ChatGPT和Python的结合：开发智能对话系统的最佳实践

# 导入相关库
import JSON

# 读取对话数据集
def read_dialogues(file_path):
    dialogues = []
    with open(file_path, 'r', encoding='utf-8') as file:
        for line in file:
            dialogue = json.loads(line)
            dialogues.append(dialogue)
    return dialogues

# 调用函数读取对话数据集
dialogues = read_dialogues('dialogues.json')

# 导入相关库
from transformers import GPT2LMHeadModel, GPT2Tokenizer, TrainingArguments, Trainer

# 初始化模型和Tokenizer
model_name = "gpt2"
model = GPT2LMHeadModel.from_pretrained(model_name)
tokenizer = GPT2Tokenizer.from_pretrained(model_name)

# 将对话数据转换为模型可接受的格式
def preprocess_dialogues(dialogues):
    inputs = []
    labels = []
    for dialogue in dialogues:
        conversation = dialogue['conversation']
        for i in range(1, len(conversation), 2):
            inputs.append(conversation[i-1])
            labels.append(conversation[i])
    return inputs, labels

# 调用函数转换对话数据
inputs, labels = preprocess_dialogues(dialogues)

# 将对话数据转换为模型输入编码
inputs_encoded = tokenizer.batch_encode_plus(inputs, padding=True, truncation=True, return_tensors="pt")
labels_encoded = tokenizer.batch_encode_plus(labels, padding=True, truncation=True, return_tensors="pt")

# 训练参数配置
training_args = TrainingArguments(
    output_dir='./results',
    num_train_epochs=5,
    per_device_train_batch_size=8,
    per_device_eval_batch_size=8,
    warmup_steps=500,
    weight_decay=0.01,
    logging_dir='./logs',
    logging_steps=100
)

# 定义Trainer并进行模型训练
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=inputs_encoded['input_ids'],
    eval_dataset=labels_encoded['input_ids']
)

# 开始训练模型
trainer.train()

# 导入相关库
from flask import Flask, request, jsonify

# 初始化Flask应用
app = Flask(__name__)
  
# 定义路由
@app.route("/chat", methods=["POST"])
def chat():
    # 获取请求的对话内容
    conversation = request.json["conversation"]
    
    # 对话内容转换为模型输入编码
    inputs_encoded = tokenizer.batch_encode_plus(conversation, padding=True, truncation=True, return_tensors="pt")
    
    # 使用训练好的模型生成对话回复
    outputs_encoded = model.generate(inputs_encoded['input_ids'])
    
    # 对话回复解码为文本
    outputs = tokenizer.batch_decode(outputs_encoded, skip_special_tokens=True)
    
    # 返回对话回复
    return jsonify({"reply": outputs[0]})
  
# 启动Flask应用
if __name__ == "__main__":
    app.run(host='0.0.0.0', port=5000)