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| import pandas as pd | |
| from core.parsing.schema import Resume | |
| import uuid | |
| def resume_to_dfs(resume: Resume): | |
| r = resume.model_dump() | |
| # Flattens the top-level fields and the nested 'contact' dict | |
| base_data = { | |
| "name": r.get("full_name"), | |
| **{f"contact_{k}": v for k, v in (r.get("contact") or {}).items()}, | |
| "summary": r.get("summary"), | |
| } | |
| df_base = pd.DataFrame([base_data]) | |
| df_skl = pd.DataFrame(r.get("skills") or []) | |
| df_cert = pd.DataFrame(r.get("certifications") or []) | |
| df_edu = pd.DataFrame(r.get("education") or []) | |
| df_exp = pd.DataFrame(r.get("experience") or []) | |
| # 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) | |
| dfs = { | |
| "base": df_base, | |
| "skills": df_skl, | |
| "certifications": df_cert, | |
| "education": df_edu, | |
| "experience": df_exp, | |
| "projects": df_proj | |
| } | |
| resume_id = str(uuid.uuid4())[:8] # Short unique ID | |
| for df in dfs: | |
| df.insert(0, 'resume_id', resume_id) | |
| return dfs | |