Remove Hugging Face sentiment pipeline and ensure Azure creds load

agents/azure_agent.py

@@ -2,6 +2,7 @@
 import os
 import sys
 import logging
+from dotenv import load_dotenv
 
 # Add parent directory to path for imports
 sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
@@ -22,6 +23,9 @@ class AzureTextAnalyticsAgent:
             from azure.ai.textanalytics import TextAnalyticsClient
             from azure.core.credentials import AzureKeyCredential
             
+            # Load environment variables so Azure credentials from .env are available
+            load_dotenv()
+
             key = os.getenv("AZURE_TEXT_ANALYTICS_KEY")
             endpoint = os.getenv("AZURE_TEXT_ANALYTICS_ENDPOINT")
             

agents/sentiment_agent.py

@@ -1,4 +1,4 @@
-"""Sentiment Agent - responsible for sentiment analysis (uses Azure, Hugging Face, or NLTK fallback)"""
+"""Sentiment Agent - responsible for sentiment analysis (uses Azure with NLTK fallback)"""
 import os
 import sys
 import logging
@@ -8,22 +8,14 @@ sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 from config import MODEL_CONFIG
 from agents.azure_agent import AzureTextAnalyticsAgent
 
-# Import transformers with error handling
-try:
-    from transformers import pipeline
-    transformers_available = True
-except ImportError:
-    logging.warning("Transformers library not available. Using fallback sentiment analysis.")
-    transformers_available = False
-
 class SentimentAgent:
-    """Agent responsible for sentiment analysis (uses Azure, Hugging Face, or NLTK fallback)"""
+    """Agent responsible for sentiment analysis (uses Azure with NLTK fallback)"""
     
     def __init__(self, config=None):
         self.config = config or MODEL_CONFIG or {}
         self.azure_agent = AzureTextAnalyticsAgent(config=self.config)
-        self.sentiment_analyzer = None
         self.ready = False
+        self._fallback_analyzer = None
         self._initialize()
     
     def _initialize(self):
@@ -33,56 +25,29 @@ class SentimentAgent:
             self.ready = True
             logging.info("[SentimentAgent] Using Azure Text Analytics")
             return
-        
-        # Fallback to Hugging Face
-        sentiment_model = self.config.get('sentiment', {}).get('primary_model', 'distilbert/distilbert-base-uncased-finetuned-sst-2-english') if self.config else 'distilbert/distilbert-base-uncased-finetuned-sst-2-english'
-        
-        if transformers_available:
-            try:
-                logging.info("[SentimentAgent] Initializing Hugging Face sentiment analyzer...")
-                self.sentiment_analyzer = pipeline("sentiment-analysis", model=sentiment_model)
-                self.ready = True
-                logging.info("[SentimentAgent] ✓ Initialized successfully")
-            except Exception as e:
-                logging.warning(f"[SentimentAgent] Hugging Face model failed: {e}, using fallback")
-                self.sentiment_analyzer = None
-                self.ready = True  # Fallback available
-        else:
-            self.ready = True  # Fallback available
-    
+ 
+        # Azure not available – rely on fallback
+        logging.info("[SentimentAgent] Using NLTK VADER fallback for sentiment analysis")
+        self.ready = True  # Fallback available
+ 
     def analyze(self, text):
-        """Analyze sentiment of text (tries Azure, then Hugging Face, then NLTK)"""
+        """Analyze sentiment of text (tries Azure first, falls back to NLTK)"""
         # Try Azure first
         if self.azure_agent.is_ready():
             result = self.azure_agent.analyze(text)
             if result is not None:
                 return result
-        
-        # Fallback to Hugging Face
-        if self.sentiment_analyzer:
-            try:
-                result = self.sentiment_analyzer(text)[0]
-                label = result['label'].lower()
-                score = result['score']
-                
-                if 'positive' in label:
-                    return score
-                elif 'negative' in label:
-                    return -score
-                else:
-                    return 0.0
-            except Exception as e:
-                logging.error(f"[SentimentAgent] Error: {e}")
-                return self._fallback_analyze(text)
-        else:
-            return self._fallback_analyze(text)
-    
+ 
+        return self._fallback_analyze(text)
+ 
     def _fallback_analyze(self, text):
         """Fallback sentiment analysis using NLTK VADER"""
         try:
-            from nltk.sentiment import SentimentIntensityAnalyzer
-            analyzer = SentimentIntensityAnalyzer()
-            scores = analyzer.polarity_scores(text)
+            if self._fallback_analyzer is None:
+                from nltk.sentiment import SentimentIntensityAnalyzer
+                self._fallback_analyzer = SentimentIntensityAnalyzer()
+
+            scores = self._fallback_analyzer.polarity_scores(text)
             return scores['compound']  # Returns value between -1 and 1
         except Exception as e:
             logging.error(f"[SentimentAgent] Fallback failed: {e}")

app.py

@@ -165,31 +165,19 @@ def initialize_sentiment_analyzer():
     try:
         logging.info("🔄 [Sentiment Analyzer] Starting initialization...")
         print("🔄 [Sentiment Analyzer] Starting initialization...")
-        try:
-            from transformers import pipeline
-            analyzer = pipeline(
-                "sentiment-analysis",
-                model="distilbert/distilbert-base-uncased-finetuned-sst-2-english"
-            )
-            result = analyzer("test")
-            logging.info("✓ [Sentiment Analyzer] Hugging Face model loaded")
-            print("✓ [Sentiment Analyzer] Hugging Face model loaded")
-            init_status['sentiment_analyzer']['ready'] = True
-            return True
-        except ImportError:
+        from agents.sentiment_agent import SentimentAgent
+
+        agent = SentimentAgent()
+
+        if agent.azure_agent.is_ready():
+            logging.info("✓ [Sentiment Analyzer] Azure Text Analytics ready")
+            print("✓ [Sentiment Analyzer] Azure Text Analytics ready")
+        else:
             logging.info("✓ [Sentiment Analyzer] Using fallback (NLTK VADER)")
             print("✓ [Sentiment Analyzer] Using fallback (NLTK VADER)")
-            init_status['sentiment_analyzer']['ready'] = True
-            return True
-        except Exception as e:
-            error_msg = str(e)
-            if 'np.float_' in error_msg or 'NumPy 2' in error_msg or '_ARRAY_API' in error_msg:
-                logging.warning(f"⚠ [Sentiment Analyzer] NumPy compatibility issue - using fallback")
-                print("⚠ [Sentiment Analyzer] NumPy compatibility issue - using fallback")
-                init_status['sentiment_analyzer']['ready'] = True
-                return True
-            else:
-                raise
+
+        init_status['sentiment_analyzer']['ready'] = True
+        return True
     except Exception as e:
         error_msg = f"Sentiment analyzer initialization failed: {e}"
         logging.warning(f"⚠ [Sentiment Analyzer] {error_msg} - using fallback")
@@ -506,9 +494,9 @@ def initialize_components():
         # CRITICAL: Only mark as initialized if ALL components are ready
         # If any component fails, EXIT the application immediately
         if init_status['fully_initialized']:
-            is_initialized = True
+        is_initialized = True
             logging.info("✓ Galatea AI system fully initialized and ready")
-            logging.info(f"Emotions initialized: {galatea_ai.emotional_state}")
+        logging.info(f"Emotions initialized: {galatea_ai.emotional_state}")
         else:
             logging.error("=" * 60)
             logging.error("❌ INITIALIZATION FAILED - EXITING APPLICATION")

models.yaml

@@ -28,9 +28,10 @@ inflection_ai:
 
 # Sentiment Analysis Configuration
 sentiment:
-  # Primary model (Hugging Face)
-  primary_model: "distilbert/distilbert-base-uncased-finetuned-sst-2-english"
-  # Fallback: NLTK VADER (automatic if primary fails)
+  # Uses Azure Text Analytics when credentials are provided
+  preferred: "azure_text_analytics"
+  # Automatically falls back to NLTK VADER if Azure is unavailable
+  fallback: "nltk_vader"
 
 # Memory System Configuration (JSON only)
 memory:

requirements.txt

@@ -4,3 +4,4 @@ python-dotenv==1.0.0
 requests==2.31.0
 pyyaml==6.0.1
 openai>=1.0.0
+azure-ai-textanalytics==5.3.0