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import os
os.environ["KERAS_BACKEND"] = "torch"
from keras import models, utils
import gradio as gr
import numpy as np
import pandas as pd
from huggingface_hub import hf_hub_download
def echo_sudoku(sudoku, model_name):
model = models.load_model(hf_hub_download(
repo_id="Ritvik19/SudokuNet",
filename=f"{model_name}.keras",
))
puzzles = sudoku.copy().values.reshape(1, 9, 9)
for _ in range((puzzles == 0).sum((1, 2)).max()):
model_preds = model.predict(
utils.to_categorical(puzzles, num_classes=10), verbose=0
)
preds = np.zeros((puzzles.shape[0], 81, 9))
for i in range(9):
for j in range(9):
preds[:, i * 9 + j] = model_preds[f"position_{i+1}_{j+1}"]
probs = preds.max(2)
values = preds.argmax(2) + 1
zeros = (puzzles == 0).reshape((puzzles.shape[0], 81))
for grid, prob, value, zero in zip(puzzles, probs, values, zeros):
if any(zero):
where = np.where(zero)[0]
confidence_position = where[prob[zero].argmax()]
confidence_value = value[confidence_position]
grid.flat[confidence_position] = confidence_value
return puzzles[0]
model_types = ['ffn', 'cnn']
model_sizes = ['64x2', '64x4', '128x2', '128x4']
model_names = [f"{model_type}__{model_size}" for model_type in model_types for model_size in model_sizes]
DEFAULT_PUZZLE = """
0 0 4 3 0 0 2 0 9
0 0 5 0 0 9 0 0 1
0 7 0 0 6 0 0 4 3
0 0 6 0 0 2 0 8 7
1 9 0 0 0 7 4 0 0
0 5 0 0 8 3 0 0 0
6 0 0 0 0 0 1 0 5
0 0 3 5 0 8 6 9 0
0 4 2 9 1 0 3 0 0
""".strip()
DEFAULT_PUZZLE = np.array([int(digit) for digit in DEFAULT_PUZZLE.split()]).reshape(9, 9)
interface = gr.Interface(
fn=echo_sudoku,
inputs=[
gr.Dataframe(label="Input Sudoku Puzzle", datatype="number", row_count=9, col_count=9, value=DEFAULT_PUZZLE),
gr.Dropdown(label="Select Model", choices=model_names, value="cnn__64x2")
],
outputs=gr.Dataframe(label="Input Sudoku Puzzle", datatype="number", row_count=9, col_count=9),
title="Sudoku Solver",
description='A demo app for <a href="https://ritvik19.github.io/sudoku-net" target="_blank">SudokuNet</a>'
)
# Run the app
if __name__ == "__main__":
interface.launch(debug=True) |