included privateaatr in all tools

agents/tools/knowledge_tools.py

@@ -1,6 +1,6 @@
 #from .base_tool import BaseTool
 from utils.knowledge_base import KnowledgeBase
-#from pydantic import BaseModel, PrivateAttr
+from pydantic import  PrivateAttr
 from crewai.tools import BaseTool
 
 
@@ -9,10 +9,10 @@ class SearchKnowledgeTool(BaseTool):
     name: str = "search_knowledge"
     description: str = "Search self-help or spiritual wisdom."
     model_config = {"arbitrary_types_allowed": True}
-    #_kp: KnowledgeBase = PrivateAttr()
+    _kp: KnowledgeBase = PrivateAttr()
     def __init__(self, config=None):
         super().__init__()
-        self.kb = KnowledgeBase(config)
+        self._kb = KnowledgeBase(config)
     def _run(self, query: str, k: int = 5):
         return self.kb.search(query, k=k) if self.kb.is_initialized() else \
             [{"text": "General wisdom", "score": 1.0}]

agents/tools/llm_tools.py

@@ -1,6 +1,6 @@
 #from .base_tool import BaseTool
 from models.tinygpt2_model import TinyGPT2Model
-#from pydantic import BaseModel, PrivateAttr
+from pydantic import PrivateAttr
 from crewai.tools import BaseTool
 
 
@@ -9,10 +9,10 @@ class MistralChatTool(BaseTool):
     name: str = "mistral_chat"
     description: str = "Generate an empathetic AI chat response."
     model_config = {"arbitrary_types_allowed": True}
-    #_model: TinyGPT2Model = PrivateAttr()
+    _model: TinyGPT2Model = PrivateAttr()
     def __init__(self, config=None):
         super().__init__()
-        self.model = TinyGPT2Model()
+        self._model = TinyGPT2Model()
     def _run(self, prompt: str, context: dict = None):
         msg = f"Context: {context}\nUser: {prompt}" if context else prompt
         return self.model.generate(msg)
@@ -21,10 +21,10 @@ class GenerateAdviceTool(BaseTool):
     name: str = "generate_advice"
     description: str = "Generate personalized advice."
     model_config = {"arbitrary_types_allowed": True}
-    #_model: TinyGPT2Model = PrivateAttr()
+    _model: TinyGPT2Model = PrivateAttr()
     def __init__(self, config=None):
         super().__init__()
-        self.model = TinyGPT2Model()
+        self._model = TinyGPT2Model()
     def _run(self, user_analysis: dict, wisdom_quotes: list):
         prompt = f"Advice for: {user_analysis}, with wisdom: {wisdom_quotes}"
         return self.model.generate(prompt, max_length=300)
@@ -33,10 +33,10 @@ class SummarizeConversationTool(BaseTool):
     name: str = "summarize_conversation"
     description: str = "Summarize chat with insights and next steps."
     model_config = {"arbitrary_types_allowed": True}
-    #_model: TinyGPT2Model = PrivateAttr()
+    _model: TinyGPT2Model = PrivateAttr()
     def __init__(self, config=None):
         super().__init__()
-        self.model = TinyGPT2Model()
+        self._model = TinyGPT2Model()
     def _run(self, conversation: list):
         prompt = f"Summarize: {conversation}"
         return self.model.generate(prompt, max_length=200)

agents/tools/validation_tools.py

@@ -8,7 +8,7 @@ from dataclasses import dataclass
 import json
 from transformers import pipeline
 import torch
-#from pydantic import BaseModel, PrivateAttr
+from pydantic import  PrivateAttr
 from crewai.tools import BaseTool
 #from .base_tool import BaseTool
 
@@ -408,10 +408,10 @@ class ValidateResponseTool(BaseTool):
     name: str = "validate_response"
     description: str = "Validates safety and helpfulness."
     model_config = {"arbitrary_types_allowed": True}
-    #_config: object = PrivateAttr()
+    _config: object = PrivateAttr()
     def __init__(self, config=None, **data):
         super().__init__(**data)
-        self.config = config
+        self._config = config
         # ... any required initialization ...
     def _run(self, response: str, context: dict = None):
         # Place your actual validation logic here, include dummy for illustration