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from typing import Dict, Any, List, Optional
# from langchain.tools import Tool
# from langchain.agents import AgentExecutor, create_react_agent
# from langchain.prompts import PromptTemplate
# from bs4 import BeautifulSoup
import requests
import json
import logging
# Set up logging
logger = logging.getLogger(__name__)
import re
def extract_json_from_text(text: str) -> str:
"""
Extract JSON string from LLM response with markdown formatting.
"""
match = re.search(r"```(?:json)?\s*(\{.*?\})\s*```", text, re.DOTALL)
if match:
return match.group(1).strip()
match = re.search(r"(\{.*\})", text, re.DOTALL)
if match:
return match.group(1).strip()
raise ValueError("No valid JSON found in LLM response")
class ResumeMatcher:
def __init__(
self,
llm_client: Any,
current_user: str,
current_time: str
):
self.llm = llm_client
self.current_user = current_user
self.current_time = current_time
# self.tools = [
# Tool(
# name="job_scraper",
# func=self._scrape_job_posting,
# description="Scrapes job descriptions from URLs. Input should be a URL."
# )
# ]
logger.info("ResumeMatcher initialized successfully")
def _get_llm_response(self, messages: List[Dict[str, str]]) -> str:
"""Helper method to get LLM response"""
try:
# Use the generate method directly as implemented in your LLMClient
response = self.llm.generate(messages)
# Handle the response based on your LLMClient's output format
return response
except Exception as e:
logger.error(f"Error getting LLM response: {e}")
raise
def analyze_resume(self, resume_text: str) -> Dict[str, Any]:
"""Analyze resume and extract information"""
if not resume_text or not resume_text.strip():
raise ValueError("Empty response from LLM")
try:
messages = [
{
"role": "system",
"content": "You are an expert resume analyzer. Extract key information from the resume and return it in a structured JSON format."
},
{
"role": "user",
"content": f"""Analyze this resume and extract information:
{resume_text}
Return a JSON object with these exact keys:
{{
"personal_info": {{
"name": "Full name",
"email": "Email address",
"phone": "Phone number",
"location": "Current location",
"linkedin": "LinkedIn URL if available"
}},
"current_role": "Most recent/current job title",
"years_of_experience": "Total years of professional experience as a number",
"education": [
{{
"degree": "Degree name",
"field": "Field of study",
"institution": "Institution name",
"year": "Year of completion"
}}
],
"technical_skills": ["List of technical skills"],
"soft_skills": ["List of soft skills"],
"industries": ["List of industries worked in"],
"key_achievements": ["List of key achievements"],
"certifications": ["List of relevant certifications"],
"languages": ["List of languages known"]
}}"""
}
]
response_text = self._get_llm_response(messages)
clean_json = extract_json_from_text(response_text)
return json.loads(clean_json)
except Exception as e:
logger.error(f"Resume analysis error: {e}")
return {
"error": str(e),
"timestamp": self.current_time
}
# def _scrape_job_posting(self, url: str) -> str:
# """Scrape job posting from URL"""
# try:
# headers = {
# 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
# }
# response = requests.get(url, headers=headers)
# soup = BeautifulSoup(response.text, 'html.parser')
# for script in soup(["script", "style"]):
# script.decompose()
# text = soup.get_text()
# lines = (line.strip() for line in text.splitlines())
# chunks = (phrase.strip() for line in lines for phrase in line.split(" "))
# result = ' '.join(chunk for chunk in chunks if chunk)
# print(result)
# return result
# except Exception as e:
# logger.error(f"Error scraping job posting: {e}")
# return f"Error: {str(e)}"
# def parse_job(self, url: str) -> Dict[str, Any]:
# """Parse job posting from URL"""
# try:
# job_text = self._scrape_job_posting(url)
# messages = [
# {
# "role": "system",
# "content": "You are an expert at parsing job descriptions. Extract key information accurately."
# },
# {
# "role": "user",
# "content": f"""Extract key information from this job description:
# {job_text}
# Return a JSON object with:
# {{
# "title": "Job title",
# "required_skills": ["List of required technical skills"],
# "preferred_skills": ["List of preferred skills"],
# "experience_required": "Years of experience required",
# "education_required": "Required education level",
# "responsibilities": ["List of key responsibilities"]
# }}"""
# }
# ]
# response_text = self._get_llm_response(messages)
# return json.loads(response_text)
# except Exception as e:
# logger.error(f"Job parsing error: {e}")
# return {
# "error": str(e),
# "timestamp": self.current_time
# }
def parse_job_from_text(self, job_text: str) -> Dict[str, Any]:
"""Parse manually pasted job description"""
try:
if not job_text.strip():
raise ValueError("Empty job description text")
messages = [
{
"role": "system",
"content": "You are an expert at parsing job descriptions. Extract key information accurately."
},
{
"role": "user",
"content": f"""Extract key information from this job description:
{job_text}
Return a JSON object with:
{{
"title": "Job title",
"required_skills": ["List of required technical skills"],
"preferred_skills": ["List of preferred skills"],
"experience_required": "Years of experience required",
"education_required": "Required education level",
"responsibilities": ["List of key responsibilities"]
}}"""
}
]
response_text = self._get_llm_response(messages)
clean_json = extract_json_from_text(response_text)
return json.loads(clean_json)
except Exception as e:
logger.error(f"Job parsing error (manual input): {e}")
return {
"error": str(e),
"timestamp": self.current_time
}
def calculate_match(
self,
resume_data: Dict[str, Any],
job_data: Dict[str, Any]
) -> Dict[str, Any]:
"""Calculate match between resume and job"""
try:
messages = [
{
"role": "system",
"content": "You are an expert at matching resumes to job requirements. Provide detailed analysis and concrete improvement suggestions."
},
{
"role": "user",
"content": f"""Calculate the match between this resume and job description:
Resume Data:
{json.dumps(resume_data, indent=2)}
Job Requirements:
{json.dumps(job_data, indent=2)}
Return a JSON object with:
{{
"match_score": "Overall match percentage (0-100)",
"confidence_score": "Analysis confidence level (0-100)",
"skills_analysis": [
{{
"skill": "Name of required skill",
"status": "found/missing/partial",
"found_in_resume": "Where/how skill appears in resume",
"relevance_score": "Relevance score (0-100)"
}}
],
"detailed_analysis": "Detailed analysis of the match",
"improvement_suggestions": ["Specific suggestions to improve match"]
}}"""
}
]
response_text = self._get_llm_response(messages)
clean_json = extract_json_from_text(response_text)
return json.loads(clean_json)
except Exception as e:
logger.error(f"Match calculation error: {e}")
return {
"error": str(e),
"timestamp": self.current_time,
"match_score": 0,
"confidence_score": 0,
"skills_analysis": [],
"detailed_analysis": f"Error: {str(e)}",
"improvement_suggestions": ["An error occurred during analysis"]
}
def generate_cover_letter(self, resume_data: dict, job_data: dict) -> str:
"""Generate a personalized cover letter"""
try:
messages = [
{
"role": "system",
"content": "You are a career coach writing customized cover letters."
},
{
"role": "user",
"content": f"""
Based on the following resume and job description, generate a compelling, personalized cover letter.
Resume:
{json.dumps(resume_data, indent=2)}
Job Description:
{json.dumps(job_data, indent=2)}
Make sure to:
- Mention the candidate's name and relevant experience
- Relate skills to job responsibilities
- End with a call to action
- Avoid placeholders
"""
}
]
return self._get_llm_response(messages).strip()
except Exception as e:
logger.error(f"Cover letter generation error: {e}")
return f"Error generating cover letter: {str(e)}"
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