修改代码，我需要是从文件中导入，然后结果画出图

以下是修改后的代码，可以从文件中导入图的邻接矩阵，然后使用 `networkx` 库绘制最小割结果的图形：

import random
import networkx as nx
import matplotlib.pyplot as plt

def min_cut(graph):
    n = len(graph)
    # 初始化点集和边集
    vertices = [i for i in range(n)]
    edges = []
    for i in range(n):
        for j in range(i+1, n):
            if graph[i][j] > 0:
                edges.append((i, j))

    while len(vertices) > 1:
        # 随机选择一个点作为源点
        s = random.choice(vertices)
        # 初始化两个集合
        A = [s]
        B = [v for v in vertices if v != s]
        # 初始化两个集合的权值和
        wA = [0] * n
        wB = [0] * n

        while len(B) > 0:
            # 找到连接A和B的最小横切边
            min_cut_edge = (-1, -1)
            min_cut_weight = float('inf')
            for i, j in edges:
                if i in A and j in B and graph[i][j] < min_cut_weight:
                    min_cut_edge = (i, j)
                    min_cut_weight = graph[i][j]
                elif j in A and i in B and graph[j][i] < min_cut_weight:
                    min_cut_edge = (j, i)
                    min_cut_weight = graph[j][i]

            # 将该边的两个端点合并到同一个集合中
            if min_cut_edge[0] in A:
                A.append(min_cut_edge[1])
                B.remove(min_cut_edge[1])
                for i in range(n):
                    wA[i] += graph[min_cut_edge[1]][i]
                    wB[i] -= graph[min_cut_edge[1]][i]
            else:
                B.append(min_cut_edge[0])
                A.remove(min_cut_edge[0])
                for i in range(n):
                    wB[i] += graph[min_cut_edge[0]][i]
                    wA[i] -= graph[min_cut_edge[0]][i]

        # 计算剩余边的权值
        cut_weight = 0
        for i in range(n):
            if i in B:
                cut_weight += wB[i]

        # 删除该点和与该点相连的所有边
        vertices.remove(s)
        new_graph = [[0] * (len(vertices)) for _ in range(len(vertices))]
        for i in range(len(vertices)):
            for j in range(i+1, len(vertices)):
                new_graph[i][j] = graph[vertices[i]][vertices[j]]
                new_graph[j][i] = graph[vertices[j]][vertices[i]]

        # 更新边集
        edges = []
        for i in range(len(vertices)):
            for j in range(i+1, len(vertices)):
                if new_graph[i][j] > 0:
                    edges.append((i, j))

        # 更新图
        graph = new_graph

    # 返回最小割的权值
    return cut_weight

# 从文件中导入邻接矩阵
with open('graph.txt', 'r') as f:
    lines = f.readlines()
    graph = [[int(x) for x in line.strip().split()] for line in lines]

# 计算最小割
min_cut_weight = min_cut(graph)
print('最小割的权值为:', min_cut_weight)

# 使用networkx库绘制图形
G = nx.Graph()
for i in range(len(graph)):
    for j in range(i+1, len(graph)):
        if graph[i][j] > 0:
            G.add_edge(i, j, weight=graph[i][j])

pos = nx.spring_layout(G)
cut_edges = []
for i, j in G.edges():
    if i in A and j in B:
        cut_edges.append((i, j))

nx.draw_networkx_nodes(G, pos, node_size=500, node_color='y')
nx.draw_networkx_labels(G, pos, font_size=16)
nx.draw_networkx_edges(G, pos, edge_color='b', width=1)
nx.draw_networkx_edge_labels(G, pos, edge_labels={(i, j): graph[i][j] for i, j in G.edges()}, font_size=12, label_pos=0.5)
nx.draw_networkx_edges(G, pos, edgelist=cut_edges, edge_color='r', width=2)
plt.axis('off')
plt.show()

其中，`graph.txt` 文件中存放了图的邻接矩阵，其格式如下：

0 2 3 4
2 0 1 0
3 1 0 5
4 0 5 0

运行代码后，会输出最小割的权值，并显示绘制的最小割结果的图形。其中，红色的边表示最小割的割边，蓝色的边表示原图的边，边权值标在边上。