Job analysis is working

frontend/src/components/JobMatchAnalysis.tsx

@@ -49,7 +49,7 @@ const JobMatchAnalysis: React.FC<JobAnalysisProps> = (props: JobAnalysisProps) =
   const { apiClient } = useAuth();
   const theme = useTheme();
   const [jobRequirements, setJobRequirements] = useState<JobRequirements | null>(null);
-  const [requirements, setRequirements] = useState<string[]>([]);
+  const [requirements, setRequirements] = useState<{ requirement: string, domain: string }[]>([]);
   const [skillMatches, setSkillMatches] = useState<SkillMatch[]>([]);
   const [creatingSession, setCreatingSession] = useState<boolean>(false);
   const [loadingRequirements, setLoadingRequirements] = useState<boolean>(false);
@@ -102,23 +102,24 @@ const JobMatchAnalysis: React.FC<JobAnalysisProps> = (props: JobAnalysisProps) =
             console.log(`onMessage: ${msg.type}`, msg);
             if (msg.type === "response") {
               const incoming: any = toCamelCase<JobRequirements>(JSON.parse(msg.content || ''));
-              const requirements: string[] = ['technicalSkills', 'experienceRequirements'].flatMap((type) => {
+              const requirements: { requirement: string, domain: string }[] = ['technicalSkills', 'experienceRequirements'].flatMap((domain) => {
                 return ['required', 'preferred'].flatMap((level) => {
-                  return incoming[type][level].map((s: string) => s);
+                  return incoming[domain][level].map((s: string) => { return { requirement: s, domain: domain }; });
                 })
               });
-              ['softSkills', 'experience', 'education', 'certifications', 'preferredAttributes'].forEach(l => {
+              ['softSkills', 'experience', 'education', 'certifications', 'preferredAttributes'].forEach(domain => {
-                if (incoming[l]) {
+                if (incoming[domain]) {
-                  incoming[l].forEach((s: string) => requirements.push(s));
+                  incoming[domain].forEach((s: string) => requirements.push({ requirement: s, domain: domain }));
                 }
               });
 
-        // Initialize skill matches with pending status
               const initialSkillMatches = requirements.map(req => ({
-                requirement: req,
+                requirement: req.requirement,
+                domain: req.domain,
                 status: 'pending' as const,
                 matchScore: 0,
                 assessment: '',
+                description: '',
                 citations: []
               }));
 
@@ -167,8 +168,27 @@ const JobMatchAnalysis: React.FC<JobAnalysisProps> = (props: JobAnalysisProps) =
       // Process requirements one by one
       for (let i = 0; i < requirements.length; i++) {
         try {
-          const match: SkillMatch = await apiClient.candidateMatchForRequirement(candidate.id || '', requirements[i]);
+          const result: any = await apiClient.candidateMatchForRequirement(candidate.id || '', requirements[i].requirement);
-          console.log(match);
+          const skillMatch = result.skillMatch;
+          let matchScore: number = 0;
+          switch (skillMatch.evidenceStrength) {
+            case "STRONG": matchScore = 100; break;
+            case "MODERATE": matchScore = 75; break;
+            case "WEAK": matchScore = 50; break;
+            case "NONE": matchScore = 0; break;
+          }
+          if (skillMatch.evidenceStrength == "NONE" && skillMatch.citations.length > 3) {
+            matchScore = Math.min(skillMatch.citations.length * 8, 40);
+          }
+          const match: SkillMatch = {
+            status: "complete",
+            matchScore,
+            domain: requirements[i].domain,
+            requirement: skillMatch.skill,
+            assessment: skillMatch.assessment,
+            citations: skillMatch.evidenceDetails.map((evidence: any) => { return { source: evidence.source, text: evidence.quote, context: evidence.context } }),
+            description: skillMatch.description
+          };
           setSkillMatches(prev => {
             const updated = [...prev];
             updated[i] = match;
@@ -341,9 +361,14 @@ const JobMatchAnalysis: React.FC<JobAnalysisProps> = (props: JobAnalysisProps) =
                 }}>
                   <Box sx={{ display: 'flex', alignItems: 'center' }}>
                     {getStatusIcon(match.status, match.matchScore)}
-                    <Typography sx={{ ml: 1, fontWeight: 'medium' }}>
+                    <Box sx={{ display: "flex", flexDirection: "column", gap: 0, p: 0, m: 0 }}>
+                      <Typography sx={{ ml: 1, mb: 0, fontWeight: 'medium', marginBottom: "0px !important" }}>
                       {match.requirement}
                     </Typography>
+                      <Typography variant="caption" sx={{ ml: 1, fontWeight: 'light' }}>
+                        {match.domain}
+                      </Typography>
+                    </Box>
                   </Box>
                   
                   {match.status === 'complete' ? (
@@ -356,6 +381,12 @@ const JobMatchAnalysis: React.FC<JobAnalysisProps> = (props: JobAnalysisProps) =
                         minWidth: 90
                       }} 
                     />
+                  ) : match.status === 'waiting' ? (
+                    <Chip
+                      label="Waiting..."
+                      size="small"
+                      sx={{ bgcolor: "rgb(189, 173, 85)", color: 'white', minWidth: 90 }}
+                    />
                   ) : match.status === 'pending' ? (
                     <Chip 
                       label="Analyzing..." 
@@ -387,7 +418,7 @@ const JobMatchAnalysis: React.FC<JobAnalysisProps> = (props: JobAnalysisProps) =
                 ) : (
                   <Box>
                     <Typography variant="h6" gutterBottom>
-                      Assessment:
+                          Assessment
                     </Typography>
                     
                     <Typography paragraph sx={{ mb: 3 }}>
@@ -395,9 +426,8 @@ const JobMatchAnalysis: React.FC<JobAnalysisProps> = (props: JobAnalysisProps) =
                     </Typography>
                     
                     <Typography variant="h6" gutterBottom>
-                      Supporting Evidence:
+                          Supporting Evidence
-                    </Typography>
+                        </Typography>
-                    
                         {match.citations && match.citations.length > 0 ? (
                       match.citations.map((citation, citIndex) => (
                         <Card 
@@ -413,17 +443,20 @@ const JobMatchAnalysis: React.FC<JobAnalysisProps> = (props: JobAnalysisProps) =
                             <Typography variant="body1" component="div" sx={{ mb: 1, fontStyle: 'italic' }}>
                               "{citation.text}"
                             </Typography>
-                            <Box sx={{ display: 'flex', justifyContent: 'space-between', alignItems: 'center' }}>
+                            <Box sx={{ display: 'flex', justifyContent: 'space-between', alignItems: 'flex-start', flexDirection: "column" }}>
+                              <Typography variant="body2" color="text.secondary">
+                                Relevance: {citation.context}
+                              </Typography>
                               <Typography variant="caption" color="text.secondary">
                                 Source: {citation.source}
                               </Typography>
-                              <Chip 
+                              {/* <Chip
-                                size="small" 
+                                size="small"
                                 label={`Relevance: ${citation.relevance}%`}
-                                sx={{ 
+                                sx={{
                                   bgcolor: theme.palette.grey[200],
                                 }}
-                              />
+                              /> */}
                             </Box>
                           </CardContent>
                         </Card>
@@ -433,6 +466,13 @@ const JobMatchAnalysis: React.FC<JobAnalysisProps> = (props: JobAnalysisProps) =
                         No specific evidence found in candidate's profile.
                       </Typography>
                     )}
+                        <Typography variant="h6" gutterBottom>
+                          Skill description
+                        </Typography>
+                        <Typography paragraph>
+                          {match.description}
+                        </Typography>
+
                   </Box>
                 )}
               </AccordionDetails>

frontend/src/types/types.ts

@@ -1,6 +1,6 @@
 // Generated TypeScript types from Pydantic models
 // Source: src/backend/models.py
-// Generated on: 2025-06-04T03:59:11.250216
+// Generated on: 2025-06-04T05:16:43.020718
 // DO NOT EDIT MANUALLY - This file is auto-generated
 
 // ============================
@@ -49,7 +49,7 @@ export type SearchType = "similarity" | "mmr" | "hybrid" | "keyword";
 
 export type SkillLevel = "beginner" | "intermediate" | "advanced" | "expert";
 
-export type SkillStatus = "pending" | "complete" | "error";
+export type SkillStatus = "pending" | "complete" | "waiting" | "error";
 
 export type SocialPlatform = "linkedin" | "twitter" | "github" | "dribbble" | "behance" | "website" | "other";
 
@@ -391,6 +391,7 @@ export interface ChromaDBGetResponse {
 export interface Citation {
   text: string;
   source: string;
+  context: string;
   relevance: number;
 }
 
@@ -835,10 +836,12 @@ export interface SkillAssessment {
 
 export interface SkillMatch {
   requirement: string;
-  status: "pending" | "complete" | "error";
+  domain: string;
+  status: "pending" | "complete" | "waiting" | "error";
   matchScore: number;
   assessment: string;
   citations?: Array<Citation>;
+  description: string;
 }
 
 export interface SocialLink {

src/backend/agents/skill_match.py

@@ -73,7 +73,8 @@ a SPECIFIC skill based solely on their resume and supporting evidence.
     "skill": "{skill}",
     "evidence_found": true/false,
     "evidence_strength": "STRONG/MODERATE/WEAK/NONE",
-    "description": "short (two to three sentence) description of what {skill} means with a concise example of what you're looking for",
+    "assessment": "short (one to two sentence) assessment of the candidate's proficiency with {skill}",
+    "description": "short (two to three sentence) description of what the {skill} is, independent of whether the candidate has that skill or not",
     "evidence_details": [
       {{
         "source": "resume section/position/project",
@@ -182,20 +183,9 @@ JSON RESPONSE:"""
             return
 
         json_str = self.extract_json_from_text(skill_assessment.content)
-        skill_match = json_str#: SkillMatch | None = None
         skill_assessment_data = ""
         try:
-            skill_assessment_data = json.loads(json_str)
+            skill_assessment_data = json.loads(json_str).get("skill_assessment", {})
-            match_level = (
-                skill_assessment_data
-                .get("skill_assessment", {})
-                .get("evidence_strength", "UNKNOWN")
-            )
-            skill_description = (
-                skill_assessment_data
-                .get("skill_assessment", {})
-                .get("description", "")
-            )
         except json.JSONDecodeError as e:
             status_message.status = ChatStatusType.ERROR
             status_message.content = f"Failed to parse Skill assessment JSON: {str(e)}\n\n{skill_assessment_data}"
@@ -215,15 +205,9 @@ JSON RESPONSE:"""
             logger.error(f"⚠️ {status_message.content}")
             yield status_message
             return
-        if skill_match is None:
-            status_message.status = ChatStatusType.ERROR
-            status_message.content = "Skill assessment analysis failed to produce valid data."
-            logger.error(f"⚠️ {status_message.content}")
-            yield status_message
-            return
         status_message.status = ChatStatusType.DONE
         status_message.type = ChatMessageType.RESPONSE
-        status_message.content = skill_match
+        status_message.content = json.dumps(skill_assessment_data)
         yield status_message
 
         logger.info(f"✅ Skill assessment completed successfully.")

src/backend/main.py

@@ -3395,8 +3395,8 @@ async def get_candidate_skill_match(
                     status_code=500,
                     content=create_error_response("NO_MATCH", "No skill match found for the given requirement")
                 )
-            skill_match = skill_match.content.strip()
+            skill_match = json.loads(skill_match.content)
-            logger.info(f"✅ Skill match found for candidate {candidate.id}: {skill_match}")
+            logger.info(f"✅ Skill match found for candidate {candidate.id}: {skill_match['evidence_strength']}")
 
         return create_success_response({
             "candidateId": candidate.id,

src/backend/models.py

@@ -87,19 +87,23 @@ class Requirements(BaseModel):
 class Citation(BaseModel):
     text: str
     source: str
+    context: str
     relevance: int # 0-100 scale
 
 class SkillStatus(str, Enum):
     PENDING = "pending"
     COMPLETE = "complete"
+    WAITING = "waiting"
     ERROR = "error"
 
 class SkillMatch(BaseModel):
     requirement: str
+    domain: str
     status: SkillStatus
     match_score: int = Field(..., alias='matchScore')
     assessment: str
     citations: List[Citation] = Field(default_factory=list)
+    description: str
     model_config = {
         "populate_by_name": True   # Allow both field names and aliases        
     }