Update tools_engine.py

tools_engine.py

@@ -1,74 +1,101 @@
 """
-Nexari Tools Engine (Smart Override Edition)
+Nexari Tools Engine (UPDATED)
 Author: Piyush
-Description: Prevents silly mistakes on short messages by using a 'Hardcoded Logic Layer' before the Neural Network.
+Improvements:
+ - Canonical intent labels returned
+ - More robust greeting detection
+ - Safer web search formatting
+ - Defensive error handling
 """
 
 from duckduckgo_search import DDGS
 from transformers import pipeline
+import re
 
 print(">>> Tools: Loading Intent Classification Model...")
+# zero-shot pipeline; if heavy, Docker predownload already handles it
 intent_classifier = pipeline("zero-shot-classification", model="typeform/distilbert-base-uncased-mnli")
 
 def analyze_intent(user_text):
     """
-    Decides intent with a Safety Layer for short greetings to prevent hallucination.
+    Returns canonical intent labels:
+      - "internet_search"
+      - "coding_request"
+      - "checking_time"
+      - "general"
     """
+    if not user_text:
+        return "general"
+
     text_lower = user_text.lower().strip()
-    
-    # === LAYER 1: HARDCODED SAFETY (The Fix for 'Hi' -> Searching) ===
-    # Agar user bas hello bol raha hai, to AI brain mat lagao. Direct chat karo.
-    direct_chat_triggers = ["hi", "hello", "hey", "hlo", "hola", "namaste", "what is your name", "who are you"]
-    
-    # Agar input EXACTLY inme se ek hai, ya start hota hai
+
+    # === HARDCODED SAFETY (Greeting/Identity) ===
+    direct_chat_triggers = [
+        "hi", "hello", "hey", "hlo", "hola", "namaste",
+        "what is your name", "who are you", "who r you", "your name"
+    ]
+    # exact match or common short greeting at start
     if text_lower in direct_chat_triggers or any(text_lower.startswith(t + " ") for t in direct_chat_triggers):
-        print(f">>> Brain Override: Detected Greeting/Identity -> Force 'general conversation'")
-        return "general conversation"
+        print(f">>> Brain Override: Detected Greeting/Identity -> Force 'general'")
+        return "general"
 
-    # === LAYER 2: NEURAL NETWORK DECISION ===
+    # === ZERO-SHOT PASSES ===
     candidate_labels = [
-        "internet search", 
-        "general conversation", 
-        "coding request", 
+        "internet search",
+        "general conversation",
+        "coding request",
         "checking time"
     ]
-    
+
     try:
         result = intent_classifier(user_text, candidate_labels)
-        top_intent = result['labels'][0]
+        top_label = result['labels'][0]
         confidence = result['scores'][0]
-        
-        print(f">>> Brain: Detected '{top_intent}' ({confidence:.2f})")
-        
-        # Confidence Threshold badha diya (0.4 -> 0.5) taaki ghalat search na kare
+        print(f">>> Brain: Detected '{top_label}' ({confidence:.2f})")
+
         if confidence > 0.5:
-            return top_intent
+            # map to canonical labels
+            mapping = {
+                "internet search": "internet_search",
+                "general conversation": "general",
+                "coding request": "coding_request",
+                "checking time": "checking_time"
+            }
+            return mapping.get(top_label, "general")
     except Exception as e:
         print(f"Intent Error: {e}")
-        
-    return "general conversation"
 
-def perform_web_search(user_text):
+    return "general"
+
+def perform_web_search(user_text, max_results=3):
     """
-    Executes search only when triggered.
+    Execute a brief web search and return a compact summary.
     """
     try:
         clean_query = user_text.lower()
-        remove_phrases = ["search for", "google", "find", "tell me about", "latest info on", "news about"]
+        remove_phrases = ["search for", "google", "find", "tell me about", "latest info on", "news about", "lookup"]
         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 
+        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)
-        
+        results = list(DDGS().text(clean_query, max_results=max_results))
+
         if results:
-            summary = "\n".join([f"- {r['title']}: {r['body']}" for r in results])
-            return f"### WEB DATA ###\n{summary}\nINSTRUCTION: Answer based on this data."
+            parts = []
+            for r in results:
+                title = r.get('title', '').strip()
+                body = re.sub(r'\s+', ' ', r.get('body', '').strip())
+                # keep short
+                if len(body) > 250:
+                    body = body[:250].rsplit(' ', 1)[0] + "..."
+                parts.append(f"- {title}: {body}")
+            summary = "\n".join(parts)
+            return f"### WEB DATA (short) ###\n{summary}\nINSTRUCTION: Use this to inform the answer; prefer concise synthesis."
         return ""
-            
     except Exception as e:
         print(f"Search Error: {e}")
-        return ""
+        return ""