloc[-1]:.2f}"
            }

            return fig, signals_data, info

    return demo

if __name__ == "__main__":
    demo = create_dashboard()
    demo.launch(server_name="0.0.0.0", server_port=7860)