import numpy as np
import streamlit as st
import tensorflow as tf
import keras
import matplotlib.pyplot as plt
import torch
from skorch import NeuralNetClassifier
from torch import nn
import torch.nn.functional as F
import torchvision.transforms as transforms


class Cnn(nn.Module):
    def __init__(self, dropout=0.5):
        super(Cnn, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, kernel_size=3)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=3)
        self.conv2_drop = nn.Dropout2d(p=dropout)
        self.fc1 = nn.Linear(1600, 100) # 1600 = number channels * width * height
        self.fc2 = nn.Linear(100, 10)
        self.fc1_drop = nn.Dropout(p=dropout)

    def forward(self, x):
        x = torch.relu(F.max_pool2d(self.conv1(x), 2))
        x = torch.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))

        # flatten over channel, height and width = 1600
        x = x.view(-1, x.size(1) * x.size(2) * x.size(3))

        x = torch.relu(self.fc1_drop(self.fc1(x)))
        x = torch.softmax(self.fc2(x), dim=-1)
        return x