Add job description analysis and job match scoring

evaluator.py

@@ -37,17 +37,50 @@ def _initialize_llm_provider(self):
         """Initialize the appropriate LLM provider based on the model."""
         self.provider = initialize_llm_provider(self.model_name)
 
-    def _load_evaluation_prompt(self, resume_text: str) -> str:
+    def _load_evaluation_prompt(self, resume_text: str, job_requirements: dict = None) -> str:
         criteria_template = self.template_manager.render_template(
-            "resume_evaluation_criteria", text_content=resume_text
+            "resume_evaluation_criteria",
+            text_content=resume_text,
+            job_requirements=job_requirements,
         )
         if criteria_template is None:
             raise ValueError("Failed to load resume evaluation criteria template")
         return criteria_template
 
-    def evaluate_resume(self, resume_text: str) -> EvaluationData:
+    def _analyze_job_description(self, job_description: str) -> dict:
+        """Analyze the job description to extract key requirements."""
+        try:
+            prompt = self.template_manager.render_template(
+                "job_description_analysis", text_content=job_description
+            )
+            if prompt is None:
+                raise ValueError("Failed to load job description analysis template")
+
+            response = self.provider.chat(
+                model=self.model_name,
+                messages=[{"role": "user", "content": prompt}],
+                options={
+                    "stream": False,
+                    "temperature": 0.2,
+                },
+            )
+            response_text = extract_json_from_response(response["message"]["content"])
+            return json.loads(response_text)
+        except Exception as e:
+            logger.error(f"Error analyzing job description: {str(e)}")
+            return {}
+
+    def evaluate_resume(
+        self, resume_text: str, job_description: str = None
+    ) -> EvaluationData:
         self._last_resume_text = resume_text
-        full_prompt = self._load_evaluation_prompt(resume_text)
+        job_requirements = None
+        if job_description:
+            job_requirements = self._analyze_job_description(job_description)
+
+        full_prompt = self._load_evaluation_prompt(
+            resume_text, job_requirements=job_requirements
+        )
         # logger.info(f"🔤 Evaluation prompt being sent: {full_prompt}")
         try:
             system_message = self.template_manager.render_template(

job_description.txt

@@ -0,0 +1,13 @@
+**Software Engineer Intern**
+
+**Responsibilities:**
+- Develop and maintain web applications using Python and JavaScript.
+- Collaborate with the team to design and implement new features.
+- Write clean, efficient, and well-documented code.
+- Troubleshoot and debug issues.
+
+**Requirements:**
+- Experience with Python and Django.
+- Familiarity with JavaScript, HTML, and CSS.
+- Strong problem-solving skills.
+- Excellent communication and teamwork skills.

models.py

@@ -243,6 +243,7 @@ class Deductions(BaseModel):
 
 class EvaluationData(BaseModel):
     scores: Scores
+    job_match_score: Optional[CategoryScore] = None
     bonus_points: BonusPoints
     deductions: Deductions
     key_strengths: List[str] = Field(min_items=1, max_items=5)

prompts/templates/job_description_analysis.jinja

@@ -0,0 +1,29 @@
+You are an expert in analyzing job descriptions. Your task is to extract the key skills and requirements from the provided job description and categorize them.
+
+**Instructions:**
+
+1.  Read the job description carefully.
+2.  Identify the key technical skills, software, and qualifications required for the role.
+3.  Categorize the extracted information into a structured JSON format.
+
+**JSON Output Structure:**
+
+```json
+{
+  "key_skills": [
+    "skill1",
+    "skill2",
+    "skill3"
+  ],
+  "required_experience": [
+    "experience1",
+    "experience2"
+  ]
+}
+```
+
+**Job Description to Analyze:**
+
+{{ text_content }}
+
+**IMPORTANT: Respond with ONLY the JSON structure specified above. Do not include any other text or explanations.**

prompts/templates/resume_evaluation_criteria.jinja

@@ -29,6 +29,18 @@ You are evaluating a resume for a Software Intern position at HackerRank. Analyz
 - Use GitHub data (if provided in === GITHUB DATA === section) as additional context
 - Use blog data (if provided in === BLOG DATA === section) for technical communication assessment
 
+{% if job_requirements %}
+## JOB DESCRIPTION ALIGNMENT
+- You will be provided with a set of key skills and required experience from a job description.
+- Compare the candidate's resume against these requirements to assess their suitability for the role.
+- Provide a `job_match_score` from 0 to 100, where 100 is a perfect match.
+- The `job_match_score` should be included in the final JSON output.
+
+**Job Requirements:**
+- Key Skills: {{ job_requirements.key_skills | join(', ') }}
+- Required Experience: {{ job_requirements.required_experience | join(', ') }}
+{% endif %}
+
 ## SCORING CRITERIA
 
 ### Open Source (0-35 points)
@@ -174,6 +186,7 @@ Analyze the following resume and provide a JSON response with this EXACT structu
         "production": {"score": 0, "max": 25, "evidence": "string"},
         "technical_skills": {"score": 0, "max": 10, "evidence": "string"}
     },
+    "job_match_score": {"score": 0, "max": 100, "evidence": "string"},
     "bonus_points": {"total": 0, "breakdown": "string"},
     "deductions": {"total": 0, "reasons": "string"},
     "key_strengths": ["strength1", "strength2", "strength3", "strength4", "strength5"],

resume.txt

@@ -0,0 +1,19 @@
+**John Doe**
+john.doe@email.com | (123) 456-7890 | linkedin.com/in/johndoe | github.com/johndoe
+
+**Summary**
+Aspiring Software Engineer with a passion for web development and a strong foundation in Python and JavaScript.
+
+**Experience**
+**Software Developer Intern**, ABC Corp - Anytown, USA (May 2023 - Aug 2023)
+- Developed and maintained a web application using Python, Django, and JavaScript.
+- Collaborated with the team to design and implement new features.
+
+**Education**
+**B.S. in Computer Science**, University of Example - Exampleton, USA (2020 - 2024)
+
+**Skills**
+- **Programming Languages:** Python, JavaScript, HTML, CSS
+- **Frameworks:** Django, React
+- **Databases:** PostgreSQL, MongoDB
+- **Tools:** Git, Docker

score.py

@@ -3,6 +3,7 @@
 import json
 import logging
 import csv
+import argparse
 from pdf import PDFHandler
 from github import fetch_and_display_github_info
 from models import JSONResume, EvaluationData
@@ -71,6 +72,15 @@ def print_evaluation_results(
     # Overall Score
     print(f"\n🎯 OVERALL SCORE: {total_score:.1f}/{max_score}")
 
+    # Job Match Score
+    if hasattr(evaluation, "job_match_score") and evaluation.job_match_score:
+        print("\n" + "-" * 80)
+        print("🤝 JOB DESCRIPTION ALIGNMENT:")
+        js_score = evaluation.job_match_score
+        print(f"   Match Score: {js_score.score}/{js_score.max}")
+        print(f"   Evidence: {js_score.evidence}")
+        print("-" * 80)
+
     # Detailed Scores
     print("\n📈 DETAILED SCORES:")
     print("-" * 60)
@@ -160,7 +170,10 @@ def print_evaluation_results(
 
 
 def _evaluate_resume(
-    resume_data: JSONResume, github_data: dict = None, blog_data: dict = None
+    resume_data: JSONResume,
+    github_data: dict = None,
+    blog_data: dict = None,
+    job_description: str = None,
 ) -> Optional[EvaluationData]:
     """Evaluate the resume using AI and display results."""
 
@@ -181,7 +194,9 @@ def _evaluate_resume(
         resume_text += blog_text
 
     # Evaluate the enhanced resume
-    evaluation_result = evaluator.evaluate_resume(resume_text)
+    evaluation_result = evaluator.evaluate_resume(
+        resume_text, job_description=job_description
+    )
 
     # print(evaluation_result)
 
@@ -197,7 +212,7 @@ def find_profile(profiles, network):
     )
 
 
-def main(pdf_path):
+def main(pdf_path, job_description_path=None):
     # Create cache filename based on PDF path
     cache_filename = (
         f"cache/resumecache_{os.path.basename(pdf_path).replace('.pdf', '')}.json"
@@ -206,6 +221,13 @@ def main(pdf_path):
         f"cache/githubcache_{os.path.basename(pdf_path).replace('.pdf', '')}.json"
     )
 
+    job_description = None
+    if job_description_path:
+        if os.path.exists(job_description_path):
+            job_description = Path(job_description_path).read_text()
+        else:
+            print(f"Warning: Job description file not found at '{job_description_path}'")
+
     # Check if cache exists and we're in development mode
     if DEVELOPMENT_MODE and os.path.exists(cache_filename):
         print(f"Loading cached data from {cache_filename}")
@@ -226,7 +248,7 @@ def main(pdf_path):
             os.makedirs(os.path.dirname(cache_filename), exist_ok=True)
             Path(cache_filename).write_text(
                 json.dumps(resume_data.model_dump(), indent=2, ensure_ascii=False),
-                encoding='utf-8'
+                encoding="utf-8",
             )
 
     # Check if cache exists and we're in development mode
@@ -252,10 +274,10 @@ def main(pdf_path):
             os.makedirs(os.path.dirname(github_cache_filename), exist_ok=True)
             Path(github_cache_filename).write_text(
                 json.dumps(github_data, indent=2, ensure_ascii=False),
-                encoding='utf-8'
+                encoding="utf-8",
             )
 
-    score = _evaluate_resume(resume_data, github_data)
+    score = _evaluate_resume(resume_data, github_data, job_description=job_description)
 
     # Get candidate name for display
     candidate_name = os.path.basename(pdf_path).replace(".pdf", "")
@@ -297,13 +319,21 @@ def main(pdf_path):
 
 
 if __name__ == "__main__":
-    if len(sys.argv) < 2:
-        print("Usage: python score.py <pdf_path>")
-        exit(1)
-    pdf_path = sys.argv[1]
+    parser = argparse.ArgumentParser(
+        description="Evaluate a resume PDF and optionally compare it against a job description."
+    )
+    parser.add_argument("pdf_path", help="Path to the resume PDF file.")
+    parser.add_argument(
+        "-j",
+        "--job-description",
+        dest="job_description_path",
+        help="Path to a text file containing the job description.",
+        required=False,
+    )
+    args = parser.parse_args()
 
-    if not os.path.exists(pdf_path):
-        print(f"Error: File '{pdf_path}' does not exist.")
+    if not os.path.exists(args.pdf_path):
+        print(f"Error: File '{args.pdf_path}' does not exist.")
         exit(1)
 
-    main(pdf_path)
+    main(args.pdf_path, args.job_description_path)