Added enhanced linguistic analyzer and persona quality improver

backend/services/blog_writer/content/transition_generator.py

@@ -0,0 +1,143 @@
+"""
+TransitionGenerator - produces intelligent transitions between sections using LLM analysis.
+
+Uses Gemini API for natural transitions while maintaining cost efficiency through smart caching.
+"""
+
+from typing import Optional, Dict
+from loguru import logger
+import hashlib
+
+# Import the common gemini provider
+from services.llm_providers.gemini_provider import gemini_text_response
+
+
+class TransitionGenerator:
+    def __init__(self):
+        # Simple cache to avoid redundant LLM calls for similar transitions
+        self._cache: Dict[str, str] = {}
+        logger.info("✅ TransitionGenerator initialized with LLM-based generation")
+
+    def generate_transition(self, previous_text: str, current_heading: str, use_llm: bool = True) -> str:
+        """
+        Return a 1–2 sentence bridge from previous_text into current_heading.
+        
+        Args:
+            previous_text: Previous section content
+            current_heading: Current section heading
+            use_llm: Whether to use LLM generation (default: True for substantial content)
+        """
+        prev = (previous_text or "").strip()
+        if not prev:
+            return f"Let's explore {current_heading.lower()} next."
+
+        # Create cache key
+        cache_key = self._get_cache_key(prev, current_heading)
+        
+        # Check cache first
+        if cache_key in self._cache:
+            logger.debug("Transition generation cache hit")
+            return self._cache[cache_key]
+
+        # Determine if we should use LLM
+        should_use_llm = use_llm and self._should_use_llm_generation(prev, current_heading)
+        
+        if should_use_llm:
+            try:
+                transition = self._llm_generate_transition(prev, current_heading)
+                self._cache[cache_key] = transition
+                logger.info("LLM-based transition generated")
+                return transition
+            except Exception as e:
+                logger.warning(f"LLM transition generation failed, using fallback: {e}")
+                # Fall through to heuristic generation
+        
+        # Heuristic fallback
+        transition = self._heuristic_transition(prev, current_heading)
+        self._cache[cache_key] = transition
+        return transition
+
+    def _should_use_llm_generation(self, previous_text: str, current_heading: str) -> bool:
+        """Determine if content is substantial enough to warrant LLM generation."""
+        # Use LLM for substantial previous content (>100 words) or complex headings
+        word_count = len(previous_text.split())
+        complex_heading = len(current_heading.split()) > 2 or any(char in current_heading for char in [':', '-', '&'])
+        
+        return word_count > 100 or complex_heading
+
+    def _llm_generate_transition(self, previous_text: str, current_heading: str) -> str:
+        """Use Gemini API for intelligent transition generation."""
+        
+        # Truncate previous text to minimize tokens while keeping context
+        prev_truncated = previous_text[-200:]  # Last 200 chars usually contain the conclusion
+        
+        prompt = f"""
+Create a smooth, natural 1-2 sentence transition from the previous content to the new section.
+
+PREVIOUS CONTENT (ending): {prev_truncated}
+NEW SECTION HEADING: {current_heading}
+
+Requirements:
+- Write exactly 1-2 sentences
+- Create a logical bridge between the topics
+- Use natural, engaging language
+- Avoid repetition of the previous content
+- Lead smoothly into the new section topic
+
+Generate only the transition text, no explanations or formatting.
+"""
+
+        try:
+            result = gemini_text_response(
+                prompt=prompt,
+                temperature=0.6,  # Balanced creativity and consistency
+                max_tokens=300,   # Increased tokens for better transitions
+                system_prompt="You are an expert content writer creating smooth transitions between sections."
+            )
+            
+            if result and result.strip():
+                # Clean up the response
+                transition = result.strip()
+                # Ensure it's 1-2 sentences
+                sentences = transition.split('. ')
+                if len(sentences) > 2:
+                    transition = '. '.join(sentences[:2]) + '.'
+                return transition
+            else:
+                logger.warning("LLM transition response empty, using fallback")
+                return self._heuristic_transition(previous_text, current_heading)
+                
+        except Exception as e:
+            logger.error(f"LLM transition generation error: {e}")
+            return self._heuristic_transition(previous_text, current_heading)
+
+    def _heuristic_transition(self, previous_text: str, current_heading: str) -> str:
+        """Fallback heuristic-based transition generation."""
+        tail = previous_text[-240:]
+        
+        # Enhanced heuristics based on content patterns
+        if any(word in tail.lower() for word in ["problem", "issue", "challenge"]):
+            return f"Now that we've identified the challenges, let's explore {current_heading.lower()} to find solutions."
+        elif any(word in tail.lower() for word in ["solution", "approach", "method"]):
+            return f"Building on this approach, {current_heading.lower()} provides the next step in our analysis."
+        elif any(word in tail.lower() for word in ["important", "crucial", "essential"]):
+            return f"Given this importance, {current_heading.lower()} becomes our next focus area."
+        else:
+            return (
+                f"Building on the discussion above, this leads us into {current_heading.lower()}, "
+                f"where we focus on practical implications and what to do next."
+            )
+
+    def _get_cache_key(self, previous_text: str, current_heading: str) -> str:
+        """Generate cache key from content hashes."""
+        # Use last 100 chars of previous text and heading for cache key
+        prev_hash = hashlib.md5(previous_text[-100:].encode()).hexdigest()[:8]
+        heading_hash = hashlib.md5(current_heading.encode()).hexdigest()[:8]
+        return f"{prev_hash}_{heading_hash}"
+
+    def clear_cache(self):
+        """Clear transition cache (useful for testing or memory management)."""
+        self._cache.clear()
+        logger.info("TransitionGenerator cache cleared")
+
+