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🗣 Wd提问: ai聊天源码

🤖 Ai回答:
AICHAT

以下是一个简单的AI聊天程序的源码，使用Python语言和自然语言处理（NLP）库实现：

python
import nltk
from nltk.stem.lancaster import LancasterStemmer
stemmer = LancasterStemmer()
import numpy
import tflearn
import tensorflow
import random
import json
import pickle

nltk.download('punkt')

with open(intents.json) as file:
data = json.load(file)

try:
with open(data.pickle, rb) as f:
words, labels, training, output = pickle.load(f)
except:
words = []
labels = []
docs_x = []
docs_y = []

for intent in data[intents]:
for pattern in intent[patterns]:
wrds = nltk.word_tokenize(pattern)
words.extend(wrds)
docs_x.append(wrds)
docs_y.append(intent[tag])

if intent[tag] not in labels:
labels.append(intent[tag])

words = [stemmer.stem(w.lower()) for w in words if w != ?]
words = sorted(list(set(words)))

labels = sorted(labels)

training = []
output = []

out_empty = [0 for _ in range(len(labels))]

for x, doc in enumerate(docs_x):
bag = []

wrds = [stemmer.stem(w.lower()) for w in doc]

for w in words:
if w in wrds:
bag.append(1)
else:
bag.append(0)

output_row = out_empty[:]
output_row[labels.index(docs_y[x])] = 1

training.append(bag)
output.append(output_row)

training = numpy.array(training)
output = numpy.array(output)

with open(data.pickle, wb) as f:
pickle.dump((words, labels, training, output), f)

tensorflow.reset_default_graph()

net = tflearn.input_data(shape=[None, len(training[0])])
net = tflearn.fully_connected(net, 8)
net = tflearn.fully_connected(net, 8)
net = tflearn.fully_connected(net, len(output[0]), activation=softmax)
net = tflearn.regression(net)

model = tflearn.DNN(net)

try:
model.load(model.tflearn)
except:
model.fit(training, output, n_epoch=1000, batch_size=8, show_metric=True)
model.save(model.tflearn)

def bag_of_words(s, words):
bag = [0 for _ in range(len(words))]

s_words = nltk.word_tokenize(s)
s_words = [stemmer.stem(word.lower()) for word in s_words]

for se in s_words:
for i, w in enumerate(words):
if w == se:
bag[i] = 1

return numpy.array(bag)

def chat():
print(Start talking with the bot! (type quit to stop))
while True:
inp = input(You: )
if inp.lower() == quit:
break

p = bag_of_words(inp, words)
res = model.predict([p])[0]
ERROR_THRESHOLD = 0.25
results = [[i, r] for i, r in enumerate(res) if rERROR_THRESHOLD]

results.sort(key=lambda x: x[1], reverse=True)
tag = labels[results[0][0]]
for tg in data[intents]:
if(tg['tag']== tag):
responses= tg['responses']

print(random.choice(responses))

chat()

以下是`intents.json`文件示例：
json
{
intents: [
{
tag: greeting,
patterns: [Hi, Hey, Hello],
responses: [Hi there!, How can I help you?, What's up?]
},
{
tag: goodbye,
patterns: [Bye, See you later, Goodbye],
responses: [See you!, Have a nice day!, Bye! Come back again soon.]
},
{
tag: thanks,
patterns: [Thanks, Thank you],
responses: [You're welcome!, No problem!]
}
]
}

这个源码实现了一个简单的聊天机器人，可以根据用户的输入返回预定义的回答。

说明：

1、`intents.json` 文件存储了聊天机器人的知识库，定义了各种意图（tag）和对应的问句（patterns）以及回答（responses）。
2、聊天机器人使用 `nltk` 库进行分词和词干提取。
3、使用 `tflearn` 库构建了一个简单的神经网络模型来分类用户的输入。
4、在 `chat()` 函数中，用户输入会被转化为词袋向量，然后使用模型进行预测，返回最可能的意图和回答。

如何运行：

1、安装所需的库：`pip install nltk tflearn tensorflow`.
2、创建一个 `intents.json` 文件，内容如上。
3、运行源码：`python chatbot.py`.

注意：

* 这个源码只是一个简单的示例，实际应用中可能需要更多的功能和优化。
* 需要根据具体的需求和语料库调整模型的参数和结构。

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