forked from Alexey/DAS_2023_1
44 lines
1.1 KiB
Python
44 lines
1.1 KiB
Python
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from flask import Flask, render_template, request
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import numpy as np
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import concurrent.futures
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app = Flask(__name__)
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def multiply_matrices(matrix_a, matrix_b):
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result = np.dot(matrix_a, matrix_b)
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return result
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def multiply_matrices_parallel(matrix_a, matrix_b):
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with concurrent.futures.ThreadPoolExecutor() as executor:
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result = executor.submit(np.dot, matrix_a, matrix_b)
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return result.result()
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@app.route('/')
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def index():
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return render_template('index.html')
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@app.route('/multiply', methods=['POST'])
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def multiply():
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n = int(request.form.get('matrix_a'))
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matrix_a = np.random.randint(10, size=(n, n))
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matrix_b = np.random.randint(10, size=(n, n))
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operation_type = request.form.get('operation_type')
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if operation_type == 'sequential':
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result = multiply_matrices(matrix_a, matrix_b)
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elif operation_type == 'parallel':
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result = multiply_matrices_parallel(matrix_a, matrix_b)
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else:
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return "Invalid operation type"
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return render_template('result.html', matrix_a=matrix_a, matrix_b=matrix_b, result=result)
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if __name__ == '__main__':
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app.run(debug=True)
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