feat: target role option

levelup/app.py

@@ -1,6 +1,6 @@
 import json
 import re
-from typing import Any, cast
+from typing import Any, Optional, cast
 
 import google.generativeai as genai
 import pandas as pd  # type: ignore[import-untyped]
@@ -41,8 +41,10 @@ def _extract_json_block(raw_text: str) -> str | None:
     return None
 
 
-def analyzecv_pdf_withllm(text: str, report_language: str) -> dict[str, Any] | None:
-    prompt = get_resume_analysis_prompt(text, report_language)
+def analyzecv_pdf_withllm(
+    text: str, report_language: str, target_role: Optional[str] = None
+) -> dict[str, Any] | None:
+    prompt = get_resume_analysis_prompt(text, report_language, target_role)
     try:
         response = Model.generate_content(prompt)
         raw_text = (response.text or "").strip()
@@ -334,8 +336,62 @@ def display_analysis_tabs(result: dict[str, Any]) -> None:
             index=language_options.index("English"),
         )
 
+        role_options = [
+            "No specific target role",
+            "Data Scientist",
+            "Data Analyst",
+            "Data Engineer",
+            "Machine Learning Engineer",
+            "AI Engineer",
+            "MLOps Engineer",
+            "Deep Learning Engineer",
+            "Business Intelligence Analyst",
+            "Data Architect",
+            "AI Product Manager",
+            "Software Engineer",
+            "Backend Engineer",
+            "Frontend Engineer",
+            "Full Stack Engineer",
+            "Mobile Developer",
+            "DevOps Engineer",
+            "Cloud Engineer",
+            "Solution Architect",
+            "Application Developer",
+            "QA Engineer",
+            "Test Automation Engineer",
+            "Manual Tester",
+            "Cybersecurity Analyst",
+            "Security Engineer",
+            "Security Architect",
+            "SOC Analyst",
+            "Penetration Tester",
+            "Product Manager",
+            "Product Owner",
+            "Scrum Master",
+            "Project Manager",
+            "System Administrator",
+            "Network Engineer",
+            "IT Support Specialist",
+            "Platform Engineer",
+            "UX Designer",
+            "UI Designer",
+            "Product Designer",
+        ]
+
+        selected_role_label = st.selectbox(
+            "Choose a target role for the report",
+            role_options,
+            index=0,
+        )
+
+        selected_role: Optional[str]
+        if selected_role_label == "No specific target role":
+            selected_role = None
+        else:
+            selected_role = selected_role_label
+
         if st.button("Analyze Resume"):
             with st.spinner("Analyzing Resume..."):
-                result = analyzecv_pdf_withllm(text, selected_language)
+                result = analyzecv_pdf_withllm(text, selected_language, selected_role)
                 if result:
                     display_analysis_tabs(result)

levelup/prompts.py

@@ -1,10 +1,83 @@
-def get_resume_analysis_prompt(text: str, report_language: str) -> str:
-    """Builds the full LLM prompt for resume analysis.
+def get_resume_analysis_prompt(
+    text: str, report_language: str, target_role: str | None = None
+) -> str:
+    """Builds the full LLM prompt for resume analysis, with optional strong target-role conditioning."""
+
+    notes = {
+        "domain": "",
+        "competency": "",
+        "insights": "",
+        "recommendations": "",
+        "missing_skills": "",
+        "benchmarking": "",
+        "summary": "",
+        "strengths": "",
+    }
+
+    target_role_block = ""
+    if target_role is not None:
+        target_role_block = f"""
+        TARGET ROLE FOCUS:
+        - The candidate explicitly targets the role: "{target_role}".
+        - All scores, justifications, insights, and recommendations must be evaluated relative to global expectations for "{target_role}" at the candidate’s apparent seniority level.
+        - Every numeric score (domain_scores, competency_scores, overall_score, role_suitability scores) must reflect how strong the candidate is for "{target_role}" (not for their past or current role).
+        - Every justification, strength, observation, insight, recommendation, and benchmarking comment must explicitly refer to the candidate’s fit for "{target_role}".
+        - You may mention other suitable roles or domains, but always describe them in relation to the candidate’s primary fit for "{target_role}".
+
+        CONSISTENCY REQUIREMENTS FOR SCORES:
+        - The "overall_score" MUST always represent the candidate’s overall fit for "{target_role}" only.
+        - In "overall_summary.role_suitability":
+          - The FIRST item MUST have "role": "{target_role}".
+          - Its "score" MUST be EXACTLY equal to "overall_score".
+        - NO other role_suitability entry may have a score higher than "overall_score".
+        - When evaluating suitability for "{target_role}", heavily weight core role-specific capabilities.
+        - If the candidate’s experience is primarily in a different domain than "{target_role}" (e.g., Data Science vs Software Engineering), 
+          and there is limited direct evidence of core "{target_role}" responsibilities (such as algorithms/data structures, software design, testing, CI/CD, scalable services), then:
+          - The "{target_role}" role_suitability score (and thus "overall_score") MUST NOT exceed 70.
+        - Scores of 80 or above for "{target_role}" require clear, explicit, repeated evidence of strong performance in core "{target_role}" responsibilities, not just adjacent or related work.
+"""
+        notes["domain"] = f"""
+- For each domain, interpret the score as: "How strong is this candidate in this domain while working as "{target_role}"?".
+- At least one domain must clearly correspond to "{target_role}" (or a closely related domain label), and the justification must reference "{target_role}" explicitly.
+"""
+        notes["competency"] = f"""
+- Each competency score must be benchmarked against typical expectations for "{target_role}" at the candidate’s current level.
+- Strengths and observations must clearly explain how they support or limit performance as "{target_role}".
+"""
+        notes["insights"] = f"""
+- Explicitly classify the candidate’s current fit for "{target_role}" as strong, moderate, or weak, with clear reasoning.
+- State whether "{target_role}" is realistic in the short to medium term and under which conditions.
+"""
+        notes["recommendations"] = f"""
+- All recommendations must be framed as concrete actions that increase the candidate’s competitiveness for "{target_role}".
+"""
+        notes["missing_skills"] = f"""
+- When identifying missing skills, prioritize gaps that are most important for succeeding as "{target_role}" in the global market at this level.
+"""
+        notes["benchmarking"] = f"""
+- Compare the candidate specifically with typical professionals working as "{target_role}" at a similar career level, and state whether they are above, at, or below this benchmark.
+"""
+        notes["summary"] = f"""
+- Clearly state overall fit for "{target_role}", the main gaps that limit performance in this role, and whether the target is realistic now or requires significant upskilling.
+"""
+        notes["strengths"] = f"""
+-  Only list the person's strengths that align with the {target_role} role. Do not list strengths that are unrelated to the {target_role} role.
+- In all strength-related sections (including "overall_summary.key_strengths"), list strengths that directly support success as "{target_role}" or clearly demonstrate transferable potential toward becoming a stronger "{target_role}" candidate.
+- Avoid listing strengths that are only relevant to the candidate’s original role and do not materially contribute to performance as "{target_role}".
+- If a skill is strongly domain-specific to the candidate’s original role (e.g., ML/AI/LLM tooling for a Data Scientist), DO NOT list it as a strength for the target role unless it directly supports core responsibilities of "{target_role}" (such as scalable software development, software architecture, performance, reliability, system design, or production engineering).
+- Focus on strengths that are clearly relevant to "{target_role}". Do not mention strengths that are not suitable for the target role.
+"""
+    else:
+        notes["strengths"] = """
+- In all strength-related sections (including "overall_summary.key_strengths"), list strengths that best summarize the candidate’s value across their top likely roles inferred from the CV.
+- Focus on strengths that are robust and transferable across multiple plausible career paths (e.g., strong programming fundamentals, problem solving, ownership, communication).
+- Avoid overly narrow, niche strengths that are locked into a single, highly specific role unless that role is clearly dominant in the CV.
+"""
+
+    primary_role_for_json = (
+        target_role if target_role is not None else "Primary Likely Role"
+    )
 
-    Guides the model to analyze a CV across language, domain fit,
-    competencies, insights, recommendations, benchmarking, and summary.
-    Returns the complete formatted prompt string.
-    """
     return f"""
 You are a globally experienced HR and career evaluation expert with deep cross-industry insight. You will perform an extremely detailed, holistic analysis of the CV provided below. Go beyond numeric scoring — offer professional interpretation, inferences, and personalized guidance based on the profile.
 
@@ -26,44 +99,70 @@ def get_resume_analysis_prompt(text: str, report_language: str) -> str:
 - Pick one specific platform/standard per item instead of categories.
 - Exclude anything already present in the CV. Deduplicate closely related items.
 
+{target_role_block}
+
 * 1. LANGUAGE DETECTION
 Identify the dominant language of the CV.
 
 * 2. CAREER DOMAIN MATCHING
 Identify the top 3 most suitable career domains for this candidate.
 For each domain:
-- Give a score out of 100
-- Justify why the candidate fits that domain (based on experience, skills, education, etc.)
-- Optionally mention related roles the candidate could consider
+- Give a score out of 100.
+- Justify why the candidate fits that domain (based on experience, skills, education, and impact).
+- Optionally mention related roles the candidate could consider.
+{notes["domain"]}
 
 * 3. COMPETENCY EVALUATION
-Evaluate the candidate across 10 dimensions. For each, give:
-- A score out of 100
-- Specific strengths and examples from the CV
-- Observations or red flags (if any)
+Evaluate the candidate across 10 dimensions (for example: Core Technical Skills, Tools & Technologies, Problem Solving, Business Impact, Communication, Leadership, Collaboration, Learning Agility, Domain Knowledge, Delivery & Reliability). For each dimension:
+- Assign a score out of 100.
+- Describe concrete strengths and examples from the CV.
+- Note any observations or red flags (e.g., lack of scale, missing ownership, shallow impact).
+{notes["competency"]}
 
 * 4. STRATEGIC INSIGHTS & INTERPRETATION
-- Based on the full CV, what type of roles is this candidate most suited for now?
-- What future roles could be targeted with slight improvements?
-- Are there signs of underutilized potential?
-- Does the profile indicate a specialist or generalist tendency?
-- Are there inconsistencies or missing data that should be improved?
+Based on the full CV:
+- Which types of roles is this candidate most suited for now?
+- Which future roles could be realistic with incremental improvements?
+- Are there signs of underutilized potential (e.g., skills that are not fully leveraged)?
+- Does the profile suggest a specialist or generalist tendency?
+- Are there any inconsistencies, gaps, or missing data that should be clarified or improved?
+{notes["insights"]}
 
 * 5. DEVELOPMENT RECOMMENDATIONS
-Provide clear, practical and personalized suggestions for how the candidate can improve:
-- Skills, certifications, degrees
-- Portfolio, communication, network
+Provide clear, practical, and personalized suggestions on how the candidate can strengthen their profile:
+- Skills, tools, and methods to learn or deepen.
+- Certifications or degrees that would be valuable.
+- Portfolio, project, or publication ideas.
+- Improvements in communication, stakeholder management, and professional networking.
+{notes["recommendations"]}
 
-* 6. MISSING SKILLS & EXPERIENCE MISMATCH  
-Explicitly identify:  
-- Up to 5 missing or weakly represented skills relevant to the candidate’s likely target roles.
-- Any mismatched experience (e.g., experience unrelated to stated goals or domain misalignment).
+* 6. MISSING SKILLS & EXPERIENCE MISMATCH
+Explicitly identify:
+- Up to 5 missing or weakly represented skills that are relevant to the candidate’s likely or explicit target roles.
+- Any mismatched experience (e.g., long periods in unrelated domains, activities that do not support their stated or implied career direction) and how this affects perceived fit.
+{notes["missing_skills"]}
 
 * 7. COMPARATIVE BENCHMARKING
-Compare the candidate’s profile against general industry and global professional standards at a similar career level. Use widely recognized benchmarks or norms (skills depth, experience breadth, impact level). Do not reference specific datasets or sources; base this on general market understanding. Indicate whether the candidate appears above, average, or below such benchmarks.
+Compare the candidate’s profile against general industry and global professional standards at a similar career level. Consider:
+- Depth and breadth of skills.
+- Scope and impact of experience.
+- Ownership, autonomy, and complexity of work.
+Indicate whether the candidate appears above, at, or below typical benchmarks.
+{notes["benchmarking"]}
+
+* 8. KEY STRENGTHS INSIGHTS (key_strengths)
+Provide 3–5 high-level strategic insights about the candidate’s profile, career trajectory, and market positioning.
+- Focus on strengths that are most relevant to the candidate’s current and near-future roles.
+{notes["strengths"]}
 
-* 8. OVERALL SUMMARY  
-Provide a concise synthesis of the entire evaluation. Include the total score, key strengths, areas for improvement, and an assessment of overall talent potential.
+* 9. OVERALL SUMMARY
+Provide a concise synthesis of the entire evaluation. Include:
+- A single overall score (0–100) summarizing the profile.
+- Key strengths.
+- Priority areas for improvement.
+- An overall view of talent potential (High / Moderate / Needs Development).
+- A short assessment of the candidate’s role suitability across 2–3 key roles.
+{notes["summary"]}
 
 Absolutely follow the JSON format shown below. Do not add any text, comments, or explanations outside the JSON structure.
 
@@ -81,13 +180,11 @@ def get_resume_analysis_prompt(text: str, report_language: str) -> str:
     "Recommendation 2"
   ],
   "missing_skills": [
-    {{"skill": "Python", "priority": "Critical"}},
-    {{"skill": "PySpark", "priority": "Nice to have"}},
-    {{"skill": "Docker", "priority": "Important"}}
+    {{"skill": "ExampleSkill1", "priority": "Critical"}},
+    {{"skill": "ExampleSkill2", "priority": "Important"}}
   ],
   "mismatched_experience": [
-    "Example 1",
-    "Example 2"
+    "Example of experience that does not fully align with the candidate’s direction"
   ],
   "comparative_benchmarking": "Paragraph comparing this candidate against general industry standards. If not applicable, leave empty.",
   "overall_summary": {{
@@ -96,8 +193,8 @@ def get_resume_analysis_prompt(text: str, report_language: str) -> str:
     "areas_to_improve": ["Weakness 1", "Weakness 2"],
     "talent_potential": "High / Moderate / Needs Development",
     "role_suitability": [
-      {{"role": "Data Scientist", "score": 82}},
-      {{"role": "Machine Learning Engineer", "score": 78}}
+      {{"role": "{primary_role_for_json}", "score": 82}},
+      {{"role": "Secondary Likely or Related Role", "score": 78}}
     ]
   }}
 }}