Update tools_engine.py

tools_engine.py

@@ -1,44 +1,83 @@
 """
-Nexari Tools Engine (Lazy Loading Fix)
+Nexari Tools Engine (Lazy Loading)
+Author: Piyush
+Description: Loads the Intent Brain only when needed to prevent Server Crash.
 """
+
 from duckduckgo_search import DDGS
 from transformers import pipeline
 
-# Global variable, initially None
+# Global variable to hold the model (initially empty)
 _intent_classifier = None
 
-def get_classifier():
+def get_intent_model():
+    """
+    Loads model only if it's not already loaded.
+    """
     global _intent_classifier
     if _intent_classifier is None:
-        print(">>> Tools: Loading Intent Model (Lazy)...")
+        print(">>> Tools: Lazy Loading Intent Model...")
         _intent_classifier = pipeline("zero-shot-classification", model="typeform/distilbert-base-uncased-mnli")
     return _intent_classifier
 
 def analyze_intent(user_text):
+    """
+    Decides intent with Safety Layer + Lazy Loading.
+    """
     text_lower = user_text.lower().strip()
     
-    # Fast Greeting Check
-    direct_chat = ["hi", "hello", "hey", "who are you", "what is your name"]
-    if any(text_lower.startswith(t) for t in direct_chat):
-        return "general_conversation"
+    # LAYER 1: HARDCODED SAFETY
+    direct_chat_triggers = ["hi", "hello", "hey", "hlo", "hola", "namaste", "what is your name", "who are you"]
+    
+    if text_lower in direct_chat_triggers or any(text_lower.startswith(t + " ") for t in direct_chat_triggers):
+        return "general conversation"
 
+    # LAYER 2: LAZY NEURAL NETWORK
     try:
-        # Load only when needed
-        classifier = get_classifier()
-        candidate_labels = ["internet search", "general conversation", "coding request", "checking time"]
+        # Load Model Here (On Demand)
+        classifier = get_intent_model()
+        
+        candidate_labels = [
+            "internet search", 
+            "general conversation", 
+            "coding request", 
+            "checking time"
+        ]
+        
         result = classifier(user_text, candidate_labels)
+        top_intent = result['labels'][0]
+        confidence = result['scores'][0]
+        
+        print(f">>> Brain: Detected '{top_intent}' ({confidence:.2f})")
         
-        if result['scores'][0] > 0.5:
-            return result['labels'][0]
+        if confidence > 0.5:
+            return top_intent
     except Exception as e:
         print(f"Intent Error: {e}")
         
-    return "general_conversation"
+    return "general conversation"
 
 def perform_web_search(user_text):
+    """
+    Executes search.
+    """
     try:
-        clean_query = user_text.replace("search", "").strip()
+        clean_query = user_text.lower()
+        remove_phrases = ["search for", "google", "find", "tell me about", "latest info on", "news about"]
+        for phrase in remove_phrases:
+            clean_query = clean_query.replace(phrase, "")
+        
+        clean_query = clean_query.strip()
+        if len(clean_query) < 2: clean_query = user_text 
+
+        print(f">>> Action: Searching Web for '{clean_query}'...")
         results = DDGS().text(clean_query, max_results=3)
-        return results if results else None
-    except:
+        
+        if results:
+            # Return raw list
+            return results
+        return None
+            
+    except Exception as e:
+        print(f"Search Error: {e}")
         return None