import gradio as gr
import os
import matplotlib.pyplot as plt
from scipy.integrate import odeint
import torch
from torch.utils import data
from torch.utils.data import DataLoader, Dataset
from torch import nn, optim
import os
from skimage.transform import rescale, resize
from torch import nn, optim
import torch.nn.functional as F
from torch.utils.data import Subset
from scipy.interpolate import interp1d

#for pvloop simulator:
import pandas as pd
from scipy.integrate import odeint 
from scipy import interpolate
from scipy.interpolate import RegularGridInterpolator
from matplotlib import pyplot
import sys
import numpy as np
import collections
import pandas
import skimage.draw
import torchvision
import echonet

#odesolver:
from torch.storage import T
import argparse
import time

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

sequences_all = []
info_data_all = []
path = '/Users/FDean/Desktop/Physics_Informed_Transfer_Learning/EchoNet-Dynamic'
output_path = '/Users/FDean/Desktop/Physics_Informed_Transfer_Learning'

class Echo(torchvision.datasets.VisionDataset):
    """EchoNet-Dynamic Dataset.
    Args:
        root (string): Root directory of dataset (defaults to `echonet.config.DATA_DIR`)
        split (string): One of {``train'', ``val'', ``test'', ``all'', or ``external_test''}
        target_type (string or list, optional): Type of target to use,
            ``Filename'', ``EF'', ``EDV'', ``ESV'', ``LargeIndex'',
            ``SmallIndex'', ``LargeFrame'', ``SmallFrame'', ``LargeTrace'',
            or ``SmallTrace''
            Can also be a list to output a tuple with all specified target types.
            The targets represent:
                ``Filename'' (string): filename of video
                ``EF'' (float): ejection fraction
                ``EDV'' (float): end-diastolic volume
                ``ESV'' (float): end-systolic volume
                ``LargeIndex'' (int): index of large (diastolic) frame in video
                ``SmallIndex'' (int): index of small (systolic) frame in video
                ``LargeFrame'' (np.array shape=(3, height, width)): normalized large (diastolic) frame
                ``SmallFrame'' (np.array shape=(3, height, width)): normalized small (systolic) frame
                ``LargeTrace'' (np.array shape=(height, width)): left ventricle large (diastolic) segmentation
                    value of 0 indicates pixel is outside left ventricle
                             1 indicates pixel is inside left ventricle
                ``SmallTrace'' (np.array shape=(height, width)): left ventricle small (systolic) segmentation
                    value of 0 indicates pixel is outside left ventricle
                             1 indicates pixel is inside left ventricle
            Defaults to ``EF''.
        mean (int, float, or np.array shape=(3,), optional): means for all (if scalar) or each (if np.array) channel.
            Used for normalizing the video. Defaults to 0 (video is not shifted).
        std (int, float, or np.array shape=(3,), optional): standard deviation for all (if scalar) or each (if np.array) channel.
            Used for normalizing the video. Defaults to 0 (video is not scaled).
        length (int or None, optional): Number of frames to clip from video. If ``None'', longest possible clip is returned.
            Defaults to 16.
        period (int, optional): Sampling period for taking a clip from the video (i.e. every ``period''-th frame is taken)
            Defaults to 2.
        max_length (int or None, optional): Maximum number of frames to clip from video (main use is for shortening excessively
            long videos when ``length'' is set to None). If ``None'', shortening is not applied to any video.
            Defaults to 250.
        clips (int, optional): Number of clips to sample. Main use is for test-time augmentation with random clips.
            Defaults to 1.
        pad (int or None, optional): Number of pixels to pad all frames on each side (used as augmentation).
            and a window of the original size is taken. If ``None'', no padding occurs.
            Defaults to ``None''.
        noise (float or None, optional): Fraction of pixels to black out as simulated noise. If ``None'', no simulated noise is added.
            Defaults to ``None''.
        target_transform (callable, optional): A function/transform that takes in the target and transforms it.
        external_test_location (string): Path to videos to use for external testing.
    """

    def __init__(self, root=None,
                 split="train", target_type="EF",
                 mean=0., std=1.,
                 length=16, period=2,
                 max_length=250,
                 clips=1,
                 pad=None,
                 noise=None,
                 target_transform=None,
                 external_test_location=None):
        if root is None:
            root = path

        super().__init__(root, target_transform=target_transform)

        self.split = split.upper()
        if not isinstance(target_type, list):
            target_type = [target_type]
        self.target_type = target_type
        self.mean = mean
        self.std = std
        self.length = length
        self.max_length = max_length
        self.period = period
        self.clips = clips
        self.pad = pad
        self.noise = noise
        self.target_transform = target_transform
        self.external_test_location = external_test_location

        self.fnames, self.outcome = [], []

        if self.split == "EXTERNAL_TEST":
            self.fnames = sorted(os.listdir(self.external_test_location))
        else:
            # Load video-level labels
            with open(os.path.join(self.root, "FileList.csv")) as f:
                data = pandas.read_csv(f)
            data["Split"].map(lambda x: x.upper())

            if self.split != "ALL":
                data = data[data["Split"] == self.split]

            self.header = data.columns.tolist()
            self.fnames = data["FileName"].tolist()
            self.fnames = [fn + ".avi" for fn in self.fnames if os.path.splitext(fn)[1] == ""]  # Assume avi if no suffix
            self.outcome = data.values.tolist()

            # Check that files are present
            """
            missing = set(self.fnames) - set(os.listdir(os.path.join(self.root, "Videos")))
            if len(missing) != 0:
                print("{} videos could not be found in {}:".format(len(missing), os.path.join(self.root, "Videos")))
                for f in sorted(missing):
                    print("\t", f)
                raise FileNotFoundError(os.path.join(self.root, "Videos", sorted(missing)[0]))
            """

            # Load traces
            self.frames = collections.defaultdict(list)
            self.trace = collections.defaultdict(_defaultdict_of_lists)

            with open(os.path.join(self.root, "VolumeTracings.csv")) as f:
                header = f.readline().strip().split(",")
                assert header == ["FileName", "X1", "Y1", "X2", "Y2", "Frame"]

                for line in f:
                    filename, x1, y1, x2, y2, frame = line.strip().split(',')
                    x1 = float(x1)
                    y1 = float(y1)
                    x2 = float(x2)
                    y2 = float(y2)
                    frame = int(frame)
                    if frame not in self.trace[filename]:
                        self.frames[filename].append(frame)
                    self.trace[filename][frame].append((x1, y1, x2, y2))
            for filename in self.frames:
                for frame in self.frames[filename]:
                    self.trace[filename][frame] = np.array(self.trace[filename][frame])

            # A small number of videos are missing traces; remove these videos
            keep = [len(self.frames[f]) >= 2 for f in self.fnames]
            self.fnames = [f for (f, k) in zip(self.fnames, keep) if k]
            self.outcome = [f for (f, k) in zip(self.outcome, keep) if k]

    def __getitem__(self, index):
        # Find filename of video
        if self.split == "EXTERNAL_TEST":
            video = os.path.join(self.external_test_location, self.fnames[index])
        elif self.split == "CLINICAL_TEST":
            video = os.path.join(self.root, "ProcessedStrainStudyA4c", self.fnames[index])
        else:
            video = os.path.join(self.root, "Videos", self.fnames[index])

        # Load video into np.array
        video = echonet.utils.loadvideo(video).astype(np.float32)

        # Add simulated noise (black out random pixels)
        # 0 represents black at this point (video has not been normalized yet)
        if self.noise is not None:
            n = video.shape[1] * video.shape[2] * video.shape[3]
            ind = np.random.choice(n, round(self.noise * n), replace=False)
            f = ind % video.shape[1]
            ind //= video.shape[1]
            i = ind % video.shape[2]
            ind //= video.shape[2]
            j = ind
            video[:, f, i, j] = 0

        # Apply normalization
        if isinstance(self.mean, (float, int)):
            video -= self.mean
        else:
            video -= self.mean.reshape(3, 1, 1, 1)

        if isinstance(self.std, (float, int)):
            video /= self.std
        else:
            video /= self.std.reshape(3, 1, 1, 1)

        # Set number of frames
        c, f, h, w = video.shape
        if self.length is None:
            # Take as many frames as possible
            length = f // self.period
        else:
            # Take specified number of frames
            length = self.length

        if self.max_length is not None:
            # Shorten videos to max_length
            length = min(length, self.max_length)

        if f < length * self.period:
            # Pad video with frames filled with zeros if too short
            # 0 represents the mean color (dark grey), since this is after normalization
            video = np.concatenate((video, np.zeros((c, length * self.period - f, h, w), video.dtype)), axis=1)
            c, f, h, w = video.shape  # pylint: disable=E0633

        if self.clips == "all":
            # Take all possible clips of desired length
            start = np.arange(f - (length - 1) * self.period)
        else:
            # Take random clips from video
            start = np.random.choice(f - (length - 1) * self.period, self.clips)

        # Gather targets
        target = []
        for t in self.target_type:
            key = self.fnames[index]
            if t == "Filename":
                target.append(self.fnames[index])
            elif t == "LargeIndex":
                # Traces are sorted by cross-sectional area
                # Largest (diastolic) frame is last
                target.append(int(self.frames[key][-1]))
            elif t == "SmallIndex":
                # Largest (diastolic) frame is first
                target.append(int(self.frames[key][0]))
            elif t == "LargeFrame":
                target.append(video[:, self.frames[key][-1], :, :])
            elif t == "SmallFrame":
                target.append(video[:, self.frames[key][0], :, :])
            elif t in ["LargeTrace", "SmallTrace"]:
                if t == "LargeTrace":
                    t = self.trace[key][self.frames[key][-1]]
                else:
                    t = self.trace[key][self.frames[key][0]]
                x1, y1, x2, y2 = t[:, 0], t[:, 1], t[:, 2], t[:, 3]
                x = np.concatenate((x1[1:], np.flip(x2[1:])))
                y = np.concatenate((y1[1:], np.flip(y2[1:])))

                r, c = skimage.draw.polygon(np.rint(y).astype(np.int), np.rint(x).astype(np.int), (video.shape[2], video.shape[3]))
                mask = np.zeros((video.shape[2], video.shape[3]), np.float32)
                mask[r, c] = 1
                target.append(mask)
            else:
                if self.split == "CLINICAL_TEST" or self.split == "EXTERNAL_TEST":
                    target.append(np.float32(0))
                else:
                    target.append(np.float32(self.outcome[index][self.header.index(t)]))

        if target != []:
            target = tuple(target) if len(target) > 1 else target[0]
            if self.target_transform is not None:
                target = self.target_transform(target)

        # Select clips from video
        video = tuple(video[:, s + self.period * np.arange(length), :, :] for s in start)
        if self.clips == 1:
            video = video[0]
        else:
            video = np.stack(video)

        if self.pad is not None:
            # Add padding of zeros (mean color of videos)
            # Crop of original size is taken out
            # (Used as augmentation)
            c, l, h, w = video.shape
            temp = np.zeros((c, l, h + 2 * self.pad, w + 2 * self.pad), dtype=video.dtype)
            temp[:, :, self.pad:-self.pad, self.pad:-self.pad] = video  # pylint: disable=E1130
            i, j = np.random.randint(0, 2 * self.pad, 2)
            video = temp[:, :, i:(i + h), j:(j + w)]

        return video, target

    def __len__(self):
        return len(self.fnames)

    def extra_repr(self) -> str:
        """Additional information to add at end of __repr__."""
        lines = ["Target type: {target_type}", "Split: {split}"]
        return '\n'.join(lines).format(**self.__dict__)


def _defaultdict_of_lists():
    """Returns a defaultdict of lists.
    This is used to avoid issues with Windows (if this function is anonymous,
    the Echo dataset cannot be used in a dataloader).
    """

    return collections.defaultdict(list)
## 
print("Done loading training data!")
# define normalization layer to make sure output xi in an interval [ai, bi]:
# define normalization layer to make sure output xi in an interval [ai, bi]:


class IntervalNormalizationLayer(torch.nn.Module):
    def __init__(self):
        super().__init__()
        # new_output = [Tc, start_p, Emax, Emin, Rm, Ra, Vd]
        self.a = torch.tensor([0.4, 0., 0.5, 0.02, 0.005, 0.0001, 4.], dtype=torch.float32) #HR in 20-200->Tc in [0.3, 4]
        self.b = torch.tensor([1.7, 280., 3.5, 0.1, 0.1, 0.25, 16.], dtype=torch.float32)
        #taken out (initial conditions): a: 20, 5, 50; b: 400, 20, 100
    def forward(self, inputs):
        sigmoid_output = torch.sigmoid(inputs)
        scaled_output = sigmoid_output * (self.b - self.a) + self.a
        return scaled_output

class NEW3DCNN(nn.Module):
    def __init__(self, num_parameters):
        super(NEW3DCNN, self).__init__()
        
        self.conv1      = nn.Conv3d(3, 8, kernel_size=3, padding=1)
        self.batchnorm1 = nn.BatchNorm3d(8)
        self.conv2      = nn.Conv3d(8, 16, kernel_size=3, padding=1)
        self.batchnorm2 = nn.BatchNorm3d(16)
        self.conv3      = nn.Conv3d(16, 32, kernel_size=3, padding=1)
        self.batchnorm3 = nn.BatchNorm3d(32)
        self.conv4      = nn.Conv3d(32, 64, kernel_size=3, padding=1)
        self.batchnorm4 = nn.BatchNorm3d(64)
        self.conv5      = nn.Conv3d(64, 128, kernel_size=3, padding=1)
        self.batchnorm5 = nn.BatchNorm3d(128)
        self.pool       = nn.AdaptiveAvgPool3d(1)
        self.fc1        = nn.Linear(128, 512)
        self.fc2        = nn.Linear(512, num_parameters)
        self.norm1      = IntervalNormalizationLayer()
        
    def forward(self, x):
        x = F.relu(self.batchnorm1(self.conv1(x)))
        x = F.max_pool3d(x, kernel_size=2, stride=2)
        x = F.relu(self.batchnorm2(self.conv2(x)))
        x = F.max_pool3d(x, kernel_size=2, stride=2)
        x = F.relu(self.batchnorm3(self.conv3(x)))
        x = F.max_pool3d(x, kernel_size=2, stride=2)
        x = F.relu(self.batchnorm4(self.conv4(x)))
        x = F.max_pool3d(x, kernel_size=2, stride=2)
        x = F.relu(self.batchnorm5(self.conv5(x)))
        x = self.pool(x)
        x = x.view(x.size(0), -1)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        x = self.norm1(x)

        return x


# Define a neural network with one hidden layer
class Interpolator(nn.Module):
    def __init__(self):
        super().__init__()
        self.fc1 = nn.Linear(6, 250).double()
        self.fc2 = nn.Linear(250, 2).double()

    def forward(self, x):
        x = torch.relu(self.fc1(x))
        x = self.fc2(x)
        return x

# Initialize the neural network
net = Interpolator()
net.load_state_dict(torch.load('/Users/FDean/Desktop/Physics_Informed_Transfer_Learning/final_model_weights/interp6_7param_weight.pt'))
print("Done loading interpolator!")

weights_path = '/Users/FDean/Desktop/Physics_Informed_Transfer_Learning/final_model_weights/202_full_echonet_7param_Vloss_epoch_200_lr_0.001_weight_best_model.pt'
model = NEW3DCNN(num_parameters = 7)
model.load_state_dict(torch.load(weights_path))
model.to(device)

## PV loops

#returns Plv at time t using Elastance(t) and Vlv(t)-Vd=x1
def Plv(volume, Emax, Emin, t, Tc, Vd):
    return Elastance(Emax,Emin,t, Tc)*(volume - Vd)

#returns Elastance(t)
def Elastance(Emax,Emin, t, Tc):
    t = t-int(t/Tc)*Tc #can remove this if only want 1st ED (and the 1st ES before)
    tn = t/(0.2+0.15*Tc)
    return (Emax-Emin)*1.55*(tn/0.7)**1.9/((tn/0.7)**1.9+1)*1/((tn/1.17)**21.9+1) + Emin

def solve_ODE_for_volume(Rm, Ra, Emax, Emin, Vd, Tc, start_v, t):
    
    # the ODE from Simaan et al 2008
    def heart_ode(y, t, Rs, Rm, Ra, Rc, Ca, Cs, Cr, Ls, Emax, Emin, Tc):
        x1, x2, x3, x4, x5 = y #here y is a vector of 5 values (not functions), at time t, used for getting (dy/dt)(t)
        P_lv = Plv(x1+Vd,Emax,Emin,t,Tc,Vd)
        dydt = [r(x2-P_lv)/Rm-r(P_lv-x4)/Ra, (x3-x2)/(Rs*Cr)-r(x2-P_lv)/(Cr*Rm), (x2-x3)/(Rs*Cs)+x5/Cs, -x5/Ca+r(P_lv-x4)/(Ca*Ra), (x4-x3-Rc*x5)/Ls]
        return dydt

    # RELU for diodes
    def r(u):
        return max(u, 0.)
    
    # Define fixed parameters
    Rs = 1.0
    Rc = 0.0398
    Ca = 0.08
    Cs = 1.33
    Cr = 4.400
    Ls = 0.0005
    startp = 75.

    # Initial conditions
    start_pla = float(start_v*Elastance(Emax, Emin, 0, Tc))
    start_pao = startp
    start_pa = start_pao
    start_qt = 0 #aortic flow is Q_T and is 0 at ED, also see Fig5 in simaan2008dynamical
    y0 = [start_v, start_pla, start_pa, start_pao, start_qt]

    # Solve
    sol = odeint(heart_ode, y0, t, args = (Rs, Rm, Ra, Rc, Ca, Cs, Cr, Ls, Emax, Emin, Tc)) #t: list of values

    # volume is the first state variable plus theoretical zero pressure volume
    volumes = np.array(sol[:, 0]) + Vd
    
    return volumes

def pvloop_simulator(Rm, Ra, Emax, Emin, Vd, Tc, start_v):
    

    # Define initial parameters
    init_Emax = Emax # 3.0 # .5 to 3.5
    init_Emin = Emin # 0.04 # .02 to .1
    # init_Tc = Tc # .4 # .4 to 1.7
    init_Vd = Vd # 10.0 # 0 to 25
    
    # DUMMY VOLUME
    # def volume(t, Tc):
        # return 50*np.sin(2 * np.pi * t*(1/Tc))+100
    
    # SOLVE the ODE model for the VOLUME CURVE
    N = 100
    t =  np.linspace(0, Tc*N, int(60000*N)) #np.linspace(1, 100, 1000000)
    volumes = solve_ODE_for_volume(Rm, Ra, Emax, Emin, Vd, Tc, start_v, t)
    
    # FUNCTIONS for PRESSURE CURVE
    vectorized_Elastance = np.vectorize(Elastance)
    vectorized_Plv = np.vectorize(Plv)

    def pressure(t, volume, Emax, Emin, Tc, Vd):
        return vectorized_Plv(volume, Emax, Emin, t, Tc, Vd)

    # calculate PRESSURE
    pressures = pressure(t, volumes, init_Emax, init_Emin, Tc, init_Vd)
    
    # Create the figure and the loop that we will manipulate
    fig, ax = plt.subplots()
    plt.ylim((0,280))
    plt.xlim((0,280))
    line = ax.plot(volumes[(N-2)*60000:(N)*60000], pressures[(N-2)*60000:(N)*60000], lw=1)
    #print(line)
    line = line[0]
    #print(line)
    
    fig.suptitle('Predicted PI-SSL LV Pressure Volume Loop', fontsize=16)
    #plt.rcParams['fig.suptitle'] = -2.0
    #ax.set_title(f'Mitral valve circuit resistance (Rm): {Rm} mmHg*s/ml \n Aortic valve circuit resistance (Ra): {Ra} mmHg*s/ml', fontsize=6)
    ax.set_xlabel('LV Volume (ml)')
    ax.set_ylabel('LV Pressure (mmHg)')

    # adjust the main plot to make room for the sliders
    fig.subplots_adjust(left=0.25, bottom=0.25)
    
    return plt, Rm, Ra, Emax, Emin, Vd, Tc, start_v

def pvloop_simulator_plot_only(Rm, Ra, Emax, Emin, Vd, Tc, start_v):
    plot,_,_,_,_,_,_,_ =pvloop_simulator(Rm, Ra, Emax, Emin, Vd, Tc, start_v)
    return plot

## Demo 

def generate_example():
        # get random input
        data_path = '/Users/FDean/Desktop/Physics_Informed_Transfer_Learning/EchoNet-Dynamic'
        image_data = Echo(root = data_path, split = 'all', target_type=['Filename','LargeIndex','SmallIndex'])
        image_loaded_data = DataLoader(image_data, batch_size=1, shuffle=True)
        val_data = next(iter(image_loaded_data))
        #create_echo_clip(val_data,'test')
        val_seq = val_data[0]
        filename = val_data[1][0][0]
        video = os.path.join(os.getcwd(), f"EchoNet-Dynamic/Videos/{filename}")
        val_tensor = torch.tensor(val_seq, dtype=torch.float32) 
        results = model(val_tensor)

        plot, Rm, Ra, Emax, Emin, Vd,Tc, start_v = pvloop_simulator(Rm=round(results[0][4].item(),2), Ra=round(results[0][5].item(),2), Emax=results[0][2].item(), Emin=round(results[0][3].item(),2), Vd=round(results[0][6].item(),2), Tc=round(results[0][0].item(),2), start_v=round(results[0][1].item(),2))
    
        return video, plot, Rm, Ra, Emax, Emin, Vd, Tc, start_v

title = "Physics-informed self-supervised learning for predicting cardiac digital twins with echocardiography"

description = """
<p style='text-align: center'> Keying Kuang, Frances Dean, Jack B. Jedlicki, David Ouyang, Anthony Philippakis, David Sontag, Ahmed Alaa  <br>
 <a href='https://github.com/AlaaLab/CardioPINN' target='_blank'>Code</a></p>
We develop methodology for predicting digital twins from non-invasive cardiac ultrasound images in <a href='https://arxiv.org/abs/2403.00177'>Non-Invasive Medical Digital Twins using Physics-Informed Self-Supervised Learning</a>. \n\n 
We demonstrate the ability of our model to predict left ventricular pressure-volume loops using image data here.
"""

gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>" + title + "</h1>")
gr.Markdown(description)

with gr.Blocks() as demo:
        
    # text
    gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>" + title + "</h1>")
    gr.Markdown(description)
        
    with gr.Row():
        with gr.Column(scale=1.5, min_width=100):
            
            generate_button = gr.Button("Load sample echocardiogram and generate result")
            with gr.Row():
                video = gr.PlayableVideo(format="avi")
                plot = gr.Plot()
            
            with gr.Row():
                Rm = gr.Number(label="Mitral valve circuit resistance (Rm) mmHg*s/ml:")
                Ra = gr.Number(label="Aortic valve circuit resistance (Ra) mmHg*s/ml:")
                Emax = gr.Number(label="Maximum elastance (Emax) mmHg/ml:")
                Emin = gr.Number(label="Minimum elastance (Emin) mmHg/ml:")
                Vd = gr.Number(label="Theoretical zero pressure volume (Vd) ml:")
                Tc = gr.Number(label="Cycle duration (Tc) s:")
                start_v = gr.Number(label="Initial volume (start_v) ml:")
                
            simulation_button = gr.Button("Run simulation")
            
            
            
            with gr.Row():
                sl1 = gr.Slider(0.005, 0.1, value=Rm, label="Rm")
                sl2 = gr.Slider(0.0001, 0.25, value=Ra, label="Ra")
                sl3 = gr.Slider(0.5, 3.5, value=Emax, label="Emax")
                sl4 = gr.Slider(0.02, 0.1, value= Emin, label="Emin")
                sl5 = gr.Slider(4.0, 25.0, value=Vd, label="Vd")
                sl6 = gr.Slider(0.4, 1.7, value=Tc, label="Tc") 
                sl7 = gr.Slider(0.0, 280.0, value=start_v, label="start_v")
    
    
    generate_button.click(fn=generate_example, outputs = [video,plot,Rm,Ra,Emax,Emin,Vd,Tc,start_v])
    
    
    simulation_button.click(fn=pvloop_simulator_plot_only, inputs = [sl1,sl2,sl3,sl4,sl5,sl6,sl7], outputs = [gr.Plot()])
    
    
    
demo.launch(share=True)