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import streamlit as st
import seaborn as sns
import matplotlib.pyplot as plt
import os, random
import cv2
from tensorflow.keras.models import load_model
from PIL import Image
def main():
st.title('Sudoku Solver')
uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
if uploaded_file is not None:
# Importing puzzle to be solved
puzzle = cv2.imread(uploaded_file)
# Resizing puzzle to be solved
puzzle = cv2.resize(puzzle, (450,450))
# Preprocessing Puzzle
su_puzzle = preprocess(puzzle)
# Finding the outline of the sudoku puzzle in the image
su_contour_1= su_puzzle.copy()
su_contour_2= sudoku_a.copy()
su_contour, hierarchy = cv2.findContours(su_puzzle,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
cv2.drawContours(su_contour_1, su_contour,-1,(0,255,0),3)
black_img = np.zeros((450,450,3), np.uint8)
su_biggest, su_maxArea = main_outline(su_contour)
if su_biggest.size != 0:
su_biggest = reframe(su_biggest)
cv2.drawContours(su_contour_2,su_biggest,-1, (0,255,0),10)
su_pts1 = np.float32(su_biggest)
su_pts2 = np.float32([[0,0],[450,0],[0,450],[450,450]])
su_matrix = cv2.getPerspectiveTransform(su_pts1,su_pts2)
su_imagewrap = cv2.warpPerspective(puzzle,su_matrix,(450,450))
su_imagewrap =cv2.cvtColor(su_imagewrap, cv2.COLOR_BGR2GRAY)
sudoku_cell = splitcells(su_imagewrap)
sudoku_cell_croped= CropCell(sudoku_cell)
grid = read_cells(sudoku_cell_croped, model)
grid = np.asarray(grid)
grid = np.reshape(grid,(9,9))
solve(grid)
# Display the solution or appropriate message
if solve(grid):
st.header("Sudoku Solved:")
st.write(Solved(grid))
else:
st.write("No solution found.")
if __name__ == '__main__':
main()
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