Update src/interview_simulator/entretient_version_prod.py

src/interview_simulator/entretient_version_prod.py

@@ -4,16 +4,12 @@ import json
 from typing import Dict, List, Any, Annotated
 from typing_extensions import TypedDict
 
-from langchain_core.messages import AIMessage, SystemMessage, HumanMessage, ToolMessage
-from langchain_groq import ChatGroq
+from langchain_core.messages import AIMessage, SystemMessage, HumanMessage
 from langgraph.graph import StateGraph, START, END
 from langgraph.graph.message import add_messages
-from langgraph.prebuilt import ToolNode 
 from langchain_openai import ChatOpenAI
 
 from src.config import read_system_prompt, format_cv
-from src.crew.crew_pool import interview_analyser 
-
 
 class State(TypedDict):
     messages: Annotated[list, add_messages]
@@ -28,9 +24,7 @@ class InterviewProcessor:
         self.job_offer = job_offer
         self.cv_data = cv_document['candidat']
         self.conversation_history = conversation_history
-        self.tools = [interview_analyser]
         self.llm = self._get_llm()
-        self.llm_with_tools = self.llm.bind_tools(self.tools)
 
         self.system_prompt_template = self._load_prompt_template()
         self.graph = self._build_graph()
@@ -38,59 +32,41 @@ class InterviewProcessor:
     def _get_llm(self) -> ChatOpenAI:
         openai_api_key = os.getenv("OPENAI_API_KEY")
         return ChatOpenAI(
-        temperature=0.6, 
-        model_name="gpt-4o-mini", 
-        api_key=openai_api_key
-    )
+            temperature=0.6, 
+            model_name="gpt-4o-mini", 
+            api_key=openai_api_key
+        )
 
     def _load_prompt_template(self) -> str:
-        return read_system_prompt('prompts/rag_prompt_old.txt')
+        return read_system_prompt('prompts/rag_prompt_old.txt') 
 
     def _chatbot_node(self, state: State) -> dict:
-        if state["messages"] and isinstance(state["messages"][-1], ToolMessage):
-            tool_message = state["messages"][-1]
-            return {"messages": [AIMessage(content=tool_message.content)]}
         messages = state["messages"]
         formatted_cv_str = format_cv(self.cv_data)
 
-        mission = self.job_offer.get('mission', 'Non spécifiée')
-        profil_recherche = self.job_offer.get('profil_recherche', 'Non spécifié')
-        competences = self.job_offer.get('competences', 'Non spécifiées')
-        pole = self.job_offer.get('pole', 'Non spécifié')
+        # Formatage du prompt système
         system_prompt = self.system_prompt_template.format(
             entreprise=self.job_offer.get('entreprise', 'notre entreprise'),
             poste=self.job_offer.get('poste', 'ce poste'),
-            mission=mission,
-            profil_recherche=profil_recherche,
-            competences=competences,
-            pole=pole,
+            mission=self.job_offer.get('mission', 'Non spécifiée'),
+            profil_recherche=self.job_offer.get('profil_recherche', 'Non spécifié'),
+            competences=self.job_offer.get('competences', 'Non spécifiées'),
+            pole=self.job_offer.get('pole', 'Non spécifié'),
             cv=formatted_cv_str
         )
+        
         llm_messages = [SystemMessage(content=system_prompt)] + messages
-        response = self.llm_with_tools.invoke(llm_messages)
+        response = self.llm.invoke(llm_messages)
         return {"messages": [response]}
 
-    def _route_after_chatbot(self, state: State) -> str:
-        last_message = state["messages"][-1]
-        if last_message.tool_calls:
-            return "call_tool"
-        return END
-
     def _build_graph(self) -> any:
         graph_builder = StateGraph(State)
         
         graph_builder.add_node("chatbot", self._chatbot_node)
-        graph_builder.add_node("call_tool", ToolNode(self.tools))        
-        graph_builder.add_edge(START, "chatbot")        
-        graph_builder.add_conditional_edges(
-            "chatbot",
-            self._route_after_chatbot,
-            {
-                "call_tool": "call_tool", 
-                END: END                  
-            }
-        )
-        graph_builder.add_edge("call_tool", "chatbot")
+        graph_builder.add_edge(START, "chatbot")
+        # Le graph se termine toujours après la réponse du chatbot
+        graph_builder.add_edge("chatbot", END)
+        
         return graph_builder.compile()
 
     def run(self, messages: List[Dict[str, Any]]) -> Dict[str, Any]: