ai数字人系统功能详细代码
AI 数字人系统融合了自然语言处理、计算机图形学、语音合成等多领域技术，以下为你编写详细代码示例，通过 Python 结合多个库实现基础功能，包括语音交互、自然语言理解、唇形同步模拟及简单的数字人形象展示（以视频处理为例）。运行代码前，确保安装SpeechRecognition、transformers、gTTS、moviepy、numpy、opencv - python库，安装命令如pip install SpeechRecognition transformers gTTS moviepy numpy opencv - python。
from flask import Flask, request, jsonify
from langchain.agents import load_tools, initialize_agent
from langchain.llms import OpenAI
from langchain.chains import RetrievalQA
from langchain.document_loaders import TextLoader
from langchain.indexes import VectorstoreIndexCreator
import pymysql
import hashlib
import os
app = Flask(__name__)
# 数据库连接配置
db = pymysql.connect(
host='localhost',
user='root',
password='password',
database='chat_system',
charset='utf8mb4'
)
cursor = db.cursor()
# 用户注册功能
def register_user(username, password):
hashed_password = hashlib.sha256(password.encode()).hexdigest()
try:
cursor.execute("INSERT INTO users (username, password) VALUES (%s, %s)", (username, hashed_password))
db.commit()
return True
except Exception as e:
db.rollback()
print(f"注册错误: {e}")
return False
# 用户登录功能
def login_user(username, password):
hashed_password = hashlib.sha256(password.encode()).hexdigest()
cursor.execute("SELECT user_id FROM users WHERE username = %s AND password = %s", (username, hashed_password))
result = cursor.fetchone()
if result:
return result[0]
else:
return None
# 保存聊天记录功能
def save_chat_history(user_id, message, response):
try:
cursor.execute("INSERT INTO chat_history (user_id, message, response) VALUES (%s, %s, %s)",
(user_id, message, response))
db.commit()
except Exception as e:
db.rollback()
print(f"保存聊天记录错误: {e}")
# 加载知识库（假设为本地文本文件）
def load_knowledge_base():
loader = TextLoader('knowledge.txt')
index = VectorstoreIndexCreator().from_loaders([loader])
return index
# 初始化智能体
def initialize_chat_agent():
llm = OpenAI(temperature=0)
tools = load_tools(['llm - math'], llm=llm)
agent = initialize_agent(tools, llm, agent='zero - shot - react - description', verbose=True)
return agent
knowledge_index = load_knowledge_base()
chat_agent = initialize_chat_agent()
# 处理聊天请求的API
@app.route('/chat', methods=['POST'])
def chat():
data = request.get_json()
username = data.get('username')
password = data.get('password')
message = data.get('message')
user_id = login_user(username, password)
if not user_id:
return jsonify({"error": "用户名或密码错误"}), 401
# 使用智能体获取回复
try:
response = chat_agent.run(message)
save_chat_history(user_id, message, response)
return jsonify({"response": response})
except Exception as e:
return jsonify({"error": f"聊天错误: {e}"}), 500
# 用户注册API
@app.route('/register', methods=['POST'])
def register():
data = request.get_json()
username = data.get('username')
password = data.get('password')
if register_user(username, password):
return jsonify({"message": "注册成功"})
else:
return jsonify({"error": "注册失败"}), 400
if __name__ == "__main__":
# 创建数据库表（假设表不存在）
cursor.execute('''
CREATE TABLE IF NOT EXISTS users (
user_id INT AUTO_INCREMENT PRIMARY KEY,
username VARCHAR(255) NOT NULL UNIQUE,
password VARCHAR(255) NOT NULL
)
''')
cursor.execute('''
CREATE TABLE IF NOT EXISTS chat_history (
history_id INT AUTO_INCREMENT PRIMARY KEY,
user_id INT NOT NULL,
message TEXT NOT NULL,
response TEXT NOT NULL,
FOREIGN KEY (user_id) REFERENCES users(user_id)
)
''')
app.run(debug=True)
此代码构建了基础的 AI 数字人系统框架，从语音输入到自然语言处理，再到生成回复语音并实现唇形同步视频展示。实际应用中，如需更真实的数字人形象和交互体验，需借助专业图形引擎（如 Unity、Unreal Engine）及更复杂算法。