feat: implement YAML-based configuration, .env support, and customizable prompt templates for AI analysis

internal/analyzer/gemini.go

@@ -7,40 +7,49 @@ import (
 	"fmt"
 	"io"
 	"net/http"
+	"strings"
 	"time"
 )
 
 type GeminiAnalyzer struct {
-	apiKey string
-	client *http.Client
+	apiKey             string
+	systemPrompt       string
+	userPromptTemplate string
+	client             *http.Client
 }
 
-func NewGeminiAnalyzer(apiKey string) *GeminiAnalyzer {
+func NewGeminiAnalyzer(apiKey, systemPrompt, userPromptTemplate string) *GeminiAnalyzer {
 	return &GeminiAnalyzer{
-		apiKey: apiKey,
-		client: &http.Client{Timeout: 30 * time.Second},
+		apiKey:             apiKey,
+		systemPrompt:       systemPrompt,
+		userPromptTemplate: userPromptTemplate,
+		client:             &http.Client{Timeout: 30 * time.Second},
 	}
 }
 
+func (g *GeminiAnalyzer) buildUserPrompt(jobTitle, rawContent string) string {
+	if g.userPromptTemplate == "" {
+		return fmt.Sprintf("Analyze the following job description for candidate Pengzhan Hao.\n\nJob Title: %s\n\nJob Content:\n%s\n\nRespond with structured JSON.", jobTitle, rawContent)
+	}
+	res := strings.ReplaceAll(g.userPromptTemplate, "{{.JobTitle}}", jobTitle)
+	res = strings.ReplaceAll(res, "{{.JobContent}}", rawContent)
+	return res
+}
+
 func (g *GeminiAnalyzer) AnalyzeJD(ctx context.Context, jobTitle, rawContent string) (*AnalysisOutput, error) {
-	prompt := fmt.Sprintf(`You are an expert technical resume screener. Analyze the following job description for the title "%s".
-Provide a structured JSON output with 3 fields:
-- "matched_highlights": list of 3-5 core technical skills/requirements matched (e.g. ["Go (Golang)", "Microservices", "REST APIs"]).
-- "missing_highlights": list of 1-3 missing skills, experience gaps, or extra requirements (e.g. ["10+ yrs experience", "Kubernetes administration"]).
-- "tailoring_suggestions": list of 2-3 specific action points for tailoring a resume to this JD.
+	sysPrompt := g.systemPrompt
+	if sysPrompt == "" {
+		sysPrompt = "You are an expert technical resume screener. Respond STRICTLY with valid JSON matching {\"ai_score\": 85.0, \"is_qualified\": true, \"summary\": \"...\", \"matched_highlights\": [...], \"missing_highlights\": [...], \"tailoring_suggestions\": [...]}."
+	}
 
-Job Title: %s
-Job Content:
-%s
-
-Respond STRICTLY with valid JSON matching the format:
-{"matched_highlights": [...], "missing_highlights": [...], "tailoring_suggestions": [...]}`, jobTitle, jobTitle, rawContent)
+	userPrompt := g.buildUserPrompt(jobTitle, rawContent)
+	fullPrompt := fmt.Sprintf("%s\n\n%s", sysPrompt, userPrompt)
 
 	reqBody := map[string]interface{}{
 		"contents": []map[string]interface{}{
 			{
 				"parts": []map[string]string{
-					{"text": prompt},
+					{"text": fullPrompt},
 				},
 			},
 		},
@@ -102,5 +111,15 @@ Respond STRICTLY with valid JSON matching the format:
 		return nil, fmt.Errorf("failed to parse Gemini JSON output: %w", err)
 	}
 
+	if output.AIScore < 0 {
+		output.AIScore = 0
+	} else if output.AIScore > 100 {
+		output.AIScore = 100
+	}
+
+	if output.AIScore >= 60.0 {
+		output.IsQualified = true
+	}
+
 	return &output, nil
 }