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from crewai import Crew, Process
from langchain_core.tools import tool
import json
from pydantic import BaseModel, Field
from typing import Dict, List, Any, Type
from .agents import report_generator_agent, skills_extractor_agent, experience_extractor_agent, project_extractor_agent, education_extractor_agent, ProfileBuilderAgent, informations_personnelle_agent, reconversion_detector_agent
from .tasks import generate_report_task, task_extract_skills, task_extract_experience, task_extract_projects, task_extract_education, task_build_profile, task_extract_informations, task_detect_reconversion
from src.deep_learning_analyzer import MultiModelInterviewAnalyzer
from src.rag_handler import RAGHandler
from langchain_core.tools import BaseTool
@tool
def interview_analyser(conversation_history: list, job_description_text: list) -> str:
"""
Appelle cet outil à la toute fin d'un entretien d'embauche pour analyser
l'intégralité de la conversation et générer un rapport de feedback.
Ne l'utilise PAS pour répondre à une question normale, mais seulement pour conclure et analyser l'entretien.
"""
# 1. Analyse DL de la conversation
analyzer = MultiModelInterviewAnalyzer()
structured_analysis = analyzer.run_full_analysis(conversation_history, job_description_text)
# 2. Enrichissement avec RAG
rag_handler = RAGHandler()
rag_feedback = []
# Extraire les intentions et sentiments pour trouver des conseils pertinents
if structured_analysis.get("intent_analysis"):
for intent in structured_analysis["intent_analysis"]:
# Exemple de requête basée sur l'intention
query = f"Conseils pour un candidat qui cherche à {intent['labels'][0]}"
rag_feedback.extend(rag_handler.get_relevant_feedback(query))
if structured_analysis.get("sentiment_analysis"):
for sentiment_group in structured_analysis["sentiment_analysis"]:
for sentiment in sentiment_group:
if sentiment['label'] == 'stress' and sentiment['score'].item() > 0.6:
rag_feedback.extend(rag_handler.get_relevant_feedback("gestion du stress en entretien"))
unique_feedback = list(set(rag_feedback))
interview_crew = Crew(
agents=[report_generator_agent],
tasks=[generate_report_task],
process=Process.sequential,
verbose=False,
telemetry=False
)
final_report = interview_crew.kickoff(inputs={
'structured_analysis_data': json.dumps(structured_analysis, indent=2),
'rag_contextual_feedback': "\n".join(unique_feedback)
})
return final_report
def analyse_cv(cv_content: str) -> json:
crew = Crew(
agents=[
informations_personnelle_agent,
skills_extractor_agent,
experience_extractor_agent,
project_extractor_agent,
education_extractor_agent,
reconversion_detector_agent,
ProfileBuilderAgent
],
tasks=[
task_extract_informations,
task_extract_skills,
task_extract_experience,
task_extract_projects,
task_extract_education,
task_detect_reconversion,
task_build_profile
],
process=Process.sequential,
verbose=False,
telemetry=False
)
result = crew.kickoff(inputs={"cv_content": cv_content})
return result
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