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153f2a7 6e51440 153f2a7 6e51440 153f2a7 6e51440 153f2a7 6e51440 153f2a7 6e51440 153f2a7 6e51440 153f2a7 6e51440 | 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 | import pandas as pd
from core.parsing.schema import Resume
def resume_to_df(resume: Resume):
# r = resume.dict()
r = resume.model_dump() # Dictionary -> key, value pairs
base = {
"full_name": r["full_name"],
"summary": r["summary"],
**{f"contact_{k}": v for k, v in r["contact"].items() if v != None},
"ai_ml_skills": ", ".join(r.get("ai_ml_skills", []) or []),
"technical_skills": ", ".join(r.get("technical_skills", []) or []),
"certifications": ", ".join(r.get("certifications", []) or [])
}
rows = []
# max length among lists
max_len = max(
len(r.get("education") or []),
len(r.get("experience") or []),
len(r.get("projects") or []),
1 # atleast one row.
)
for i in range(max_len):
row = {} #base.copy()
# education
educations = r.get("education", []) or []
if i < len(educations):
e = educations[i]
row.update({ # row |= {}
"edu_institution": e["institution"],
"edu_degree": e["degree"],
"edu_start": e["start_date"],
"edu_end": e["end_date"],
})
# experience
experiences = r.get("experience", []) or []
if i < len(experiences):
ex = experiences[i]
row.update({
"exp_title": ex["title"],
"exp_company": ex["company"],
"exp_start": ex["start_date"],
"exp_end": ex["end_date"],
})
# projects
projects = r.get("projects", []) or []
if i < len(projects):
p = projects[i]
row.update({
"proj_name": p["name"],
"proj_desc": p["description"],
"proj_tech": ", ".join(p["technologies"]),
"proj_score": p["difficulty_score"],
})
rows.append(row)
return pd.DataFrame(rows)
def resume_to_dfs(resume: Resume):
r = resume.model_dump()
# 1. Base Info (Contact, Skills, Summary)
# Flattens the top-level fields and the nested 'contact' dict
base_data = {
"full_name": r.get("full_name"),
"summary": r.get("summary"),
**{f"contact_{k}": v for k, v in (r.get("contact") or {}).items()},
"ai_ml_skills": ", ".join(r.get("ai_ml_skills") or []),
"technical_skills": ", ".join(r.get("technical_skills") or []),
"certifications": ", ".join(r.get("certifications") or [])
}
df_base = pd.DataFrame([base_data])
# 2. Education DataFrame
df_edu = pd.DataFrame(r.get("education") or [])
# 3. Experience DataFrame
df_exp = pd.DataFrame(r.get("experience") or [])
# 4. Projects DataFrame
# We handle the 'technologies' list by joining it into a string for the CSV/Table view
projects = r.get("projects") or []
for p in projects:
if isinstance(p.get("technologies"), list):
p["technologies"] = ", ".join(p["technologies"])
df_proj = pd.DataFrame(projects)
return {
"base": df_base,
"education": df_edu,
"experience": df_exp,
"projects": df_proj
}
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