Spaces:
Running
Running
File size: 9,762 Bytes
6da2b52 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 | import json
import logging
import os
import yaml
import asyncio
from datetime import datetime
from typing import Dict, Any
from crewai import Agent, Task, Crew, Process
from src.config.app_config import get_small_llm, get_big_llm
logger = logging.getLogger(__name__)
class CVAgentOrchestrator:
def __init__(self):
self.llm = get_small_llm()
self.big_llm = get_big_llm()
self.agents_config = self._load_yaml("agents.yaml")
self.tasks_config = self._load_yaml("tasks.yaml")
self._create_agents()
def _load_yaml(self, filename: str) -> Dict:
base_path = os.path.dirname(os.path.dirname(__file__))
config_path = os.path.join(base_path, "config", filename)
with open(config_path, 'r', encoding='utf-8') as f:
return yaml.safe_load(f)
def _create_agents(self):
def make_agent(name, llm_override=None):
return Agent(
config=self.agents_config[name],
llm=llm_override or self.llm,
allow_delegation=False,
verbose=False,
max_iter=1,
respect_context_window=True
)
self.cv_splitter = make_agent('cv_splitter', llm_override=self.big_llm)
self.skills_extractor = make_agent('skills_extractor')
self.experience_extractor = make_agent('experience_extractor')
self.project_extractor = make_agent('project_extractor')
self.education_extractor = make_agent('education_extractor')
self.reconversion_detector = make_agent('reconversion_detector')
self.language_extractor = make_agent('language_extractor')
self.etudiant_detector = make_agent('etudiant_detector')
self.identity_extractor = make_agent('identity_extractor')
async def split_cv_sections(self, cv_content: str) -> Dict[str, str]:
"""
decoupage du cv en sections
"""
task_config = self.tasks_config['split_cv_task'].copy()
task_config['description'] = task_config['description'].format(cv_content=cv_content[:20000])
task = Task(
config=task_config,
agent=self.cv_splitter
)
crew = Crew(
agents=[self.cv_splitter],
tasks=[task],
process=Process.sequential,
verbose=False
)
result = await crew.kickoff_async()
parsed = self._parse_json_output(result, default_structure={})
return parsed
async def extract_all_sections(self, sections: Dict[str, str]) -> Dict[str, Any]:
"""
execution des taches en parraléle.
"""
def create_task_async(task_key, agent, **kwargs):
t_config = self.tasks_config[task_key].copy()
t_config['description'] = t_config['description'].format(**kwargs)
task = Task(config=t_config, agent=agent)
c = Crew(agents=[agent], tasks=[task], verbose=False)
return (task_key, c.kickoff_async())
tasks_def = [
('skills_task', self.skills_extractor, {
'experiences': sections.get('experiences', ''),
'projects': sections.get('projects', ''),
'skills': sections.get('skills', ''),
'education': sections.get('education', '')
}),
('experience_task', self.experience_extractor, {'experiences': sections.get('experiences', '')}),
('project_task', self.project_extractor, {'projects': sections.get('projects', '')}),
('education_task', self.education_extractor, {'education': sections.get('education', '')}),
('reconversion_task', self.reconversion_detector, {
'experiences': sections.get('experiences', ''),
'education': sections.get('education', '')
}),
('language_task', self.language_extractor, {
'languages': sections.get('languages', '')
}),
('etudiant_task', self.etudiant_detector, {
'education': sections.get('education', ''),
'current_date': datetime.now().strftime("%Y-%m-%d")
}),
('identity_task', self.identity_extractor, {
'personal_info': sections.get('personal_info', '')
})
]
task_coroutines = [create_task_async(key, agent, **kwargs) for key, agent, kwargs in tasks_def]
keys = [t[0] for t in task_coroutines]
coroutines = [t[1] for t in task_coroutines]
results_list = await asyncio.gather(*coroutines, return_exceptions=True)
results_map = {}
for key, result in zip(keys, results_list):
if isinstance(result, Exception):
logger.error(f"Task '{key}' failed: {result}")
else:
results_map[key] = result
return self._aggregate_results(results_map)
def _aggregate_results(self, results_map: Dict[str, Any]) -> Dict[str, Any]:
def get_parsed(key, default=None):
if key not in results_map:
return default
return self._parse_json_output(results_map[key], default)
competences = get_parsed('skills_task', {"hard_skills": [], "soft_skills": []})
experiences = get_parsed('experience_task', [])
projets = get_parsed('project_task', {"professional": [], "personal": []})
formations = get_parsed('education_task', [])
reconversion = get_parsed('reconversion_task', {}).get("reconversion_analysis", {})
etudiant_data = get_parsed('etudiant_task', {}).get("etudiant_analysis", {})
latest_end_date = etudiant_data.get("latest_education_end_date")
if latest_end_date:
is_student_by_date = self._is_still_student(latest_end_date)
etudiant_data["is_etudiant"] = is_student_by_date
langues_raw = get_parsed('language_task', {})
if isinstance(competences, dict):
# Deduplicate hard_skills while preserving order
raw_skills = competences.get("hard_skills", [])
seen = set()
unique_skills = []
for skill in raw_skills:
key = str(skill).lower() if not isinstance(skill, str) else skill.lower()
if key not in seen:
seen.add(key)
unique_skills.append(skill)
competences["hard_skills"] = unique_skills
identity = get_parsed('identity_task', {})
return {
"candidat": {
"first_name": identity.get("first_name") if isinstance(identity, dict) else None,
"compétences": competences,
"expériences": experiences,
"reconversion": reconversion,
"projets": projets,
"formations": formations,
"etudiant": etudiant_data,
"langues": langues_raw.get("langues", []) if isinstance(langues_raw, dict) else [],
}
}
def _is_still_student(self, date_str: str) -> bool:
if not date_str:
return False
date_str = str(date_str).lower().strip()
ongoing_keywords = ["present", "présent", "current", "cours", "aujourd'hui", "now"]
if any(keyword in date_str for keyword in ongoing_keywords):
return True
try:
now = datetime.now()
end_date = None
if len(date_str) == 10 and date_str[4] == '-' and date_str[7] == '-':
end_date = datetime.strptime(date_str, "%Y-%m-%d")
elif len(date_str) == 7 and date_str[4] == '-':
end_date = datetime.strptime(date_str, "%Y-%m")
elif '/' in date_str:
parts = date_str.split('/')
if len(parts) == 2:
m, y = parts
if len(y) == 4:
end_date = datetime.strptime(date_str, "%m/%Y")
elif len(y) == 2:
end_date = datetime.strptime(date_str, "%m/%y")
elif len(date_str) == 4 and date_str.isdigit():
end_date = datetime.strptime(date_str, "%Y")
end_date = end_date.replace(month=12, day=31)
if end_date:
return end_date >= now
return False
except (ValueError, IndexError):
logger.warning(f"Date parsing failed for: {date_str}")
return False
def _parse_json_output(self, crew_output, default_structure=None) -> Any:
raw = crew_output.raw if hasattr(crew_output, 'raw') else str(crew_output)
if '```json' in raw:
raw = raw.split('```json')[1].split('```')[0].strip()
elif '```' in raw:
parts = raw.split('```')
if len(parts) >= 3:
raw = parts[1].strip()
# Clean common LLM artifacts
raw = raw.strip().lstrip('\ufeff') # BOM
try:
return json.loads(raw)
except json.JSONDecodeError:
# Try to find the first JSON object or array in the output
for start_char, end_char in [('{', '}'), ('[', ']')]:
start_idx = raw.find(start_char)
end_idx = raw.rfind(end_char)
if start_idx != -1 and end_idx > start_idx:
try:
return json.loads(raw[start_idx:end_idx + 1])
except json.JSONDecodeError:
continue
logger.error(f"JSON Parse Error (after cleanup): {raw[:200]}")
return default_structure if default_structure is not None else {} |