import gradio as gr
import torch
from chronos import ChronosPipeline
import yfinance as yf
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from sklearn.metrics import mean_absolute_error, mean_squared_error
import tempfile

def get_popular_tickers():
    return [
        "AAPL", "MSFT", "GOOGL", "AMZN", "META", "TSLA", "NVDA", "JPM",
        "JNJ", "V", "PG", "WMT", "BAC", "DIS", "NFLX", "INTC"
    ]

# Resto del código se mantiene igual hasta la sección de la interfaz Gradio

with gr.Blocks() as demo:
    gr.Markdown("# Aplicación de Predicción de Precios de Acciones")
    
    with gr.Row():
        with gr.Column(scale=1):
            ticker = gr.Dropdown(
                choices=get_popular_tickers(),
                value="AAPL",  # Añadido valor por defecto
                label="Selecciona el Símbolo de la Acción"
            )
            train_data_points = gr.Slider(
                minimum=50,
                maximum=5000,
                value=1000,
                step=1,
                label="Número de Datos para Entrenamiento"
            )
            prediction_days = gr.Slider(
                minimum=1,
                maximum=60,
                value=5,
                step=1,
                label="Número de Días a Predecir"
            )
            predict_btn = gr.Button("Predecir")
        
    with gr.Column():
        plot_output = gr.Plot(label="Gráfico de Predicción")
        download_btn = gr.File(label="Descargar Predicciones")
    
    def update_train_data_points(ticker):
        try:
            stock = yf.Ticker(ticker)
            hist = stock.history(period="max")
            total_points = len(hist)
            return gr.Slider.update(
                maximum=total_points,
                value=min(1000, total_points),
                visible=True
            )
        except Exception as e:
            print(f"Error updating slider: {str(e)}")
            return gr.Slider.update(visible=True)  # Mantener slider visible en caso de error
    
    ticker.change(
        fn=update_train_data_points,
        inputs=[ticker],
        outputs=[train_data_points]
    )
    
    predict_btn.click(
        fn=predict_stock,
        inputs=[ticker, train_data_points, prediction_days],
        outputs=[plot_output, download_btn]
    )

demo.launch()