fix: WYSIWYG editor, content generation, and writing assistant bug fixes

- Fix text selection menu not showing: wire contentRef via inputRef on multiline TextField - Fix blog title not truncating: add min-w-0 for flex item overflow - Fix outline generation 500: escape curly braces in f-string prompt template - Fix content generation 'NoneType not callable': replace SessionLocal() with get_session_for_user(), add db param to MediumBlogGenerator, fix signature mismatch in database_task_manager - Fix writing assistant suggest 500: add auth + user_id to API endpoint and service, replace sync requests with httpx.AsyncClient - Fix hallucination detector 404: explicitly include router in main.py and app.py - Fix missing error_data in task failure responses - Hide CopilotKit web inspector button - Remove hardcoded fallback suggestions from SmartTypingAssist - Fix stale closure refs in SmartTypingAssist handleTypingChange - Add two-column editor layout, stats bar, section hover menu - Various subscription, billing, and research module improvements
2026-05-14 09:11:30 +05:30
parent 7385100017
commit 928c2f20aa
113 changed files with 4344 additions and 10064 deletions
--- a/backend/services/blog_writer/research/competitor_analyzer.py
+++ b/backend/services/blog_writer/research/competitor_analyzer.py
@@ -6,6 +6,7 @@ Extracts competitor insights and market intelligence from research content.

 from typing import Dict, Any
 from loguru import logger
+import json


 class CompetitorAnalyzer:
@@ -22,7 +23,7 @@ class CompetitorAnalyzer:
        Extract and analyze:
        1. Top competitors mentioned (companies, brands, platforms)
        2. Content gaps (what competitors are missing)
-        3. Market opportunities (untapped areas)
+        3. Opportunities (untapped areas)
        4. Competitive advantages (what makes content unique)
        5. Market positioning insights
        6. Industry leaders and their strategies
@@ -55,18 +56,38 @@ class CompetitorAnalyzer:
            "required": ["top_competitors", "content_gaps", "opportunities", "competitive_advantages", "market_positioning", "industry_leaders", "analysis_notes"]
        }
        
-        competitor_analysis = llm_text_gen(
+        raw = llm_text_gen(
            prompt=competitor_prompt,
-            json_struct=competitor_schema,
            user_id=user_id
        )
        
-        if isinstance(competitor_analysis, dict) and 'error' not in competitor_analysis:
-            logger.info("✅ AI competitor analysis completed successfully")
-            return competitor_analysis
+        # Parse JSON from LLM response (works with both string and dict return types)
+        import re
+        if isinstance(raw, str):
+            cleaned = raw.strip()
+            if cleaned.startswith('```json'):
+                cleaned = cleaned[7:]
+            if cleaned.startswith('```'):
+                cleaned = cleaned[3:]
+            if cleaned.endswith('```'):
+                cleaned = cleaned[:-3]
+            cleaned = cleaned.strip()
+            try:
+                competitor_analysis = json.loads(cleaned)
+            except json.JSONDecodeError:
+                json_match = re.search(r'\{.*\}', cleaned, re.DOTALL)
+                if json_match:
+                    competitor_analysis = json.loads(json_match.group(0))
+                else:
+                    raise ValueError(f"Competitor analysis returned non-JSON string: {cleaned[:200]}")
+        elif isinstance(raw, dict):
+            competitor_analysis = raw
        else:
-            # Fail gracefully - no fallback data
-            error_msg = competitor_analysis.get('error', 'Unknown error') if isinstance(competitor_analysis, dict) else str(competitor_analysis)
-            logger.error(f"AI competitor analysis failed: {error_msg}")
-            raise ValueError(f"Competitor analysis failed: {error_msg}")
+            raise ValueError(f"Unexpected LLM response type: {type(raw)}")
+        
+        if 'error' in competitor_analysis:
+            raise ValueError(f"Competitor analysis failed: {competitor_analysis.get('error', 'Unknown error')}")
+        
+        logger.info("✅ AI competitor analysis completed successfully")
+        return competitor_analysis
    
--- a/backend/services/blog_writer/research/content_angle_generator.py
+++ b/backend/services/blog_writer/research/content_angle_generator.py
@@ -63,18 +63,41 @@ class ContentAngleGenerator:
            "required": ["content_angles"]
        }
        
-        angles_result = llm_text_gen(
+        raw = llm_text_gen(
            prompt=angles_prompt,
-            json_struct=angles_schema,
            user_id=user_id
        )
        
-        if isinstance(angles_result, dict) and 'content_angles' in angles_result:
-            logger.info("✅ AI content angles generation completed successfully")
-            return angles_result['content_angles'][:7]
+        # Parse JSON from LLM response (works with both string and dict return types)
+        import json, re
+        if isinstance(raw, str):
+            cleaned = raw.strip()
+            if cleaned.startswith('```json'):
+                cleaned = cleaned[7:]
+            if cleaned.startswith('```'):
+                cleaned = cleaned[3:]
+            if cleaned.endswith('```'):
+                cleaned = cleaned[:-3]
+            cleaned = cleaned.strip()
+            try:
+                angles_result = json.loads(cleaned)
+            except json.JSONDecodeError:
+                json_match = re.search(r'\{.*\}', cleaned, re.DOTALL)
+                if json_match:
+                    angles_result = json.loads(json_match.group(0))
+                else:
+                    raise ValueError(f"Content angles returned non-JSON string: {cleaned[:200]}")
+        elif isinstance(raw, dict):
+            angles_result = raw
        else:
-            # Fail gracefully - no fallback data
-            error_msg = angles_result.get('error', 'Unknown error') if isinstance(angles_result, dict) else str(angles_result)
-            logger.error(f"AI content angles generation failed: {error_msg}")
-            raise ValueError(f"Content angles generation failed: {error_msg}")
+            raise ValueError(f"Unexpected LLM response type: {type(raw)}")
+        
+        if 'error' in angles_result:
+            raise ValueError(f"Content angles generation failed: {angles_result.get('error', 'Unknown error')}")
+        
+        if 'content_angles' not in angles_result:
+            raise ValueError(f"Content angles missing from response")
+        
+        logger.info("✅ AI content angles generation completed successfully")
+        return angles_result['content_angles'][:7]
    
--- a/backend/services/blog_writer/research/keyword_analyzer.py
+++ b/backend/services/blog_writer/research/keyword_analyzer.py
@@ -6,6 +6,7 @@ Extracts and analyzes keywords from research content using structured AI respons

 from typing import Dict, Any, List
 from loguru import logger
+import json


 class KeywordAnalyzer:
@@ -62,18 +63,38 @@ class KeywordAnalyzer:
            "required": ["primary", "secondary", "long_tail", "search_intent", "difficulty", "content_gaps", "semantic_keywords", "trending_terms", "analysis_insights"]
        }
        
-        keyword_analysis = llm_text_gen(
+        raw = llm_text_gen(
            prompt=keyword_prompt,
-            json_struct=keyword_schema,
            user_id=user_id
        )
        
-        if isinstance(keyword_analysis, dict) and 'error' not in keyword_analysis:
-            logger.info("✅ AI keyword analysis completed successfully")
-            return keyword_analysis
+        # Parse JSON from LLM response (works with both string and dict return types)
+        import re
+        if isinstance(raw, str):
+            cleaned = raw.strip()
+            if cleaned.startswith('```json'):
+                cleaned = cleaned[7:]
+            if cleaned.startswith('```'):
+                cleaned = cleaned[3:]
+            if cleaned.endswith('```'):
+                cleaned = cleaned[:-3]
+            cleaned = cleaned.strip()
+            try:
+                keyword_analysis = json.loads(cleaned)
+            except json.JSONDecodeError:
+                json_match = re.search(r'\{.*\}', cleaned, re.DOTALL)
+                if json_match:
+                    keyword_analysis = json.loads(json_match.group(0))
+                else:
+                    raise ValueError(f"Keyword analysis returned non-JSON string: {cleaned[:200]}")
+        elif isinstance(raw, dict):
+            keyword_analysis = raw
        else:
-            # Fail gracefully - no fallback data
-            error_msg = keyword_analysis.get('error', 'Unknown error') if isinstance(keyword_analysis, dict) else str(keyword_analysis)
-            logger.error(f"AI keyword analysis failed: {error_msg}")
-            raise ValueError(f"Keyword analysis failed: {error_msg}")
+            raise ValueError(f"Unexpected LLM response type: {type(raw)}")
+        
+        if 'error' in keyword_analysis:
+            raise ValueError(f"Keyword analysis failed: {keyword_analysis.get('error', 'Unknown error')}")
+        
+        logger.info("✅ AI keyword analysis completed successfully")
+        return keyword_analysis
    
--- a/backend/services/blog_writer/research/research_service.py
+++ b/backend/services/blog_writer/research/research_service.py
@@ -111,19 +111,22 @@ class ResearchService:
                # Exa research workflow
                from .exa_provider import ExaResearchProvider
                from services.subscription.preflight_validator import validate_exa_research_operations
-                from services.database import get_db
+                from services.database import get_session_for_user
                from services.subscription import PricingService
                import os
                import time
                
-                # Pre-flight validation
-                db_val = next(get_db())
+                # Pre-flight validation (use get_session_for_user since get_db is a FastAPI dependency)
+                db_val = get_session_for_user(user_id)
+                if not db_val:
+                    raise HTTPException(status_code=503, detail="Database temporarily unavailable. Please try again.")
                try:
                    pricing_service = PricingService(db_val)
                    gpt_provider = os.getenv("GPT_PROVIDER", "google")
                    validate_exa_research_operations(pricing_service, user_id, gpt_provider)
                finally:
-                    db_val.close()
+                    if db_val:
+                        db_val.close()
                
                # Execute Exa search
                api_start_time = time.time()
@@ -162,13 +165,15 @@ class ResearchService:
            elif config.provider == ResearchProvider.TAVILY:
                # Tavily research workflow
                from .tavily_provider import TavilyResearchProvider
-                from services.database import get_db
+                from services.database import get_session_for_user
                from services.subscription import PricingService
                import os
                import time
                
-                # Pre-flight validation (similar to Exa)
-                db_val = next(get_db())
+                # Pre-flight validation (use get_session_for_user since get_db is a FastAPI dependency)
+                db_val = get_session_for_user(user_id)
+                if not db_val:
+                    raise HTTPException(status_code=503, detail="Database temporarily unavailable. Please try again.")
                try:
                    pricing_service = PricingService(db_val)
                    # Check Tavily usage limits
@@ -429,14 +434,16 @@ class ResearchService:
                # Exa research workflow
                from .exa_provider import ExaResearchProvider
                from services.subscription.preflight_validator import validate_exa_research_operations
-                from services.database import get_db
+                from services.database import get_session_for_user
                from services.subscription import PricingService
                import os
                
                await task_manager.update_progress(task_id, "🌐 Connecting to Exa neural search...")
                
-                # Pre-flight validation
-                db_val = next(get_db())
+                # Pre-flight validation (use get_session_for_user since get_db is a FastAPI dependency)
+                db_val = get_session_for_user(user_id)
+                if not db_val:
+                    raise HTTPException(status_code=503, detail="Database temporarily unavailable. Please try again.")
                try:
                    pricing_service = PricingService(db_val)
                    gpt_provider = os.getenv("GPT_PROVIDER", "google")
@@ -446,7 +453,8 @@ class ResearchService:
                    await task_manager.update_progress(task_id, f"❌ Subscription limit exceeded: {http_error.detail.get('message', str(http_error.detail)) if isinstance(http_error.detail, dict) else str(http_error.detail)}")
                    raise
                finally:
-                    db_val.close()
+                    if db_val:
+                        db_val.close()
                
                # Execute Exa search
                await task_manager.update_progress(task_id, "🤖 Executing Exa neural search...")
@@ -485,14 +493,16 @@ class ResearchService:
            elif config.provider == ResearchProvider.TAVILY:
                # Tavily research workflow
                from .tavily_provider import TavilyResearchProvider
-                from services.database import get_db
+                from services.database import get_session_for_user
                from services.subscription import PricingService
                import os
                
                await task_manager.update_progress(task_id, "🌐 Connecting to Tavily AI search...")
                
-                # Pre-flight validation
-                db_val = next(get_db())
+                # Pre-flight validation (use get_session_for_user since get_db is a FastAPI dependency)
+                db_val = get_session_for_user(user_id)
+                if not db_val:
+                    raise HTTPException(status_code=503, detail="Database temporarily unavailable. Please try again.")
                try:
                    pricing_service = PricingService(db_val)
                    # Check Tavily usage limits
@@ -529,7 +539,8 @@ class ResearchService:
                except Exception as e:
                    logger.warning(f"Error checking Tavily limits: {e}")
                finally:
-                    db_val.close()
+                    if db_val:
+                        db_val.close()
                
                # Execute Tavily search
                await task_manager.update_progress(task_id, "🤖 Executing Tavily AI search...")
--- a/backend/services/blog_writer/research/tavily_provider.py
+++ b/backend/services/blog_writer/research/tavily_provider.py
@@ -135,11 +135,14 @@ class TavilyResearchProvider(BaseProvider):
    
    def track_tavily_usage(self, user_id: str, cost: float, search_depth: str):
        """Track Tavily API usage after successful call."""
-        from services.database import get_db
+        from services.database import get_session_for_user
        from services.subscription import PricingService
        from sqlalchemy import text
        
-        db = next(get_db())
+        db = get_session_for_user(user_id)
+        if not db:
+            logger.warning(f"[Tavily] Could not get DB session for user {user_id}, skipping usage tracking")
+            return
        try:
            pricing_service = PricingService(db)
            current_period = pricing_service.get_current_billing_period(user_id)