AI创作平台对比：百度千帆、阿里百炼、字节豆包等大模型平台评测

# 百度千帆API调用示例 - 剧本生成
import requests
import json

API_KEY = "your_api_key"
SECRET_KEY = "your_secret_key"

def get_access_token():
    url = f"https://aip.baidubce.com/oauth/2.0/token?grant_type=client_credentials&client_id={API_KEY}&client_secret={SECRET_KEY}"
    response = requests.post(url)
    return response.json().get("access_token")

def generate_script(access_token, topic):
    url = f"https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/ernie-4.5-8k?access_token={access_token}"
    payload = {
        "messages": [
            {"role": "user", "content": f"请为以下题材创作一个AI漫剧剧本大纲：{topic}。要求包含5个角色、10个场景、主线剧情和支线剧情。"}
        ],
        "temperature": 0.7,
        "top_p": 0.9
    }
    response = requests.post(url, json=payload)
    return response.json()

# 使用示例
token = get_access_token()
result = generate_script(token, "都市悬疑，一个程序员发现AI系统有了自我意识")
print(result.get("result", ""))

# 阿里百炼API调用示例 - 分镜描述生成
import openai

client = openai.OpenAI(
    api_key="your_api_key",
    base_url="https://dashscope.aliyuncs.com/compatible-mode/v1"
)

def generate_storyboard(scene_description):
    response = client.chat.completions.create(
        model="qwen-max",
        messages=[
            {"role": "system", "content": "你是一个专业的AI漫剧分镜师。根据场景描述，生成详细的画面描述，用于AI绘画工具生成图片。"},
            {"role": "user", "content": f"场景描述：{scene_description}\n\n请生成详细的画面描述（Prompt），包括：构图、人物姿态、表情、服装、场景细节、光影效果、画风要求。"}
        ],
        temperature=0.8,
        max_tokens=2000
    )
    return response.choices[0].message.content

# 使用示例
prompt = generate_storyboard("夜晚的城市天台，女主角独自站立，望着远处的霓虹灯，表情忧郁，微风吹动她的长发")
print(prompt)

# 字节豆包API调用示例 - 角色对话生成
import requests
import json

def generate_character_dialogue(api_key, character_profiles, scene_context):
    url = "https://ark.cn-beijing.volces.com/api/v3/chat/completions"
    headers = {
        "Content-Type": "application/json",
        "Authorization": f"Bearer {api_key}"
    }
    payload = {
        "model": "doubao-pro-32k",
        "messages": [
            {"role": "system", "content": f"角色设定：\n{character_profiles}\n\n请根据角色性格和场景，生成自然流畅的对话。"},
            {"role": "user", "content": f"场景：{scene_context}\n\n请生成3-5轮角色对话。"}
        ],
        "temperature": 0.9,
        "max_tokens": 1500
    }
    response = requests.post(url, headers=headers, json=payload)
    return response.json()

# 使用示例
profiles = """
角色A：林晓，25岁女程序员，性格冷静理性，说话简洁直接
角色B：陈默，28岁AI研究员，性格温和但有些神经质，喜欢用比喻
"""
context = "深夜实验室，林晓发现AI系统出现了异常行为，陈默正在调试代码"
result = generate_character_dialogue("your_api_key", profiles, context)
print(result["choices"][0]["message"]["content"])

剧本创作 → 百度千帆（ERNIE 4.5，文学性强）
    ↓
分镜描述 → 阿里百炼（Qwen-Max，多语言Prompt）
    ↓
角色对话 → 字节豆包（低成本，大量调用）
    ↓
文本校对 → 智谱GLM（逻辑检查，一致性维护）
    ↓
脚本自动化 → DeepSeek（代码生成，工作流开发）

"""
AI漫剧多平台LLM调用框架
支持百度千帆、阿里百炼、字节豆包、智谱GLM、Kimi、DeepSeek
"""

import requests
import json
from abc import ABC, abstractmethod
from typing import List, Dict, Optional

class LLMProvider(ABC):
    """大模型平台抽象基类"""

    @abstractmethod
    def chat(self, messages: List[Dict], temperature: float = 0.7,
             max_tokens: int = 2000) -> str:
        pass

    @abstractmethod
    def generate_script(self, topic: str, episodes: int = 10) -> str:
        pass

    @abstractmethod
    def generate_storyboard(self, scene: str) -> str:
        pass

    @abstractmethod
    def generate_dialogue(self, characters: str, context: str) -> str:
        pass


class BaiduQianfan(LLMProvider):
    """百度千帆平台"""

    def __init__(self, api_key: str, secret_key: str):
        self.api_key = api_key
        self.secret_key = secret_key
        self.token_url = "https://aip.baidubce.com/oauth/2.0/token"
        self.chat_url = "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/ernie-4.5-8k"

    def _get_token(self) -> str:
        params = {
            "grant_type": "client_credentials",
            "client_id": self.api_key,
            "client_secret": self.secret_key
        }
        resp = requests.post(self.token_url, params=params)
        return resp.json()["access_token"]

    def chat(self, messages, temperature=0.7, max_tokens=2000):
        token = self._get_token()
        url = f"{self.chat_url}?access_token={token}"
        payload = {"messages": messages, "temperature": temperature}
        resp = requests.post(url, json=payload)
        return resp.json().get("result", "")

    def generate_script(self, topic, episodes=10):
        messages = [
            {"role": "user", "content": f"请为以下题材创作{episodes}集AI漫剧剧本大纲：{topic}"}
        ]
        return self.chat(messages, temperature=0.8, max_tokens=4000)

    def generate_storyboard(self, scene):
        messages = [
            {"role": "user", "content": f"将以下场景描述转化为AI绘画Prompt：{scene}"}
        ]
        return self.chat(messages, temperature=0.7, max_tokens=1000)

    def generate_dialogue(self, characters, context):
        messages = [
            {"role": "user", "content": f"角色设定：{characters}\n场景：{context}\n请生成角色对话"}
        ]
        return self.chat(messages, temperature=0.9, max_tokens=2000)


class AliBailian(LLMProvider):
    """阿里百炼平台（兼容OpenAI格式）"""

    def __init__(self, api_key: str):
        import openai
        self.client = openai.OpenAI(
            api_key=api_key,
            base_url="https://dashscope.aliyuncs.com/compatible-mode/v1"
        )

    def chat(self, messages, temperature=0.7, max_tokens=2000):
        response = self.client.chat.completions.create(
            model="qwen-max",
            messages=messages,
            temperature=temperature,
            max_tokens=max_tokens
        )
        return response.choices[0].message.content

    def generate_script(self, topic, episodes=10):
        messages = [{"role": "user", "content": f"创作{episodes}集AI漫剧剧本大纲：{topic}"}]
        return self.chat(messages, temperature=0.8, max_tokens=4000)

    def generate_storyboard(self, scene):
        messages = [{"role": "user", "content": f"转化为AI绘画Prompt：{scene}"}]
        return self.chat(messages, temperature=0.7, max_tokens=1000)

    def generate_dialogue(self, characters, context):
        messages = [{"role": "user", "content": f"角色：{characters}\n场景：{context}\n生成对话"}]
        return self.chat(messages, temperature=0.9, max_tokens=2000)


class DoubaoProvider(LLMProvider):
    """字节豆包平台"""

    def __init__(self, api_key: str, endpoint_id: str):
        self.api_key = api_key
        self.endpoint_id = endpoint_id
        self.url = "https://ark.cn-beijing.volces.com/api/v3/chat/completions"

    def chat(self, messages, temperature=0.7, max_tokens=2000):
        headers = {
            "Content-Type": "application/json",
            "Authorization": f"Bearer {self.api_key}"
        }
        payload = {
            "model": self.endpoint_id,
            "messages": messages,
            "temperature": temperature,
            "max_tokens": max_tokens
        }
        resp = requests.post(self.url, headers=headers, json=payload)
        return resp.json()["choices"][0]["message"]["content"]

    def generate_script(self, topic, episodes=10):
        messages = [{"role": "user", "content": f"创作{episodes}集AI漫剧剧本大纲：{topic}"}]
        return self.chat(messages, temperature=0.8, max_tokens=4000)

    def generate_storyboard(self, scene):
        messages = [{"role": "user", "content": f"转化为AI绘画Prompt：{scene}"}]
        return self.chat(messages, temperature=0.7, max_tokens=1000)

    def generate_dialogue(self, characters, context):
        messages = [{"role": "user", "content": f"角色：{characters}\n场景：{context}\n生成对话"}]
        return self.chat(messages, temperature=0.9, max_tokens=2000)


# 使用示例
if __name__ == "__main__":
    # 选择平台（可随时切换）
    provider = AliBailian(api_key="your_api_key")
    # provider = BaiduQianfan(api_key="xxx", secret_key="xxx")
    # provider = DoubaoProvider(api_key="xxx", endpoint_id="xxx")

    # 生成剧本
    script = provider.generate_script("都市悬疑，程序员发现AI有了自我意识", episodes=5)
    print("=== 剧本大纲 ===")
    print(script)

    # 生成分镜
    storyboard = provider.generate_storyboard("夜晚天台，女主角独自站立，望着远处霓虹灯")
    print("\n=== 分镜描述 ===")
    print(storyboard)