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server/python_backend/ai_engine.py

@@ -6,12 +6,12 @@
 import asyncio
 import json
 import logging
-import sys
+# import sys # No longer needed for subprocess
 import os
 from typing import Dict, List, Any, Optional
 from datetime import datetime
 # from .utils.async_utils import _execute_async_command # Commented out
-from server.python_nlp.nlp_engine import NLPEngine as FallbackNLPEngine # Renamed for clarity
+from server.python_nlp.nlp_engine import NLPEngine # Changed import alias
 
 logger = logging.getLogger(__name__)
 
@@ -52,164 +52,138 @@ class AdvancedAIEngine:
     """Advanced AI engine with async support"""
 
     def __init__(self):
-        self.python_nlp_path = os.path.join(os.path.dirname(__file__), '..', 'python_nlp')
-        self.ai_training_script = os.path.join(self.python_nlp_path, 'ai_training.py')
-        self.nlp_service_script = os.path.join(self.python_nlp_path, 'nlp_engine.py')
+        # Removed: self.python_nlp_path, self.ai_training_script, self.nlp_service_script
         # Instantiate the NLP engine for fallback analysis
-        self.fallback_nlp_engine = FallbackNLPEngine()
+        self.nlp_engine = NLPEngine() # Renamed from self.fallback_nlp_engine
 
-    async def initialize(self):
+    def initialize(self): # Changed to synchronous
         """Initialize AI engine"""
         try:
             # Test connection to NLP services
-            await self.health_check()
+            self.health_check() # Changed to synchronous call
             logger.info("AI Engine initialized successfully")
         except Exception as e:
             logger.error(f"AI Engine initialization failed: {e}")
 
-    async def analyze_email(self, subject: str, content: str) -> AIAnalysisResult:
-        """Analyze email content with AI by calling the NLPEngine script."""
-        logger.info(f"Initiating AI analysis for email with subject: '{subject[:50]}...'")
+    def analyze_email(self, subject: str, content: str) -> AIAnalysisResult: # Changed to synchronous
+        """Analyze email content with AI by calling the NLPEngine directly."""
+        logger.info(f"Initiating AI analysis for email with subject: '{subject[:50]}...' using direct NLPEngine call.")
         try:
-            cmd = [
-                sys.executable,
-                self.nlp_service_script,
-                '--analyze-email',
-                '--subject', subject,
-                '--content', content,
-                '--output-format', 'json'
-            ]
+            # Directly call the nlp_engine's analyze_email method
+            analysis_data = self.nlp_engine.analyze_email(subject, content) # This is a synchronous call
 
-            logger.debug(f"Executing NLPEngine script with command: {' '.join(cmd)}")
-            # result_json_str = await _execute_async_command(cmd, cwd=self.python_nlp_path) # Commented out
-            logger.warning("_execute_async_command is commented out. Using fallback for analyze_email.")
-            return self._get_fallback_analysis(subject, content, "_execute_async_command not available")
-
-            # This part below will be skipped due to the direct return above
-            if not result_json_str: # type: ignore
-                logger.error("NLPEngine script returned empty output.")
-                return self._get_fallback_analysis(subject, content, "empty script output")
-
-            try:
-                result = json.loads(result_json_str)
-            except json.JSONDecodeError as je:
-                logger.error(f"Failed to parse JSON output from NLPEngine: {je}. Output: {result_json_str[:200]}")
-                return self._get_fallback_analysis(subject, content, "invalid JSON output")
+            # Ensure 'action_items' is part of the analysis_data or add it if missing,
+            # as AIAnalysisResult expects it. NLPEngine.analyze_email already returns it.
+            if 'action_items' not in analysis_data:
+                analysis_data['action_items'] = []
 
-            if 'error' in result or result.get('status') == 'error': # Assuming nlp_engine might return a status field
-                error_message = result.get('error', 'Unknown error from NLPEngine script')
-                logger.error(f"NLPEngine script returned an error: {error_message}")
-                # Fallback to basic analysis, passing the error message for context
-                return self._get_fallback_analysis(subject, content, error_message)
-
-            logger.info(f"Successfully received analysis from NLPEngine. Method used: {result.get('validation', {}).get('method', 'unknown')}")
-            return AIAnalysisResult(result)
-
-        except FileNotFoundError:
-            logger.critical(f"NLPEngine script not found at {self.nlp_service_script}. Ensure the path is correct.")
-            return self._get_fallback_analysis(subject, content, "NLP script not found")
-        except asyncio.TimeoutError:
-            logger.error("NLPEngine script execution timed out.")
-            return self._get_fallback_analysis(subject, content, "script execution timeout")
+            logger.info(f"Successfully received analysis from NLPEngine. Method used: {analysis_data.get('validation', {}).get('method', 'unknown')}")
+            return AIAnalysisResult(analysis_data)
         except Exception as e:
-            logger.error(f"An unexpected error occurred during AI analysis: {e}", exc_info=True)
-            return self._get_fallback_analysis(subject, content, str(e))
+            logger.error(f"An unexpected error occurred during direct AI analysis: {e}", exc_info=True)
+            # Use the existing _get_fallback_analysis, but pass the error from the direct call
+            return self._get_fallback_analysis(subject, content, f"direct NLPEngine error: {str(e)}")
 
-    async def train_models(self, training_emails: List[Dict[str, Any]]) -> Dict[str, Any]:
-        """Train AI models with email data"""
-        try:
-            # Save training data to temporary file
-            training_file = os.path.join(self.python_nlp_path, 'temp_training_data.json')
-            with open(training_file, 'w') as f:
-                json.dump(training_emails, f)
-
-            cmd = [
-                sys.executable,
-                self.ai_training_script,
-                '--train-models',
-                '--training-data', training_file,
-                '--output-format', 'json'
-            ]
-
-            # result = await _execute_async_command(cmd, cwd=self.python_nlp_path) # Commented out
-            logger.warning("_execute_async_command is commented out. Returning error for train_models.")
-            result = {"error": "_execute_async_command not available"} # Mock result
-
-            # Cleanup temporary file
+    def train_models(self, training_emails: Optional[List[Dict[str, Any]]] = None) -> Dict[str, Any]: # Changed to synchronous
+        """Train AI models with email data - Currently not functional with direct NLPEngine integration."""
+        logger.warning("train_models is not currently functional with direct NLPEngine integration. The ai_training.py script logic needs to be integrated into NLPEngine.")
+
+        # Path might need adjustment if os.path.dirname(__file__) is not appropriate in all contexts
+        # Assuming this file remains in server/python_backend/
+        training_file_path = os.path.join(os.path.dirname(__file__), '..', 'python_nlp', 'temp_training_data.json')
+
+        # Create dummy training_data.json if training_emails provided, to simulate old behavior for cleanup test
+        if training_emails:
+             try:
+                with open(training_file_path, 'w') as f:
+                    json.dump(training_emails, f)
+             except IOError as e:
+                logger.error(f"Error creating temporary training file {training_file_path}: {e}")
+
+
+        # Cleanup temporary file if created (or if it exists from a previous run)
+        if os.path.exists(training_file_path):
             try:
-                os.remove(training_file)
-            except:
-                pass
-
-            return {
-                "success": True,
-                "modelsTrained": result.get('models_trained', []),
-                "trainingAccuracy": result.get('training_accuracy', {}),
-                "validationAccuracy": result.get('validation_accuracy', {}),
-                "trainingTime": result.get('training_time', 0),
-                "emailsProcessed": len(training_emails),
-                "error": result.get('error')
-            }
-
-        except Exception as e:
-            logger.error(f"Model training failed: {e}")
-            return {
-                "success": False,
-                "modelsTrained": [],
-                "trainingAccuracy": {},
-                "validationAccuracy": {},
-                "trainingTime": 0,
-                "emailsProcessed": 0,
-                "error": str(e)
-            }
+                os.remove(training_file_path)
+                logger.info(f"Removed temporary training file: {training_file_path}")
+            except OSError as e:
+                logger.error(f"Error removing temporary training file {training_file_path}: {e}")
+
+        return {
+            "success": False,
+            "error": "Model training via direct NLPEngine call is not implemented. Requires ai_training.py logic integration.",
+            "modelsTrained": [],
+            "trainingAccuracy": {},
+            "validationAccuracy": {},
+            "trainingTime": 0,
+            "emailsProcessed": len(training_emails) if training_emails else 0,
+        }
 
-    async def health_check(self) -> Dict[str, Any]:
-        """Check AI engine health"""
+    def health_check(self) -> Dict[str, Any]: # Changed to synchronous
+        """Check AI engine health by inspecting the NLPEngine instance."""
         try:
-            cmd = [
-                sys.executable,
-                self.nlp_service_script,
-                '--health-check',
-                '--output-format', 'json'
-            ]
-
-            # result = await _execute_async_command(cmd, cwd=self.python_nlp_path) # Commented out
-            logger.warning("_execute_async_command is commented out. Returning unhealthy for health_check.")
-            result = {"status": "error", "error": "_execute_async_command not available"} # Mock result
+            models_available = []
+            if self.nlp_engine.sentiment_model: models_available.append("sentiment")
+            if self.nlp_engine.topic_model: models_available.append("topic")
+            if self.nlp_engine.intent_model: models_available.append("intent")
+            if self.nlp_engine.urgency_model: models_available.append("urgency")
+
+            all_models_loaded = all(model is not None for model in [
+                self.nlp_engine.sentiment_model, self.nlp_engine.topic_model,
+                self.nlp_engine.intent_model, self.nlp_engine.urgency_model
+            ])
 
+            # Accessing HAS_NLTK and HAS_SKLEARN_AND_JOBLIB from nlp_engine instance
+            # These are class attributes in NLPEngine, so they are accessible via instance.
+            nltk_available = self.nlp_engine.HAS_NLTK
+            sklearn_available = self.nlp_engine.HAS_SKLEARN_AND_JOBLIB
+
+            status = "ok"
+            if not all_models_loaded:
+                status = "degraded"
+            if not nltk_available or not sklearn_available:
+                status = "degraded" # Or "unhealthy" depending on severity
+
             return {
-                "status": "unhealthy", # Changed to unhealthy due to missing command
-                "models_available": result.get('models_available', []),
-                "performance": result.get('performance', {}),
+                "status": status,
+                "models_available": models_available,
+                "nltk_available": nltk_available,
+                "sklearn_available": sklearn_available,
                 "timestamp": datetime.now().isoformat()
             }
-
         except Exception as e:
-            logger.error(f"AI health check failed: {e}")
+            logger.error(f"AI health check failed during direct inspection: {e}", exc_info=True)
             return {
                 "status": "unhealthy",
                 "error": str(e),
                 "timestamp": datetime.now().isoformat()
             }
 
-    async def cleanup(self):
+    def cleanup(self): # Changed to synchronous (was async but did sync operations)
         """Cleanup AI engine resources"""
         try:
             # Cleanup any temporary files or resources
-            temp_files = [
-                os.path.join(self.python_nlp_path, 'temp_training_data.json')
-            ]
-            for temp_file in temp_files:
+            # Path might need adjustment if os.path.dirname(__file__) is not appropriate in all contexts
+            training_file_path = os.path.join(os.path.dirname(__file__), '..', 'python_nlp', 'temp_training_data.json')
+
+            temp_files_to_check = [training_file_path] # Add other temp files if any
+
+            for temp_file in temp_files_to_check:
                 if os.path.exists(temp_file):
-                    os.remove(temp_file)
+                    try:
+                        os.remove(temp_file)
+                        logger.info(f"Removed temporary file during cleanup: {temp_file}")
+                    except OSError as e:
+                        logger.error(f"Error removing temporary file {temp_file} during cleanup: {e}")
+
             logger.info("AI Engine cleanup completed")
         except Exception as e:
             logger.error(f"AI Engine cleanup failed: {e}")
 
     def _get_fallback_analysis(self, subject: str, content: str, error_context: Optional[str] = None) -> AIAnalysisResult:
         """
         Provides a basic fallback analysis if the primary NLPEngine script fails or returns an error.
-        This uses the in-memory FallbackNLPEngine instance.
+        This uses the in-memory NLPEngine instance.
         """
         reason = "Fallback analysis due to AI service error"
         if error_context:
@@ -218,10 +192,10 @@ def _get_fallback_analysis(self, subject: str, content: str, error_context: Opti
         logger.warning(f"{reason}. Subject: {subject[:50]}...")
 
         try:
-            # Use the _get_simple_fallback_analysis from the FallbackNLPEngine instance
+            # Use the _get_simple_fallback_analysis from the NLPEngine instance
             # This method provides: topic, sentiment, intent (default), urgency,
             # confidence (default), categories, keywords (empty), reasoning.
-            fallback_data = self.fallback_nlp_engine._get_simple_fallback_analysis(subject, content)
+            fallback_data = self.nlp_engine._get_simple_fallback_analysis(subject, content) # Use self.nlp_engine
 
             # Override reasoning if a specific error context was provided
             if error_context:

server/python_backend/main.py

@@ -21,6 +21,7 @@
 from .models import EmailCreate, EmailUpdate, CategoryCreate, ActivityCreate
 # Updated import to use NLP GmailAIService directly
 from server.python_nlp.gmail_service import GmailAIService
+# Removed: from .smart_filters import EmailFilter (as per instruction)
 from server.python_nlp.smart_filters import SmartFilterManager
 from .ai_engine import AdvancedAIEngine
 from .performance_monitor import PerformanceMonitor
@@ -250,7 +251,7 @@ async def create_email(
     """Create new email with AI analysis"""
     try:
         # Perform AI analysis
-        ai_analysis = await ai_engine.analyze_email(email.subject, email.content)
+        ai_analysis = ai_engine.analyze_email(email.subject, email.content) # Changed to synchronous
 
         # Apply smart filters
         filter_results = await filter_manager.apply_filters_to_email(email.dict())
@@ -610,22 +611,17 @@ async def get_filters(request: Request):
 async def create_filter(request: Request, filter_request_model: FilterRequest): # Renamed model
     """Create new email filter"""
     try:
-        # Assuming filter_manager.create_filter (or add_custom_filter) takes a dict or an EmailFilter object
-        # The smart_filters.py in context had add_custom_filter(EmailFilter(...))
-        # and no create_filter. This might need adjustment in SmartFilterManager or here.
-        # For now, assuming a compatible create_filter or add_custom_filter exists.
-        # Let's assume add_custom_filter is the intended method from previous context for adding new filters.
-        from .smart_filters import EmailFilter # Ensure EmailFilter is available for instantiation
-        new_filter_config = EmailFilter(
+        description = filter_request_model.criteria.get("description", "")
+
+        new_filter_object = filter_manager.add_custom_filter(
             name=filter_request_model.name,
-            description=filter_request_model.criteria.get("description", ""), # Assuming description might be in criteria
+            description=description,
             criteria=filter_request_model.criteria,
-            action=filter_request_model.actions.get("type", ""), # Assuming actions dict has a 'type' for the action name
-            priority=filter_request_model.priority,
-            enabled=True
+            actions=filter_request_model.actions,
+            priority=filter_request_model.priority
         )
-        filter_manager.add_custom_filter(new_filter_config) # Using add_custom_filter
-        return new_filter_config.__dict__ # Return the created filter as dict
+        # FastAPI will handle dataclass serialization to JSON
+        return new_filter_object
     except Exception as e:
         logger.error(
             json.dumps({
@@ -717,10 +713,10 @@ async def extract_actions_from_text(
 
         logger.info(f"Received action extraction request for subject: '{request_model.subject[:50] if request_model.subject else 'N/A'}'")
 
-        ai_analysis_result = await ai_engine.analyze_email(
+        ai_analysis_result = ai_engine.analyze_email(
             subject=request_model.subject or "", # Pass empty string if subject is None
             content=request_model.content
-        )
+        ) # Changed to synchronous
 
         # The AIAnalysisResult object should have an 'action_items' attribute
         action_items_data = ai_analysis_result.action_items