如何识别文字是哪个国家的

pip install langdetect

from langdetect import detect

text = "Bonjour tout le monde"
language = detect(text)
print(language)  # 输出: 'fr' (法语)

pip install langid

import langid

text = "Hola, ¿cómo estás?"
language, confidence = langid.classify(text)
print(language)  # 输出: 'es' (西班牙语)

import tensorflow as tf
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Embedding, LSTM, Dense

# 假设你有一个训练数据集
texts = ["Hello world", "Bonjour tout le monde", "Hola, ¿cómo estás?"]
labels = [0, 1, 2]  # 0: English, 1: French, 2: Spanish

# 预处理数据
tokenizer = Tokenizer(num_words=10000)
tokenizer.fit_on_texts(texts)
sequences = tokenizer.texts_to_sequences(texts)
data = pad_sequences(sequences, maxlen=100)

# 构建模型
model = Sequential()
model.add(Embedding(10000, 128, input_length=100))
model.add(LSTM(128))
model.add(Dense(3, activation='softmax'))

model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

# 训练模型
model.fit(data, labels, epochs=10)

# 预测
test_text = "Hola, ¿qué tal?"
test_sequence = tokenizer.texts_to_sequences([test_text])
test_data = pad_sequences(test_sequence, maxlen=100)
prediction = model.predict(test_data)
print(prediction)  # 输出: 预测的语言类别

pip install google-cloud-translate

from google.cloud import translate_v2 as translate

client = translate.Client()

text = "こんにちは"
result = client.detect_language(text)
print(result['language'])  # 输出: 'ja' (日语)

pip install azure-ai-textanalytics

from azure.ai.textanalytics import TextAnalyticsClient
from azure.core.credentials import AzureKeyCredential

key = "YOUR_AZURE_KEY"
endpoint = "YOUR_AZURE_ENDPOINT"

client = TextAnalyticsClient(endpoint=endpoint, credential=AzureKeyCredential(key))

documents = ["Hallo Welt"]
response = client.detect_language(documents=documents)[0]
print(response.primary_language.name)  # 输出: 'German'