Add system prompt, glossary, presets for Ollama/WebLLM, image translation support

services/translation_service.py

@@ -70,30 +70,65 @@ class LibreTranslationProvider(TranslationProvider):
 class OllamaTranslationProvider(TranslationProvider):
     """Ollama LLM translation implementation"""
     
-    def __init__(self, base_url: str = "http://localhost:11434", model: str = "llama3", vision_model: str = "llava"):
+    def __init__(self, base_url: str = "http://localhost:11434", model: str = "llama3", vision_model: str = "llava", system_prompt: str = ""):
         self.base_url = base_url.rstrip('/')
-        self.model = model
-        self.vision_model = vision_model
+        self.model = model.strip()  # Remove any leading/trailing whitespace
+        self.vision_model = vision_model.strip()
+        self.custom_system_prompt = system_prompt  # Custom context, glossary, instructions
     
     def translate(self, text: str, target_language: str, source_language: str = 'auto') -> str:
         if not text or not text.strip():
             return text
         
+        # Skip very short text or numbers only
+        if len(text.strip()) < 2 or text.strip().isdigit():
+            return text
+        
         try:
-            prompt = f"Translate the following text to {target_language}. Return ONLY the translation, nothing else:\n\n{text}"
+            # Build system prompt with custom context if provided
+            base_prompt = f"You are a translator. Translate the user's text to {target_language}. Return ONLY the translation, nothing else."
             
+            if self.custom_system_prompt:
+                system_content = f"""{base_prompt}
+
+ADDITIONAL CONTEXT AND INSTRUCTIONS:
+{self.custom_system_prompt}"""
+            else:
+                system_content = base_prompt
+            
+            # Use /api/chat endpoint (more compatible with all models)
             response = requests.post(
-                f"{self.base_url}/api/generate",
+                f"{self.base_url}/api/chat",
                 json={
                     "model": self.model,
-                    "prompt": prompt,
-                    "stream": False
+                    "messages": [
+                        {
+                            "role": "system",
+                            "content": system_content
+                        },
+                        {
+                            "role": "user",
+                            "content": text
+                        }
+                    ],
+                    "stream": False,
+                    "options": {
+                        "temperature": 0.3,
+                        "num_predict": 500
+                    }
                 },
-                timeout=30
+                timeout=120  # 2 minutes timeout
             )
             response.raise_for_status()
             result = response.json()
-            return result.get("response", text).strip()
+            translated = result.get("message", {}).get("content", "").strip()
+            return translated if translated else text
+        except requests.exceptions.ConnectionError:
+            print(f"Ollama error: Cannot connect to {self.base_url}. Is Ollama running?")
+            return text
+        except requests.exceptions.Timeout:
+            print(f"Ollama error: Request timeout after 120s")
+            return text
         except Exception as e:
             print(f"Ollama translation error: {e}")
             return text
@@ -107,21 +142,25 @@ class OllamaTranslationProvider(TranslationProvider):
             with open(image_path, 'rb') as img_file:
                 image_data = base64.b64encode(img_file.read()).decode('utf-8')
             
-            prompt = f"Extract all text from this image and translate it to {target_language}. Return ONLY the translated text, preserving the structure and formatting."
-            
+            # Use /api/chat for vision models too
             response = requests.post(
-                f"{self.base_url}/api/generate",
+                f"{self.base_url}/api/chat",
                 json={
                     "model": self.vision_model,
-                    "prompt": prompt,
-                    "images": [image_data],
+                    "messages": [
+                        {
+                            "role": "user",
+                            "content": f"Extract all text from this image and translate it to {target_language}. Return ONLY the translated text, preserving the structure and formatting.",
+                            "images": [image_data]
+                        }
+                    ],
                     "stream": False
                 },
                 timeout=60
             )
             response.raise_for_status()
             result = response.json()
-            return result.get("response", "").strip()
+            return result.get("message", {}).get("content", "").strip()
         except Exception as e:
             print(f"Ollama vision translation error: {e}")
             return ""
@@ -158,6 +197,7 @@ class TranslationService:
         else:
             # Auto-select provider based on configuration
             self.provider = self._get_default_provider()
+        self.translate_images = False  # Flag to enable image translation
     
     def _get_default_provider(self) -> TranslationProvider:
         """Get the default translation provider from configuration"""
@@ -182,6 +222,26 @@ class TranslationService:
         
         return self.provider.translate(text, target_language, source_language)
     
+    def translate_image(self, image_path: str, target_language: str) -> str:
+        """
+        Translate text in an image using vision model (Ollama only)
+        
+        Args:
+            image_path: Path to image file
+            target_language: Target language code
+        
+        Returns:
+            Translated text from image
+        """
+        if not self.translate_images:
+            return ""
+        
+        # Only Ollama supports image translation
+        if isinstance(self.provider, OllamaTranslationProvider):
+            return self.provider.translate_image(image_path, target_language)
+        
+        return ""
+    
     def translate_batch(self, texts: list[str], target_language: str, source_language: str = 'auto') -> list[str]:
         """
         Translate multiple text strings