Implement script generation endpoint (Task 18)

- Added ScriptRequest, ScriptResponse, and ScriptSegment Pydantic models
- Created /api/tts/script POST endpoint in main.py
- Integrated with TTSService to generate script segments using AI
- Added generate_script_segments method to TTSService
- Returns segments with unique IDs for frontend tracking
- Handles errors gracefully when AI segmentation fails
- Falls back to pattern-based segmentation when Ollama unavailable
- Added comprehensive tests for the endpoint
- Validates: Requirements 15.1, 15.2

backend/services/tts_service.py

@@ -288,6 +288,62 @@ class TTSService:
         
         return concatenated
     
+    async def generate_script_segments(
+        self,
+        text: str,
+        roles: List[str]
+    ) -> List[Tuple[str, str]]:
+        """
+        Generates script segments with speaker assignments using AI.
+        
+        This method is used for the script preview feature, allowing users to
+        review and edit speaker assignments before generating audio.
+        
+        Args:
+            text: Input text to segment
+            roles: List of detected role IDs/names
+            
+        Returns:
+            List of tuples: (speaker_name, text_content)
+            
+        Raises:
+            Exception: If both AI and pattern-based segmentation fail
+        """
+        logger.info(f"Generating script segments for {len(text)} chars with {len(roles)} roles")
+        
+        # Try AI segmentation first
+        try:
+            is_available = await self.ollama_service.is_available()
+            if is_available:
+                logger.info("Using AI for script segmentation")
+                segments = await self.ollama_service.segment_text(text, roles)
+                
+                if segments:
+                    logger.info(f"AI segmentation produced {len(segments)} segments")
+                    return segments
+                else:
+                    logger.warning("AI segmentation returned empty result")
+            else:
+                logger.info("Ollama not available for script segmentation")
+        except Exception as e:
+            logger.warning(f"AI segmentation failed: {e}")
+        
+        # Fall back to pattern-based segmentation
+        logger.info("Using pattern-based segmentation as fallback")
+        
+        # Create a dummy role_voice_map for pattern-based segmentation
+        # We don't need actual voices here, just role assignments
+        role_voice_map = {role: self.DEFAULT_VOICE for role in roles}
+        
+        # Use existing segment_text method
+        segments_with_voice = self.segment_text(text, role_voice_map)
+        
+        # Convert from (role, voice, text) to (role, text)
+        segments = [(role, text) for role, voice, text in segments_with_voice]
+        
+        logger.info(f"Pattern-based segmentation produced {len(segments)} segments")
+        return segments
+    
     async def generate_audiobook(
         self,
         text: str,