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websocket_server/image_generator.py

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+import os
+import time
+import json
+import requests
+from dotenv import load_dotenv
+import dashscope
+from dashscope import ImageSynthesis, Generation
+
+# Load environment variables
+load_dotenv()
+dashscope.api_key = os.getenv("DASHSCOPE_API_KEY")
+
+class ImageGenerator:
+    def __init__(self, provider="dashscope", model=None):
+        self.provider = provider
+        self.model = model
+        self.api_key = None
+        
+        if provider == "doubao":
+            self.api_key = os.getenv("volcengine_API_KEY")
+            if not self.model:
+                self.model = "doubao-seedream-5-0-260128" # Default model from user input
+        elif provider == "dashscope":
+            self.api_key = os.getenv("DASHSCOPE_API_KEY")
+            if not self.model:
+                self.model = "wanx2.0-t2i-turbo"
+
+    def optimize_prompt(self, asr_text, progress_callback=None):
+        """Use LLM to optimize the prompt"""
+        print(f"Optimizing prompt for: {asr_text}")
+        
+        if progress_callback:
+            progress_callback(0, "正在准备优化提示词...")
+        
+        system_prompt = """你是一个AI图像提示词优化专家。你的任务是将用户的语音识别结果转化为适合生成"黑白线稿"的提示词。
+关键要求：
+1. 风格必须是：简单的黑白线稿、简笔画、图标风格 (Line art, Sketch, Icon style)。
+2. 画面必须清晰、线条粗壮，适合低分辨率热敏打印机打印。
+3. 绝对不要有复杂的阴影、渐变、黑白线条描述。
+4. 背景必须是纯白 (White background)。
+5. 提示词内容请使用英文描述，因为绘图模型对英文理解更好，但在描述中强调 "black and white line art", "simple lines", "vector style"。
+6. 尺寸比例遵循宽48mm:高30mm (约 1.6:1)。
+7. 直接输出优化后的提示词，不要包含任何解释。
+如果用户要求输入文字，则用```把文字包裹起来，文字是中文
+"black and white line art， Chinese:```中国人```"
+"""
+
+        try:
+            if progress_callback:
+                progress_callback(10, "正在调用AI优化提示词...")
+            
+            # Currently using Qwen-Turbo for all providers for prompt optimization
+            # You can also decouple this if needed
+            response = Generation.call(
+                model='qwen-turbo',
+                prompt=f'{system_prompt}\n\n用户语音识别结果：{asr_text}\n\n优化后的提示词：',
+                max_tokens=200,
+                temperature=0.8
+            )
+            
+            if response.status_code == 200:
+                if hasattr(response, 'output') and response.output and \
+                   hasattr(response.output, 'choices') and response.output.choices and \
+                   len(response.output.choices) > 0:
+                    
+                    optimized = response.output.choices[0].message.content.strip()
+                    print(f"Optimized prompt: {optimized}")
+                    
+                    if progress_callback:
+                        progress_callback(30, f"提示词优化完成: {optimized[:50]}...")
+                    
+                    return optimized
+                elif hasattr(response, 'output') and response.output and hasattr(response.output, 'text'):
+                    optimized = response.output.text.strip()
+                    print(f"Optimized prompt (direct text): {optimized}")
+                    if progress_callback:
+                        progress_callback(30, f"提示词优化完成: {optimized[:50]}...")
+                    return optimized
+                else:
+                    print(f"Prompt optimization response format error: {response}")
+                    if progress_callback:
+                        progress_callback(0, "提示词优化响应格式错误")
+                    return asr_text
+            else:
+                print(f"Prompt optimization failed: {response.code} - {response.message}")
+                if progress_callback:
+                    progress_callback(0, f"提示词优化失败: {response.message}")
+                return asr_text
+                
+        except Exception as e:
+            print(f"Error optimizing prompt: {e}")
+            if progress_callback:
+                progress_callback(0, f"提示词优化出错: {str(e)}")
+            return asr_text
+
+    def generate_image(self, prompt, progress_callback=None):
+        """Generate image based on provider"""
+        if self.provider == "dashscope":
+            return self._generate_dashscope(prompt, progress_callback)
+        elif self.provider == "doubao":
+            return self._generate_doubao(prompt, progress_callback)
+        else:
+            raise ValueError(f"Unknown provider: {self.provider}")
+
+    def _generate_dashscope(self, prompt, progress_callback=None):
+        print(f"Generating image with DashScope for prompt: {prompt}")
+        
+        if progress_callback:
+            progress_callback(35, "正在请求DashScope生成图片...")
+        
+        try:
+            response = ImageSynthesis.call(
+                model=self.model,
+                prompt=prompt,
+                size='1280*720'
+            )
+            
+            if response.status_code == 200:
+                if not response.output:
+                    print("Error: response.output is None")
+                    return None
+                    
+                task_status = response.output.get('task_status')
+                
+                if task_status == 'PENDING' or task_status == 'RUNNING':
+                    print("Waiting for image generation to complete...")
+                    if progress_callback:
+                        progress_callback(45, "AI正在生成图片中...")
+                    
+                    task_id = response.output.get('task_id')
+                    max_wait = 120
+                    waited = 0
+                    while waited < max_wait:
+                        time.sleep(2)
+                        waited += 2
+                        task_result = ImageSynthesis.fetch(task_id)
+                        if task_result.output.task_status == 'SUCCEEDED':
+                            response.output = task_result.output
+                            break
+                        elif task_result.output.task_status == 'FAILED':
+                            error_msg = task_result.output.message if hasattr(task_result.output, 'message') else 'Unknown error'
+                            print(f"Image generation failed: {error_msg}")
+                            if progress_callback:
+                                progress_callback(35, f"图片生成失败: {error_msg}")
+                            return None
+                
+                if response.output.get('task_status') == 'SUCCEEDED':
+                    image_url = response.output['results'][0]['url']
+                    print(f"Image generated, url: {image_url}")
+                    return image_url
+            else:
+                error_msg = f"{response.code} - {response.message}"
+                print(f"Image generation failed: {error_msg}")
+                if progress_callback:
+                    progress_callback(35, f"图片生成失败: {error_msg}")
+                return None
+                
+        except Exception as e:
+            print(f"Error generating image: {e}")
+            if progress_callback:
+                progress_callback(35, f"图片生成出错: {str(e)}")
+            return None
+
+    def _generate_doubao(self, prompt, progress_callback=None):
+        print(f"Generating image with Doubao for prompt: {prompt}")
+        
+        if progress_callback:
+            progress_callback(35, "正在请求豆包生成图片...")
+            
+        url = "https://ark.cn-beijing.volces.com/api/v3/images/generations"
+        headers = {
+            "Content-Type": "application/json",
+            "Authorization": f"Bearer {self.api_key}"
+        }
+        data = {
+            "model": self.model,
+            "prompt": prompt,
+            "sequential_image_generation": "disabled",
+            "response_format": "url",
+            "size": "2K", # Doubao supports different sizes, user example used 2K. But we might want something smaller if possible to save bandwidth/time? 
+                          # User's curl says "2K". I will stick to it or maybe check docs.
+                          # Actually for thermal printer, we don't need 2K. But let's follow user example.
+            "stream": False,
+            "watermark": True
+        }
+        
+        try:
+            response = requests.post(url, headers=headers, json=data, timeout=60)
+            
+            if response.status_code == 200:
+                result = response.json()
+                # Check format of result
+                # Typically OpenAI compatible or similar
+                # User example didn't show response format, but usually it's "data": [{"url": "..."}]
+                
+                if "data" in result and len(result["data"]) > 0:
+                    image_url = result["data"][0]["url"]
+                    print(f"Image generated, url: {image_url}")
+                    return image_url
+                elif "error" in result:
+                     error_msg = result["error"].get("message", "Unknown error")
+                     print(f"Doubao API error: {error_msg}")
+                     if progress_callback:
+                         progress_callback(35, f"图片生成失败: {error_msg}")
+                     return None
+                else:
+                    print(f"Unexpected response format: {result}")
+                    return None
+            else:
+                print(f"Doubao API failed with status {response.status_code}: {response.text}")
+                if progress_callback:
+                    progress_callback(35, f"图片生成失败: {response.status_code}")
+                return None
+                
+        except Exception as e:
+            print(f"Error calling Doubao API: {e}")
+            if progress_callback:
+                progress_callback(35, f"图片生成出错: {str(e)}")
+            return None