Added image generation to blog writer

backend/services/blog_writer/content/enhanced_content_generator.py

@@ -1,12 +1,14 @@
 """
-EnhancedContentGenerator - thin orchestrator combining URL selection and Gemini provider.
+EnhancedContentGenerator - thin orchestrator for section generation.
 
-Provides Draft vs Polished modes and optional URL Context usage.
+Provider parity:
+- Uses main_text_generation.llm_text_gen to respect GPT_PROVIDER (Gemini/HF)
+- No direct provider coupling here; Google grounding remains in research only
 """
 
 from typing import Any, Dict
 
-from services.llm_providers.gemini_grounded_provider import GeminiGroundedProvider
+from services.llm_providers.main_text_generation import llm_text_gen
 from .source_url_manager import SourceURLManager
 from .context_memory import ContextMemory
 from .transition_generator import TransitionGenerator
@@ -15,24 +17,37 @@ from .flow_analyzer import FlowAnalyzer
 
 class EnhancedContentGenerator:
     def __init__(self):
-        self.provider = GeminiGroundedProvider()
         self.url_manager = SourceURLManager()
         self.memory = ContextMemory(max_entries=12)
         self.transitioner = TransitionGenerator()
         self.flow = FlowAnalyzer()
 
     async def generate_section(self, section: Any, research: Any, mode: str = "polished") -> Dict[str, Any]:
-        urls = self.url_manager.pick_relevant_urls(section, research)
         prev_summary = self.memory.build_previous_sections_summary(limit=2)
-        prompt = self._build_prompt(section, research, prev_summary)
-        result = await self.provider.generate_grounded_content(
-            prompt=prompt,
-            content_type="linkedin_article",
-            temperature=0.6 if mode == "polished" else 0.8,
-            max_tokens=2048,
-            urls=urls,
-            mode=mode,
-        )
+        urls = self.url_manager.pick_relevant_urls(section, research)
+        prompt = self._build_prompt(section, research, prev_summary, urls)
+        # Provider-agnostic text generation (respect GPT_PROVIDER & circuit-breaker)
+        content_text: str = ""
+        try:
+            ai_resp = llm_text_gen(
+                prompt=prompt,
+                json_struct=None,
+                system_prompt=None,
+            )
+            if isinstance(ai_resp, dict) and ai_resp.get("text"):
+                content_text = ai_resp.get("text", "")
+            elif isinstance(ai_resp, str):
+                content_text = ai_resp
+            else:
+                # Fallback best-effort extraction
+                content_text = str(ai_resp or "")
+        except Exception as e:
+            content_text = ""
+
+        result = {
+            "content": content_text,
+            "sources": [{"title": u.get("title", ""), "url": u.get("url", "")} for u in urls] if urls else [],
+        }
         # Generate transition and compute intelligent flow metrics
         previous_text = prev_summary
         current_text = result.get("content", "")
@@ -56,19 +71,22 @@ class EnhancedContentGenerator:
             pass
         return result
 
-    def _build_prompt(self, section: Any, research: Any, prev_summary: str) -> str:
+    def _build_prompt(self, section: Any, research: Any, prev_summary: str, urls: list) -> str:
         heading = getattr(section, 'heading', 'Section')
         key_points = getattr(section, 'key_points', [])
         keywords = getattr(section, 'keywords', [])
         target_words = getattr(section, 'target_words', 300)
+        url_block = "\n".join([f"- {u.get('title','')} ({u.get('url','')})" for u in urls]) if urls else "(no specific URLs provided)"
 
         return (
             f"You are writing the blog section '{heading}'.\n\n"
-            f"Context summary: {prev_summary}\n"
-            f"Key points: {', '.join(key_points)}\n"
-            f"Keywords: {', '.join(keywords)}\n"
-            f"Target word count: {target_words}.\n"
-            "Use only factual info from provided sources; add short transition, then body."
+            f"Context summary (previous sections): {prev_summary}\n\n"
+            f"Authoring requirements:\n"
+            f"- Target word count: ~{target_words}\n"
+            f"- Use the following key points: {', '.join(key_points)}\n"
+            f"- Include these keywords naturally: {', '.join(keywords)}\n"
+            f"- Cite insights from these sources when relevant (do not output raw URLs):\n{url_block}\n\n"
+            "Write engaging, well-structured markdown with clear paragraphs (2-4 sentences each) separated by double line breaks."
         )