inference/deepseek_service.py

"""
DeepSeek API Service
Used to optimize and organize SkinGPT model output results
"""

import os
import re
from typing import Optional
from openai import AsyncOpenAI


class DeepSeekService:
    """DeepSeek API Service Class"""
    
    def __init__(self, api_key: Optional[str] = None):
        """
        Initialize DeepSeek service
        
        Parameters:
            api_key: DeepSeek API key, reads from environment variable if not provided
        """
        self.api_key = api_key or os.environ.get("DEEPSEEK_API_KEY")
        self.base_url = "https://api.deepseek.com"
        self.model = "deepseek-chat"  # Using deepseek-chat model
        
        self.client = None
        self.is_loaded = False
        
        print(f"DeepSeek API service initializing...")
        print(f"API Base URL: {self.base_url}")
    
    async def load(self):
        """Initialize DeepSeek API client"""
        try:
            if not self.api_key:
                print("DeepSeek API key not provided")
                self.is_loaded = False
                return
            
            # Initialize OpenAI compatible client
            self.client = AsyncOpenAI(
                api_key=self.api_key,
                base_url=self.base_url
            )
            
            self.is_loaded = True
            print("DeepSeek API service is ready!")
            
        except Exception as e:
            print(f"DeepSeek API service initialization failed: {e}")
            self.is_loaded = False
    
    async def refine_diagnosis(
        self, 
        raw_answer: str,
        raw_thinking: Optional[str] = None,
        language: str = "zh"
    ) -> dict:
        """
        Use DeepSeek API to optimize and organize diagnosis results
        
        Parameters:
            raw_answer: Original diagnosis result
            raw_thinking: AI thinking process
            language: Language option
        
        Returns:
            Dictionary containing "description", "analysis_process" and "diagnosis_result"
        """
        
        if not self.is_loaded or self.client is None:
            error_msg = "API not initialized, cannot generate analysis" if language == "en" else "API未初始化，无法生成分析过程"
            print("DeepSeek API not initialized, returning original result")
            return {
                "success": False,
                "description": "",
                "analysis_process": raw_thinking or error_msg,
                "diagnosis_result": raw_answer,
                "original_diagnosis": raw_answer,
                "error": "DeepSeek API not initialized"
            }
        
        try:
            # Build prompt
            prompt = self._build_refine_prompt(raw_answer, raw_thinking, language)
            
            # Select system prompt based on language
            if language == "en":
                system_content = "You are a professional medical text editor. Your task is to polish and organize medical diagnostic text to make it flow smoothly while preserving the original meaning. Output ONLY the formatted result. Do NOT add any explanations, comments, or thoughts. Just follow the format exactly."
            else:
                system_content = "你是医学文本整理专家，按照用户要求将用户输入的文本整理成用户想要的格式，不要改写或总结。"
            
            # Call DeepSeek API
            response = await self.client.chat.completions.create(
                model=self.model,
                messages=[
                    {"role": "system", "content": system_content},
                    {"role": "user", "content": prompt}
                ],
                temperature=0.1,
                max_tokens=2048,
                top_p=0.8,
            )
            
            # Extract generated text
            generated_text = response.choices[0].message.content
            
            # Parse output
            parsed = self._parse_refined_output(generated_text, raw_answer, raw_thinking, language)
            
            return {
                "success": True,
                "description": parsed["description"],
                "analysis_process": parsed["analysis_process"],
                "diagnosis_result": parsed["diagnosis_result"],
                "original_diagnosis": raw_answer,
                "raw_refined": generated_text
            }
            
        except Exception as e:
            print(f"DeepSeek API call failed: {e}")
            error_msg = "API call failed, cannot generate analysis" if language == "en" else "API调用失败，无法生成分析过程"
            return {
                "success": False,
                "description": "",
                "analysis_process": raw_thinking or error_msg,
                "diagnosis_result": raw_answer,
                "original_diagnosis": raw_answer,
                "error": str(e)
            }
    
    def _build_refine_prompt(self, raw_answer: str, raw_thinking: Optional[str] = None, language: str = "zh") -> str:
        """
        Build optimization prompt
        
        Parameters:
            raw_answer: Original diagnosis result
            raw_thinking: AI thinking process
            language: Language option, "zh" for Chinese, "en" for English
        
        Returns:
            Built prompt
        """
        if language == "en":
            # English prompt - organize and polish while preserving meaning
            thinking_text = raw_thinking if raw_thinking else "No analysis process available."
            prompt = f"""You are a text organization expert. There are two texts that need to be organized. Text 1 is the thinking process of the SkinGPT model, and Text 2 is the diagnosis result given by SkinGPT.

【Requirements】
- Preserve the original tone and expression style
- Text 1 contains the thinking process, Text 2 contains the diagnosis result
- Extract the image observation part from the thinking process as Description. This should include all factual observations about what was seen in the image, not just a brief summary.
- For Diagnostic Reasoning: refine and condense the remaining thinking content. Remove redundancies, self-doubt, circular reasoning, and unnecessary repetition. Keep it concise and not too long. Keep the logical chain clear and enhance readability. IMPORTANT: DO NOT include any image description or visual observations in Diagnostic Reasoning. Only include reasoning, analysis, and diagnostic thought process.
- If [Text 1] content is NOT: No analysis process available. Then organize [Text 1] content accordingly, DO NOT confuse [Text 1] and [Text 2]
- If [Text 1] content IS: No analysis process available. Then extract the analysis process and description from [Text 2]
- DO NOT infer or add new medical information, DO NOT output any meta-commentary
- You may adjust unreasonable statements or remove redundant content to improve clarity

[Text 1]
{thinking_text}

[Text 2]
{raw_answer}

【Output】Only output three sections, do not output anything else:
## Description
(Extract all image observation content from the thinking process - include all factual descriptions of what was seen)

## Analysis Process
(Refined and condensed diagnostic reasoning: remove self-doubt, circular logic, and redundancies. Keep it concise and not too long. Keep logical flow clear. Do NOT include image observations)

## Diagnosis Result
(The organized diagnosis result from Text 2)

【Example】:
## Description
The image shows red inflamed patches on the skin with pustules and darker colored spots. The lesions appear as papules and pustules distributed across the affected area, with some showing signs of inflammation and possible post-inflammatory hyperpigmentation.

## Analysis Process
These findings are consistent with acne vulgaris, commonly seen during adolescence. The user's age aligns with typical onset for this condition. Treatment recommendations: over-the-counter medications such as benzoyl peroxide or topical antibiotics, avoiding picking at the skin, and consulting a dermatologist if severe. The goal is to control inflammation and prevent scarring.

## Diagnosis Result
Possible diagnosis: Acne (pimples) Explanation: Acne is a common skin condition, especially during adolescence, when hormonal changes cause overactive sebaceous glands, which can easily clog pores and form acne. Pathological care recommendations: 1. Keep face clean, wash face 2-3 times daily, use gentle cleansing products. 2. Avoid squeezing acne with hands to prevent worsening inflammation or leaving scars. 3. Avoid using irritating cosmetics and skincare products. 4. Can use topical medications containing salicylic acid, benzoyl peroxide, etc. 5. If necessary, can use oral antibiotics or other treatment methods under doctor's guidance. Precautions: 1. Avoid rubbing or damaging the affected area to prevent infection. 2. Eat less oily and spicy foods, eat more vegetables and fruits. 3. Maintain good rest habits, avoid staying up late. 4. If acne symptoms persist without improvement or show signs of worsening, seek medical attention promptly.
"""
        else:
            # Chinese prompt - translate to Simplified Chinese AND organize/polish
            thinking_text = raw_thinking if raw_thinking else "No analysis process available."
            prompt = f"""你是一个文本整理专家。有两段文本需要整理，文本1是SkinGPT模型的思考过程的文本，文本2是SkinGPT给出的诊断结果的文本。

【要求】
- 保留原文的语气和表达方式
- 文本1是思考过程，文本2是诊断结果
- 从思考过程中提取图像观察部分作为图像描述。需要包含所有关于图片中观察到的事实内容，不要简化或缩短。
- 对于分析过程：提炼并精简剩余的思考内容，去除冗余、自我怀疑、兜圈子的内容。保持简洁，不要太长。保持逻辑链条清晰，增强可读性。重要：分析过程中不要包含任何图像描述或视觉观察内容，只包含推理、分析和诊断思考过程。
- 如果【文本1】内容不是：No analysis process available.那么按要求整理【文本1】的内容，不要混淆【文本1】和【文本2】。
- 如果【文本1】内容是：No analysis process available.那么从【文本2】提炼分析过程和描述。
- 【文本1】和【文本2】需要翻译成简体中文
- 禁止推断或添加新的医学信息，禁止输出任何元评论
- 可以调整不合理的语句或去除冗余内容以提高清晰度


【文本1】
{thinking_text}

【文本2】
{raw_answer}

【输出】只输出三个部分，不要输出其他任何内容：
## 图像描述
（从思考过程中提取所有图像观察内容，包含所有关于图片的事实描述）

## 分析过程
（提炼并精简后的诊断推理：去除自我怀疑、兜圈逻辑和冗余内容。保持简洁，不要太长。保持逻辑流畅。不包含图像观察）

## 诊断结果
（整理后的诊断结果）

【样例】:
## 图像描述
图片显示皮肤上有红色发炎的斑块，伴有脓疱和颜色较深的斑点。病变表现为分布在受影响区域的丘疹和脓疱，部分显示出炎症迹象和可能的炎症后色素沉着。

## 分析过程
这些表现符合寻常痤疮的特征，青春期常见。用户的年龄与该病症的典型发病年龄相符。治疗建议：使用非处方药物如过氧化苯甲酰或外用抗生素，避免抠抓皮肤，病情严重时咨询皮肤科医生。目标是控制炎症并防止疤痕形成。

## 诊断结果
可能的诊断：痤疮（青春痘） 解释：痤疮是一种常见的皮肤病，特别是在青少年期间，由于激素水平的变化导致皮脂腺过度活跃，容易堵塞毛孔，形成痤疮。 病理护理建议：1.保持面部清洁，每天洗脸2-3次，使用温和的洁面产品。 2.避免用手挤压痤疮，以免加重炎症或留下疤痕。 3.避免使用刺激性的化妆品和护肤品。 4.可以使用含有水杨酸、苯氧醇等成分的外用药物治疗。 5.如有需要，可以在医生指导下使用抗生素口服药或其他治疗方法。 注意事项：1. 避免摩擦或损伤患处，以免引起感染。 2. 饮食上应少吃油腻、辛辣食物，多吃蔬菜水果。 3. 保持良好的作息习惯，避免熬夜。 4. 如果痤疮症状持续不见好转或有恶化的趋势，应及时就医。
"""
        
        return prompt
    
    def _parse_refined_output(
        self, 
        generated_text: str, 
        raw_answer: str,
        raw_thinking: Optional[str] = None,
        language: str = "zh"
    ) -> dict:
        """
        Parse DeepSeek generated output
        
        Parameters:
            generated_text: DeepSeek generated text
            raw_answer: Original diagnosis (as fallback)
            raw_thinking: Original thinking process (as fallback)
            language: Language option
        
        Returns:
            Dictionary containing description, analysis_process and diagnosis_result
        """
        description = ""
        analysis_process = None
        diagnosis_result = None
        
        if language == "en":
            # English patterns
            desc_match = re.search(
                r'##\s*Description\s*\n([\s\S]*?)(?=##\s*Analysis\s*Process|$)',
                generated_text,
                re.IGNORECASE
            )
            analysis_match = re.search(
                r'##\s*Analysis\s*Process\s*\n([\s\S]*?)(?=##\s*Diagnosis\s*Result|$)',
                generated_text,
                re.IGNORECASE
            )
            result_match = re.search(
                r'##\s*Diagnosis\s*Result\s*\n([\s\S]*?)$',
                generated_text,
                re.IGNORECASE
            )
            
            desc_header = "## Description"
            analysis_header = "## Analysis Process"
            result_header = "## Diagnosis Result"
        else:
            # Chinese patterns
            desc_match = re.search(
                r'##\s*图像描述\s*\n([\s\S]*?)(?=##\s*分析过程|$)',
                generated_text
            )
            analysis_match = re.search(
                r'##\s*分析过程\s*\n([\s\S]*?)(?=##\s*诊断结果|$)',
                generated_text
            )
            result_match = re.search(
                r'##\s*诊断结果\s*\n([\s\S]*?)$',
                generated_text
            )
            
            desc_header = "## 图像描述"
            analysis_header = "## 分析过程"
            result_header = "## 诊断结果"
        
        # Extract description
        if desc_match:
            description = desc_match.group(1).strip()
            print(f"Successfully parsed description")
        else:
            print(f"Description parsing failed")
            description = ""
        
        # Extract analysis process
        if analysis_match:
            analysis_process = analysis_match.group(1).strip()
            print(f"Successfully parsed analysis process")
        else:
            print(f"Analysis process parsing failed, trying other methods")
            # Try to extract from generated text
            result_pos = generated_text.find(result_header)
            if result_pos > 0:
                # Get content before diagnosis result
                analysis_process = generated_text[:result_pos].strip()
                # Remove possible headers
                for header in [desc_header, analysis_header]:
                    header_escaped = re.escape(header)
                    analysis_process = re.sub(f'{header_escaped}\\s*\\n?', '', analysis_process).strip()
            else:
                # If no format at all, try to get first half
                mid_point = len(generated_text) // 2
                analysis_process = generated_text[:mid_point].strip()
            
            # If still empty, use original content (final fallback)
            if not analysis_process and raw_thinking:
                print(f"Using original raw_thinking as fallback")
                analysis_process = raw_thinking
        
        # Extract diagnosis result
        if result_match:
            diagnosis_result = result_match.group(1).strip()
            print(f"Successfully parsed diagnosis result")
        else:
            print(f"Diagnosis result parsing failed, trying other methods")
            # Try to extract from generated text
            result_pos = generated_text.find(result_header)
            if result_pos > 0:
                diagnosis_result = generated_text[result_pos:].strip()
                # Remove possible header
                result_header_escaped = re.escape(result_header)
                diagnosis_result = re.sub(f'^{result_header_escaped}\\s*\\n?', '', diagnosis_result).strip()
            else:
                # If no format at all, get second half
                mid_point = len(generated_text) // 2
                diagnosis_result = generated_text[mid_point:].strip()
            
            # If still empty, use original content (final fallback)
            if not diagnosis_result:
                print(f"Using original raw_answer as fallback")
                diagnosis_result = raw_answer
        
        return {
            "description": description,
            "analysis_process": analysis_process,
            "diagnosis_result": diagnosis_result
        }


# Global DeepSeek service instance (lazy loading)
_deepseek_service: Optional[DeepSeekService] = None


async def get_deepseek_service(api_key: Optional[str] = None) -> Optional[DeepSeekService]:
    """
    Get DeepSeek service instance (singleton pattern)
    
    Parameters:
        api_key: Optional API key to use
    
    Returns:
        DeepSeekService instance, or None if API initialization fails
    """
    global _deepseek_service
    
    if _deepseek_service is None:
        try:
            _deepseek_service = DeepSeekService(api_key=api_key)
            await _deepseek_service.load()
            if not _deepseek_service.is_loaded:
                print("DeepSeek API service initialization failed, will use fallback mode")
                return _deepseek_service  # Return instance but marked as not loaded
        except Exception as e:
            print(f"DeepSeek service initialization failed: {e}")
            return None
    
    return _deepseek_service