app.py

import streamlit as st
from sudoku.train import SudokuTrialErrorLightning, TrialEveryPosException
from sudoku.helper import display_as_dataframe, get_grid_number_soluce
import numpy as np
import re
import torch

model = SudokuTrialErrorLightning.load_from_checkpoint(checkpoint_path="model_9_m3_big_validation_th.ckpt")

empty_grid = '''
0 0 0 | 0 0 0 | 0 0 0
0 0 0 | 0 0 0 | 0 0 0
0 0 0 | 0 0 0 | 0 0 0
---------------------
0 0 0 | 0 0 0 | 0 0 0
0 0 0 | 0 0 0 | 0 0 0
0 0 0 | 0 0 0 | 0 0 0
---------------------
0 0 0 | 0 0 0 | 0 0 0
0 0 0 | 0 0 0 | 0 0 0
0 0 0 | 0 0 0 | 0 0 0
'''
grid_evil_0 = '''
    [0,0,0, 1,6,0, 2,0,0],
    [0,3,0, 0,0,5, 0,0,4],
    [0,0,7, 0,0,0, 0,9,0],

    [0,0,1, 0,0,0, 0,8,2],
    [3,0,0, 7,0,4, 0,0,5],
    [4,2,0, 0,0,0, 6,0,0],

    [0,8,0, 0,0,0, 5,0,0],
    [7,0,0, 8,0,0, 0,2,0],
    [0,0,6, 0,5,1, 0,0,0],
'''
grid_evil_1 = '''
    [0,0,0, 2,0,0, 0,0,0],
    [0,0,9, 7,0,0, 3,8,0],
    [3,6,0, 0,0,0, 0,7,0],

    [1,0,6, 8,0,0, 0,0,0],
    [0,0,0, 3,5,9, 0,0,0],
    [0,0,0, 0,0,2, 8,0,5],

    [0,4,0, 0,0,0, 0,9,1],
    [0,2,5, 0,0,8, 7,0,0],
    [0,0,0, 0,0,6, 0,0,0],
]
'''
grid_evil_2 = '''
    [0,2,0, 1,0,0, 0,7,0],
    [0,0,0, 3,0,2, 0,0,0],
    [0,0,1, 0,8,0, 0,4,0],

    [9,0,0, 0,1,0, 0,0,7],
    [0,0,8, 0,6,0, 0,5,0],
    [0,0,0, 0,0,0, 0,0,0],

    [0,8,0, 0,3,6, 0,0,9],
    [0,0,5, 0,7,0, 0,0,6],
    [0,0,2, 0,0,0, 0,0,3],
]
'''
grids = {
    'empty': empty_grid,
    'evil_1': grid_evil_0,
    'evil_2': grid_evil_1, 
    'evil_3': grid_evil_2
}

def str_to_row_col_grid(su_str):
    su_str = re.sub(r'[^\d.]', '', su_str).replace('.','0')

    assert len(su_str)==81
    return [[int(su_str[j*9+i]) for i in range(9)] for j in range(9)]

def build_tensor_out_of_grid(grid):
    np_grid = np.asarray(grid) 
    big_grid = np.zeros((1,2,9,9,9), dtype=np.float32)
    for i in range(9):
        big_grid[0,1,:,:,i] = (np_grid==i+1)
        big_grid[0,0,:,:,i] = ((np_grid!=i+1) & (np_grid!=0))
    return torch.from_numpy(big_grid).view(1,2,729)

def build_grid_and_display(str_grid):
    test_x = build_tensor_out_of_grid(str_grid)
    return test_x, display_as_dataframe(test_x)
st.markdown('# Deep learning sudoku Solver')
st.markdown('### Author: Sébastien Guissart')

option = st.selectbox(
    "select template grids",
    grids,
)
x = st.text_area('grid', value=grids[option], height=300)

tensor_grid, df_styler = build_grid_and_display(str_to_row_col_grid(x))
# st.table(df_styler)
# st.dataframe(df_styler)
html = df_styler.to_html(escape=False, index=False)
# # st.text(html)
st.html(html)
st.markdown('''
            ## Checking the number of solutions 
            (with backtracking algorithm)
            must be equal 1
            ''')
n_sol = get_grid_number_soluce(str_to_row_col_grid(x))

st.markdown(f"number of solution: {'>2' if n_sol==2 else n_sol}")
if n_sol==1:
    new_X = model.predict(tensor_grid)
    X_sum= new_X.sum()
    st.html(display_as_dataframe(new_X).to_html(escape=False, index=False))
    i=1
    while new_X.sum()<729:
        i+=1
        st.markdown(f'iteration {i}')
        try:
            new_X = model.predict(new_X)
        except TrialEveryPosException:
            st.markdown('''## The grid is super evil!
            please share it as A Discussion in the `Community` tab.
            Except if it is this one: https://www.telegraph.co.uk/news/science/science-news/9359579/Worlds-hardest-sudoku-can-you-crack-it.html''')
            is_valid, new_X = model.backtracking_predict(new_X)
            assert is_valid
        st.html(display_as_dataframe(new_X).to_html(escape=False, index=False))
        new_X_sum = new_X.sum()
        assert new_X_sum> X_sum
        X_sum = new_X_sum
    st.markdown('## Grid solved!')