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import os
from typing import Generator, Dict, Optional
from openai import OpenAI
from .base import BaseModel
class OpenAIModel(BaseModel):
def __init__(self, api_key, temperature=0.7, system_prompt=None, language=None, api_base_url=None, model_identifier=None):
super().__init__(api_key, temperature, system_prompt, language)
# 设置API基础URL，默认为OpenAI官方API
self.api_base_url = api_base_url
# 允许从外部配置显式指定模型标识符
self.model_identifier = model_identifier or "gpt-4o-2024-11-20"
def get_default_system_prompt(self) -> str:
return """You are an expert at analyzing questions and providing detailed solutions. When presented with an image of a question:
1. First read and understand the question carefully
2. Break down the key components of the question
3. Provide a clear, step-by-step solution
4. If relevant, explain any concepts or theories involved
5. If there are multiple approaches, explain the most efficient one first"""
def get_model_identifier(self) -> str:
return self.model_identifier
def analyze_text(self, text: str, proxies: dict = None) -> Generator[dict, None, None]:
"""Stream GPT-4o's response for text analysis"""
try:
# Initial status
yield {"status": "started", "content": ""}
# Save original environment state
original_env = {
'http_proxy': os.environ.get('http_proxy'),
'https_proxy': os.environ.get('https_proxy')
}
try:
# Set proxy environment variables if provided
if proxies:
if 'http' in proxies:
os.environ['http_proxy'] = proxies['http']
if 'https' in proxies:
os.environ['https_proxy'] = proxies['https']
# Initialize OpenAI client with base_url if provided
if self.api_base_url:
client = OpenAI(api_key=self.api_key, base_url=self.api_base_url)
else:
client = OpenAI(api_key=self.api_key)
# Prepare messages
messages = [
{
"role": "system",
"content": self.system_prompt
},
{
"role": "user",
"content": text
}
]
response = client.chat.completions.create(
model=self.get_model_identifier(),
messages=messages,
temperature=self.temperature,
stream=True,
max_tokens=4000
)
# 使用累积缓冲区
response_buffer = ""
for chunk in response:
if hasattr(chunk.choices[0].delta, 'content'):
content = chunk.choices[0].delta.content
if content:
# 累积内容
response_buffer += content
# 只在累积一定数量的字符或遇到句子结束标记时才发送
if len(content) >= 10 or content.endswith(('.', '!', '?', '。', '！', '？', '\n')):
yield {
"status": "streaming",
"content": response_buffer
}
# 确保发送最终完整内容
if response_buffer:
yield {
"status": "streaming",
"content": response_buffer
}
# Send completion status
yield {
"status": "completed",
"content": response_buffer
}
finally:
# Restore original environment state
for key, value in original_env.items():
if value is None:
if key in os.environ:
del os.environ[key]
else:
os.environ[key] = value
except Exception as e:
yield {
"status": "error",
"error": str(e)
}
def analyze_image(self, image_data: str, proxies: dict = None) -> Generator[dict, None, None]:
"""Stream GPT-4o's response for image analysis"""
try:
# Initial status
yield {"status": "started", "content": ""}
# Save original environment state
original_env = {
'http_proxy': os.environ.get('http_proxy'),
'https_proxy': os.environ.get('https_proxy')
}
try:
# Set proxy environment variables if provided
if proxies:
if 'http' in proxies:
os.environ['http_proxy'] = proxies['http']
if 'https' in proxies:
os.environ['https_proxy'] = proxies['https']
# Initialize OpenAI client with base_url if provided
if self.api_base_url:
client = OpenAI(api_key=self.api_key, base_url=self.api_base_url)
else:
client = OpenAI(api_key=self.api_key)
# 使用系统提供的系统提示词，不再自动添加语言指令
system_prompt = self.system_prompt
# Prepare messages with image
messages = [
{
"role": "system",
"content": system_prompt
},
{
"role": "user",
"content": [
{
"type": "image_url",
"image_url": {
"url": f"data:image/jpeg;base64,{image_data}"
}
},
{
"type": "text",
"text": "Please analyze this image and provide a detailed solution."
}
]
}
]
response = client.chat.completions.create(
model=self.get_model_identifier(),
messages=messages,
temperature=self.temperature,
stream=True,
max_tokens=4000
)
# 使用累积缓冲区
response_buffer = ""
for chunk in response:
if hasattr(chunk.choices[0].delta, 'content'):
content = chunk.choices[0].delta.content
if content:
# 累积内容
response_buffer += content
# 只在累积一定数量的字符或遇到句子结束标记时才发送
if len(content) >= 10 or content.endswith(('.', '!', '?', '。', '！', '？', '\n')):
yield {
"status": "streaming",
"content": response_buffer
}
# 确保发送最终完整内容
if response_buffer:
yield {
"status": "streaming",
"content": response_buffer
}
# Send completion status
yield {
"status": "completed",
"content": response_buffer
}
finally:
# Restore original environment state
for key, value in original_env.items():
if value is None:
if key in os.environ:
del os.environ[key]
else:
os.environ[key] = value
except Exception as e:
yield {
"status": "error",
"error": str(e)
}
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