Update src/interview_simulator/entretient_version_prod.py

src/interview_simulator/entretient_version_prod.py

@@ -1,98 +1,98 @@
-import os
-import sys
-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 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]
-
-class InterviewProcessor:
-    def __init__(self, cv_document: Dict[str, Any], job_offer: Dict[str, Any], conversation_history: List[Dict[str, Any]]):
-        if not cv_document or 'candidat' not in cv_document:
-            raise ValueError("Document CV invalide fourni.")
-        if not job_offer:
-            raise ValueError("Données de l'offre d'emploi non fournies.")
-
-        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()
-
-    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
-    )
-
-    def _load_prompt_template(self) -> str:
-        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')
-        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,
-            cv=formatted_cv_str
-        )
-        llm_messages = [SystemMessage(content=system_prompt)] + messages
-        response = self.llm_with_tools.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")
-        return graph_builder.compile()
-
-    def run(self, messages: List[Dict[str, Any]]) -> Dict[str, Any]:
-        initial_state = self.conversation_history + messages
+import os
+import sys
+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 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]
+
+class InterviewProcessor:
+    def __init__(self, cv_document: Dict[str, Any], job_offer: Dict[str, Any], conversation_history: List[Dict[str, Any]]):
+        if not cv_document or 'candidat' not in cv_document:
+            raise ValueError("Document CV invalide fourni.")
+        if not job_offer:
+            raise ValueError("Données de l'offre d'emploi non fournies.")
+
+        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()
+
+    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
+    )
+
+    def _load_prompt_template(self) -> str:
+        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')
+        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,
+            cv=formatted_cv_str
+        )
+        llm_messages = [SystemMessage(content=system_prompt)] + messages
+        response = self.llm_with_tools.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")
+        return graph_builder.compile()
+
+    def run(self, messages: List[Dict[str, Any]]) -> Dict[str, Any]:
+        initial_state = self.conversation_history + messages
         return self.graph.invoke({"messages": initial_state})