File size: 4,358 Bytes
4655858 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 | from typing import Any, Dict, Optional
from metrics import log_metric
class RenderMicroFunction:
def run(self, data: Dict[str, Any]) -> Dict[str, Any]:
draft = data.get("draft", "")
qa_result = data.get("qa_result", "")
critique = data.get("critique", "")
enriched_data = data.get("enriched", {})
quality_score = data.get("quality_score")
try:
# Create comprehensive markdown output
rendered = self._create_final_output(draft, qa_result, critique, enriched_data, quality_score)
# Add bucket processing
bucket_facts = data.get("bucket_facts", {})
bucket_markdown = ""
if bucket_facts:
from bucket_map import map_facts
from render_buckets import render_buckets
buckets = map_facts(bucket_facts)
bucket_markdown = render_buckets(bucket_facts, buckets)
log_metric("bucket_missing", {
"empty_buckets": len([k for k, v in buckets.items() if not v or (len(v) == 1 and "research needed" in v[0].lower())])
})
log_metric("render_success", {
"total_length": len(rendered),
"has_qa": bool(qa_result),
"has_critique": bool(critique),
"quality_score": quality_score,
"has_buckets": bool(bucket_markdown)
})
return {**data, "rendered_markdown": rendered, "bucket_markdown": bucket_markdown}
except Exception as e:
log_metric("render_error", {"error": str(e)})
fallback = f"# Job Analysis Results\n\n{draft}\n\n---\n\nQA: {qa_result}\n\nCritique: {critique}"
return {**data, "rendered_markdown": fallback, "bucket_markdown": ""}
def _create_final_output(self, draft: str, qa_result: str, critique: str, enriched_data: Dict, quality_score: Optional[float] = None) -> str:
"""Create comprehensive final output"""
# Header with quality indicator
quality_indicator = ""
if quality_score:
if quality_score >= 8:
quality_indicator = "🟢 High Quality"
elif quality_score >= 6:
quality_indicator = "🟡 Good Quality"
else:
quality_indicator = "🔴 Needs Improvement"
# Extract key job info for header
role = enriched_data.get("role", "Unknown Role")
company = enriched_data.get("company", "Unknown Company")
level = enriched_data.get("level", "")
header = f"""# 🎯 {role} at {company}
{f"**Level**: {level}" if level else ""}
{f"**Quality**: {quality_indicator}" if quality_indicator else ""}
---
"""
# Main content (the draft)
main_content = draft if draft else "Content generation failed."
# QA and Critique sections (collapsible)
qa_section = ""
if qa_result and qa_result != "QA skipped due to draft errors.":
qa_section = f"""
<details>
<summary>📋 Quality Assurance Results</summary>
{qa_result}
</details>
"""
critique_section = ""
if critique and critique != "Critique skipped due to draft errors.":
critique_section = f"""
<details>
<summary>🔍 Expert Critique</summary>
{critique}
</details>
"""
# Footer with metadata
tech_stack = enriched_data.get("tech_stack", [])
work_mode = enriched_data.get("work_mode", "")
salary_range = enriched_data.get("salary_range", "")
metadata = []
if tech_stack:
metadata.append(f"**Tech Stack**: {', '.join(tech_stack[:5])}")
if work_mode:
metadata.append(f"**Work Mode**: {work_mode}")
if salary_range and salary_range != "Not specified":
metadata.append(f"**Salary**: {salary_range}")
footer = ""
if metadata:
footer = f"""
---
## 📊 Quick Facts
{chr(10).join([f"- {item}" for item in metadata])}
"""
# Combine all sections
final_output = header + main_content + qa_section + critique_section + footer
return final_output |