push updated backend changes and auto start buiding

agents.py

@@ -1,3 +1,4 @@
+# bioinformatics_ai/agents.py
 import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer
 from autogen import AssistantAgent, UserProxyAgent
@@ -10,8 +11,8 @@ BASE_DIR = os.path.dirname(os.path.abspath(__file__))
 if BASE_DIR not in sys.path:
     sys.path.insert(0, BASE_DIR)
 
-# Load BioMistral once
 
+# Load BioMistral once
 class BioMistralModel:
     def __init__(self, model_name=LLM_MODEL, device=None):
         print(f"[BioMistralModel] Loading model: {model_name}")
@@ -38,56 +39,42 @@ class BioMistralModel:
             )
 
         text = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
-        
         return text.split("Answer:", 1)[-1].strip()
 
 
-
 # Formatting Agent
-
 class FormattingAgent(AssistantAgent):
     def __init__(self, name="FormattingAgent", **kwargs):
         super().__init__(name=name, **kwargs)
 
     def format_text(self, text: str) -> str:
-       
         cleaned = " ".join(text.split())
-       
         if cleaned:
             cleaned = cleaned[0].upper() + cleaned[1:]
         return cleaned
 
 
-
 # Tutor Agent
-
 class TutorAgent(AssistantAgent):
     def __init__(self, name="TutorAgent", **kwargs):
         super().__init__(name=name, **kwargs)
         self.model = BioMistralModel()
         self.format_agent = FormattingAgent()
-        self.rag_agent = RAGAgent(vectorstore_dir=str(VECTORSTORE_DIR))  # Exists but unused
+        self.rag_agent = RAGAgent(vectorstore_dir=str(VECTORSTORE_DIR))  # safe conversion
 
     def process_query(self, query: str) -> str:
         print(f"[TutorAgent] Received query: {query}")
         
-        # Direct BioMistral answer
         answer = self.model.generate_answer(query)
         confidence = self.estimate_confidence(answer)
 
         print(f"[TutorAgent] Confidence: {confidence:.2f}")
         if confidence < CONFIDENCE_THRESHOLD:
             print("[TutorAgent] Confidence low, but still using BioMistral (RAG unused).")
-           
 
-        # Format with FormattingAgent
-        final_answer = self.format_agent.format_text(answer)
-        return final_answer
+        return self.format_agent.format_text(answer)
 
     def estimate_confidence(self, answer: str) -> float:
-        """
-        Dummy confidence estimator — could be replaced with actual logic.
-        """
         length = len(answer.strip())
         if length > 100:
             return 0.9
@@ -97,9 +84,9 @@ class TutorAgent(AssistantAgent):
             return 0.5
 
 
-#
 # User Agent
-
 class BioUser(UserProxyAgent):
     def __init__(self, name="BioUser", **kwargs):
+        
+        kwargs.setdefault("code_execution_config", {"use_docker": False})
         super().__init__(name=name, **kwargs)