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代码说明：
auto_live_stream.py 实现视频循环推流功能，支持RTMP协议推送到直播平台
ai_virtual_host.py 实现基于BERT的AI虚拟主播交互系统
需要安装opencv, ffmpeg, transformers等依赖库
实际使用时需要申请各直播平台的推流地址

import cv2
import numpy as np
import subprocess
import time
from datetime import datetime
import os

class AutoLiveStream:
def init(self, video_path, rtmp_url):
self.video_path = video_path
self.rtmp_url = rtmp_url
self.fps = 30
self.width = 720
self.height = 1280
self.running = False

def get_video_info(self):
    cap = cv2.VideoCapture(self.video_path)
    self.fps = int(cap.get(cv2.CAP_PROP_FPS))
    self.width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
    self.height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
    cap.release()

def generate_overlay(self, frame):
    now = datetime.now()
    timestamp = now.strftime("%Y-%m-%d %H:%M:%S")
    cv2.putText(frame, timestamp, (50, 100), 
               cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
    return frame

def start_stream(self):
    self.get_video_info()
    command = [
        'ffmpeg',
        '-re',
        '-i', self.video_path,
        '-vf', f'fps={self.fps},scale={self.width}:{self.height}',
        '-c:v', 'libx264',
        '-preset', 'ultrafast',
        '-tune', 'zerolatency',
        '-pix_fmt', 'yuv420p',
        '-f', 'flv',
        self.rtmp_url
    ]

    self.process = subprocess.Popen(command)
    self.running = True
    print(f"开始推流到 {self.rtmp_url}")

def stop_stream(self):
    if self.running:
        self.process.terminate()
        self.running = False
        print("推流已停止")

def run_loop(self):
    cap = cv2.VideoCapture(self.video_path)
    while self.running:
        ret, frame = cap.read()
        if not ret:
            cap.set(cv2.CAP_PROP_POS_FRAMES, 0)
            continue

        frame = self.generate_overlay(frame)
        cv2.imshow('Live Preview', frame)
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break

    cap.release()
    cv2.destroyAllWindows()

if name == "main":

# 配置参数
VIDEO_FILE = "demo.mp4"
RTMP_URL = "rtmp://your-stream-server/app/stream-key"

streamer = AutoLiveStream(VIDEO_FILE, RTMP_URL)
try:
    streamer.start_stream()
    streamer.run_loop()
except KeyboardInterrupt:
    pass
finally:
    streamer.stop_stream()

import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
import speech_recognition as sr
import pyttsx3
import threading

class AIVirtualHost:
def init(self):
self.model_name = "bert-base-chinese"
self.tokenizer = AutoTokenizer.from_pretrained(self.model_name)
self.model = AutoModelForSeq2SeqLM.from_pretrained(self.model_name)
self.recognizer = sr.Recognizer()
self.engine = pyttsx3.init()
self.is_running = False

def listen(self):
    with sr.Microphone() as source:
        print("AI主播正在聆听...")
        audio = self.recognizer.listen(source)

    try:
        text = self.recognizer.recognize_google(audio, language="zh-CN")
        return text
    except Exception as e:
        print(f"识别错误: {e}")
        return None

def generate_response(self, input_text):
    inputs = self.tokenizer(input_text, return_tensors="pt")
    outputs = self.model.generate(**inputs)
    return self.tokenizer.decode(outputs[0], skip_special_tokens=True)

def speak(self, text):
    self.engine.say(text)
    self.engine.runAndWait()

def run_interactive(self):
    self.is_running = True
    while self.is_running:
        user_input = self.listen()
        if user_input:
            response = self.generate_response(user_input)
            print(f"AI主播回复: {response}")
            self.speak(response)

def start(self):
    thread = threading.Thread(target=self.run_interactive)
    thread.start()

def stop(self):
    self.is_running = False

if name == "main":
host = AIVirtualHost()
print("AI虚拟主播已启动，按Ctrl+C退出")
try:
host.start()
while True:
time.sleep(1)
except KeyboardInterrupt:
host.stop()