帮我全部写代码用python读取poetry.h5预料库，输入文字在前端啊生成藏尾诗

好的，以下是一个完整的 Python 代码示例和一个简单的前端页面，可以用于读取 poetry.h5 数据集、训练 LSTM 模型、保存模型、加载模型和生成藏尾诗。 Python 代码： ```python import h5py import numpy as np from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, LSTM, Dropout from tensorflow.keras.callbacks import ModelCheckpoint # 加载数据集 with h5py.File('poetry.h5', 'r') as f: data = f['poetry'][:] # 预处理数据 data = [str(i, encoding='utf-8') for i in data] characters = sorted(list(set(''.join(data)))) char_indices = dict((c, i) for i, c in enumerate(characters)) indices_char = dict((i, c) for i, c in enumerate(characters)) maxlen = 6 step = 1 sentences = [] next_chars = [] for i in range(0, len(data) - maxlen, step): sentences.append(data[i:i+maxlen]) next_chars.append(data[i+maxlen]) x = np.zeros((len(sentences), maxlen, len(characters)), dtype=np.bool) y = np.zeros((len(sentences), len(characters)), dtype=np.bool) for i, sentence in enumerate(sentences): for t, char in enumerate(sentence): x[i, t, char_indices[char]] = 1 y[i, char_indices[next_chars[i]]] = 1 # 构建模型 model = Sequential() model.add(LSTM(128, input_shape=(maxlen, len(characters)))) model.add(Dropout(0.2)) model.add(Dense(len(characters), activation='softmax')) model.compile(loss='categorical_crossentropy', optimizer='adam') # 定义回调函数 filepath = 'model.h5' checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=1, save_best_only=True, mode='min') callbacks_list = [checkpoint] # 训练模型 model.fit(x, y, epochs=50, batch_size=128, callbacks=callbacks_list) # 生成藏尾诗 def generate_poem(input_text): model.load_weights(filepath) generated = input_text for i in range(5): x_pred = np.zeros((1, maxlen, len(characters))) for t, char in enumerate(input_text): x_pred[0, t, char_indices[char]] = 1 preds = model.predict(x_pred, verbose=0)[0] next_index = np.argmax(preds) next_char = indices_char[next_index] generated += next_char input_text = input_text[1:] + next_char return generated # 保存模型 model.save('model.h5') ``` 前端代码： ```html <!DOCTYPE html> <html> <head> <meta charset="utf-8"> <title>藏尾诗生成器</title> </head> <body> <h1>藏尾诗生成器</h1> <p>请输入一句话：</p> <input type="text" id="input_text"> <button onclick="generatePoem()">生成</button> <p><span id="generated_poem"></span></p> <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@3.3.0"></script> <script> async function generatePoem() { const inputText = document.getElementById('input_text').value; const model = await tf.loadLayersModel('model.h5'); let generated = inputText; for (let i = 0; i < 5; i++) { const xPred = tf.zeros([1, 6, 5562]); for (let t = 0; t < inputText.length; t++) { xPred.buffer().set(1, 0, t, inputText.charCodeAt(t)); } const preds = model.predict(xPred); const nextIndex = preds.argMax(1).dataSync()[0]; const nextChar = String.fromCharCode(nextIndex); generated += nextChar; inputText = inputText.substring(1) + nextChar; } document.getElementById('generated_poem').innerHTML = generated; } </script> </body> </html> ``` 你需要将上述 Python 代码保存为一个 .py 文件，并在终端中运行它来训练模型和保存模型。然后，将上述 HTML 代码保存为一个 .html 文件，并在浏览器中打开它以使用藏尾诗生成器。注意，你需要将 poetry.h5 文件和 model.h5 文件放在与 .html 文件相同的目录中。

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帮我全部写代码用python读取poetry.h5预料库，输入文字在前端啊生成藏尾诗

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