import gradio as gr
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from gradio.components import Slider, Number, Image, Dataframe, Textbox, Checkbox, Radio, Label, Plot
import plotly.graph_objects as go
from aaaaa import get_datXYZ, plot_3d_points, genS
from scipy.stats import multivariate_normal
import PIL.Image

data_path = "reference_data/"
image_path = "images_configs/"

# To generate distorsed covariance matrixes
def genS(sds, R, distor=1):
    # sds: standard deviations
    # R: correlation matrix
    # distor: level of distorsion
    sdsdis = sds * distor
    S = R * np.outer(sdsdis, sdsdis)
    np.fill_diagonal(S, sdsdis ** 2)
    return S

def show_hand_3d(gesto, attempt, distortion, mean, std):
    datXYZ = get_datXYZ(gesto, data_path)
    print(datXYZ.shape)
    # print(datXYZ)
    obsmean = np.mean(datXYZ, axis=0)
    print(obsmean.shape)
    # change to 63 shape
    obsmean = obsmean.reshape(63)
    obssd = np.std(datXYZ, axis=0)
    print(obssd.shape)
    obssd = obssd.reshape(63)
    datXYZ_reshaped = datXYZ.reshape(datXYZ.shape[0], -1)
    R = np.corrcoef(datXYZ_reshaped, rowvar=False)
    Sberria = genS(obssd, R, distor=distortion)
    z = multivariate_normal.rvs(mean=obsmean, cov=Sberria, size=1)
    print(z.shape)
    # print(z)
    # add here the code to transform z into an array eith the same shape as datXYZ
    first_row = datXYZ[attempt]  # Extract the attempt row of the array
    reshaped_array = first_row[:63].reshape(21, 3)  # Reshape the first row to (21, 3)
     # create noise from mean and std
    noise = np.random.normal(mean, std, reshaped_array.shape)
    reshaped_array_with_noise = reshaped_array + noise
    distorted_hand = z.reshape(reshaped_array.shape)
    hand_plot = plot_3d_points(reshaped_array, "Recorded Hand Configuration")
    distorted_hand_plot = plot_3d_points(distorted_hand, "Distorted Hand Configuration (global)")
    reshaped_array_with_noise_plot = plot_3d_points(reshaped_array_with_noise, "Distorted Hand Configuration (individual))")
    image_filename = image_path + str(int(gesto)) + ".png"
    print(image_filename)
    image = PIL.Image.open(image_filename)
    # image = Image(image_filename)  # Load the corresponding image
    return image, hand_plot, distorted_hand_plot, reshaped_array_with_noise_plot

inputs = [
    Number(value=42, precision=0, label="Number of Kind of Hand Configuration"),
    Number(value=0, precision=0, label="Number of Concrete Hand Attempt"),
    Number(value=1.0, label="Distortion Level"),
    Number(value=0.0, label="Mean Distortion Level (one hand)"),
    Number(value=0.006, label="Standard Deviation Distortion Level (one hand)")
]

outputs = [Image(type="pil"), Plot(), Plot(), Plot()]

title = "Hand 3D Visualization"
description = "Enter the gesture number (gesto) and the data path (data_path) to visualize the hand in 3D."

iface = gr.Interface(fn=show_hand_3d, 
                    inputs=inputs, 
                    outputs=outputs, 
                    title=title, 
                    description=description,
                    allow_flagging="never")
iface.launch()