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eb6064a | 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 | from typing import Dict, List, Optional
from .hf_api import hf_summarize
def summarize_resume(resume_data: Dict, *, hf_model: str, hf_token: Optional[str]) -> str:
"""
Generate a summary of the resume using a Hugging Face text summarization model
via the Inference API.
Args:
resume_data: Parsed resume dictionary containing 'raw_text' and optional fields.
hf_model: Model repo id, e.g. 'facebook/bart-large-cnn' or 'sshleifer/distilbart-cnn-12-6'.
hf_token: Optional HF API token. Required for rate limits or private models.
Returns:
str: Generated summary text.
"""
name = resume_data.get('name', 'The candidate')
skills: List[str] = resume_data.get('skills', []) or []
raw_text: str = resume_data.get('raw_text', '')
if not raw_text or len(raw_text.strip()) == 0:
return generate_template_summary(resume_data)
# The Inference API has input length limits; truncate long text conservatively.
# We'll keep about ~2000 characters to stay safe across most summarization models.
truncated = raw_text.strip()
if len(truncated) > 2000:
truncated = truncated[:2000]
prompt = truncated
try:
summary = hf_summarize(prompt, model=hf_model, api_token=hf_token)
except Exception:
# Fallback to template summary on any API failure
return generate_template_summary(resume_data)
# Optionally append skills
if skills:
summary = summary.strip() + f"\n\nKey Skills: {', '.join(skills[:10])}"
return summary
def generate_extractive_summary(*args, **kwargs) -> str:
"""Deprecated placeholder. We now use the Hugging Face Inference API."""
raise NotImplementedError("Use summarize_resume with hf_model and hf_token instead.")
def generate_template_summary(resume_data: Dict) -> str:
"""Generate a template-based summary when there's not enough text for extractive summarization."""
name = resume_data.get('name', 'The candidate')
skills = resume_data.get('skills', [])
summary = f"{name} "
if skills:
summary += f"is proficient in {', '.join(skills[:5])}."
else:
summary += "has submitted a resume for review."
return summary
# For testing
if __name__ == "__main__":
test_resume = {
'name': 'John Doe',
'email': 'john.doe@example.com',
'phone': '(123) 456-7890',
'skills': ['Python', 'Machine Learning', 'Data Analysis'],
'raw_text': """John Doe
Senior Data Scientist
Experienced data scientist with 5+ years of experience in machine learning and data analysis.
Proficient in Python, R, and SQL. Strong background in statistical modeling and data visualization.
Experience:
- Data Scientist at Tech Corp (2020-Present)
- Junior Data Analyst at Data Systems (2018-2020)
Education:
- MS in Computer Science, University of Technology
- BS in Mathematics, State University
"""
}
print(summarize_resume(test_resume))
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