Update src/crew/crew_pool.py

src/crew/crew_pool.py

@@ -1,15 +1,25 @@
-import json
 from crewai import Crew, Process
-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
-from typing import List
+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, cv_section_splitter_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_split_cv_sections, 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
 
-def run_interview_analysis(conversation_history: list, job_description_text: str, analyzer_model, rag_handler) -> str:
+@tool
+def interview_analyser(conversation_history: list, job_description_text: list) -> str:
     """
-    Analyse l'intégralité de la conversation et génère un rapport de feedback.
-    Cette fonction est conçue pour être appelée en arrière-plan.
+    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.
     """
-    structured_analysis = analyzer_model.run_full_analysis(conversation_history, job_description_text)
+    analyzer = MultiModelInterviewAnalyzer()
+    structured_analysis = analyzer.run_full_analysis(conversation_history, job_description_text)
+
+    rag_handler = RAGHandler()
     rag_feedback = []
     if structured_analysis.get("intent_analysis"):
         for intent in structured_analysis["intent_analysis"]:
@@ -19,9 +29,8 @@ def run_interview_analysis(conversation_history: list, job_description_text: str
     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'] > 0.6:
+                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],
@@ -35,11 +44,86 @@ def run_interview_analysis(conversation_history: list, job_description_text: str
         'structured_analysis_data': json.dumps(structured_analysis, indent=2),
         'rag_contextual_feedback': "\n".join(unique_feedback)
     })
-    
     return final_report
 
+class CVSectionExtractor:
+    """
+    Extracteur de sections qui utilise les résultats du CVSectionSplitterAgent
+    pour distribuer le contenu approprié à chaque agent spécialisé.
+    """
+    
+    def __init__(self, sections_data: dict):
+        self.sections = sections_data
+        
+    def get_contact_section(self) -> str:
+        return self.sections.get("contact", "")
+        
+    def get_experiences_section(self) -> str:
+        return self.sections.get("experiences", "")
+        
+    def get_projects_section(self) -> str:
+        return self.sections.get("projects", "")
+        
+    def get_education_section(self) -> str:
+        return self.sections.get("education", "")
+        
+    def get_skills_section(self) -> str:
+        return self.sections.get("skills", "")
+        
+    def get_skills_context(self) -> str:
+        """Combine les sections pertinentes pour l'extraction de compétences"""
+        return f"""
+Section Expériences:
+{self.get_experiences_section()}
+
+Section Projets:
+{self.get_projects_section()}
+
+Section Compétences:
+{self.get_skills_section()}
+"""
+
 def analyse_cv(cv_content: str) -> json:
-    crew = Crew(
+    section_splitting_crew = Crew(
+        agents=[cv_section_splitter_agent],
+        tasks=[task_split_cv_sections],
+        process=Process.sequential,
+        verbose=False,
+        telemetry=False
+    )
+    
+    sections_result = section_splitting_crew.kickoff(inputs={"cv_content": cv_content})
+    
+    try:
+        if hasattr(sections_result, 'raw'):
+            sections_json = sections_result.raw
+        else:
+            sections_json = str(sections_result)
+            
+        sections_json_cleaned = sections_json.strip()
+        if '```' in sections_json:
+            if '```json' in sections_json:
+                sections_json_cleaned = sections_json.split('```json')[1].split('```')[0].strip()
+            else:
+                parts = sections_json.split('```')
+                if len(parts) >= 3:
+                    sections_json_cleaned = parts[1].strip()
+        
+        sections_data = json.loads(sections_json_cleaned)
+        extractor = CVSectionExtractor(sections_data)
+        
+    except (json.JSONDecodeError, Exception) as e:
+        sections_data = {
+            "contact": cv_content[:500],
+            "experiences": cv_content,
+            "projects": cv_content,
+            "education": cv_content,
+            "skills": cv_content,
+            "other": ""
+        }
+        extractor = CVSectionExtractor(sections_data)
+    
+    main_crew = Crew(
         agents=[            
             informations_personnelle_agent,
             skills_extractor_agent,
@@ -47,7 +131,6 @@ def analyse_cv(cv_content: str) -> json:
             project_extractor_agent,
             education_extractor_agent,
             reconversion_detector_agent,
-
             ProfileBuilderAgent       
         ],
         tasks=[
@@ -63,5 +146,14 @@ def analyse_cv(cv_content: str) -> json:
         verbose=False,
         telemetry=False
     )
-    result = crew.kickoff(inputs={"cv_content": cv_content})
+    
+    main_inputs = {
+        "contact": extractor.get_contact_section(),
+        "experiences": extractor.get_experiences_section(),
+        "projects": extractor.get_projects_section(),
+        "education": extractor.get_education_section(),
+        "skills": extractor.get_skills_section()
+    }
+    
+    result = main_crew.kickoff(inputs=main_inputs)
     return result