first python service commit from Mahroosh

.gitignore

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+__pycache__/
+*.pyc
+*.pyo
+venv/
+.venv/
+*.egg-info/
+.env
+*.log
+.DS_Store

main.py

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+from fastapi import FastAPI
+from routers import extraction
+
+app = FastAPI(title="Smartfolio Python Service", version="1.0.0")
+
+app.include_router(extraction.router)
+
+
+@app.get("/health")
+def health():
+    return {"status": "ok"}

requirements.txt

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+fastapi==0.115.0
+uvicorn==0.30.6
+pdfplumber==0.11.4
+python-multipart==0.0.9
+pydantic==2.8.2

routers/extraction.py

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+from fastapi import APIRouter, HTTPException
+from pydantic import BaseModel
+from services.pdf_extractor import extract_resume_from_path
+
+router = APIRouter(prefix="/extract-resume", tags=["extraction"])
+
+
+class ExtractRequest(BaseModel):
+    file_path: str
+
+
+@router.post("")
+def extract_resume(request: ExtractRequest):
+    """
+    Receives the absolute path of an uploaded PDF (written by NestJS),
+    extracts and structures its text, and returns it to NestJS.
+    """
+    try:
+        result = extract_resume_from_path(request.file_path)
+        return result
+    except FileNotFoundError:
+        raise HTTPException(status_code=404, detail="File not found at provided path.")
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Extraction failed: {str(e)}")

services/pdf_extractor.py

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+import re
+import pdfplumber
+
+
+# Section keywords to detect standard resume sections
+SECTION_KEYWORDS = {
+    "experience":  ["experience", "work experience", "employment", "work history"],
+    "education":   ["education", "academic background", "qualifications"],
+    "skills":      ["skills", "technical skills", "core competencies", "expertise"],
+    "summary":     ["summary", "objective", "profile", "about me"],
+    "contact":     ["contact", "personal information", "personal details"],
+    "projects":    ["projects", "personal projects", "key projects"],
+    "certifications": ["certifications", "certificates", "licenses"],
+}
+
+
+def extract_resume_from_path(file_path: str) -> dict:
+    """
+    Opens a PDF with pdfplumber, extracts text page by page,
+    detects basic metadata, and identifies resume sections.
+    """
+    text_pages = []
+    has_tables = False
+    has_images = False
+    page_count = 0
+
+    with pdfplumber.open(file_path) as pdf:
+        page_count = len(pdf.pages)
+
+        for page in pdf.pages:
+            # Detect tables
+            if page.find_tables():
+                has_tables = True
+
+            # Detect images
+            if page.images:
+                has_images = True
+
+            page_text = page.extract_text() or ""
+            text_pages.append(page_text)
+
+    full_text = "\n".join(text_pages).strip()
+
+    # Confidence: penalise if text is very short relative to page count
+    expected_min_chars = page_count * 200
+    confidence = min(1.0, len(full_text) / max(expected_min_chars, 1))
+    confidence = round(confidence, 2)
+
+    sections = _detect_sections(full_text)
+
+    return {
+        "text": full_text,
+        "sections": sections,
+        "metadata": {
+            "hasTables":  has_tables,
+            "hasImages":  has_images,
+            "pageCount":  page_count,
+            "confidence": confidence,
+        },
+    }
+
+
+def _detect_sections(text: str) -> dict:
+    """
+    Splits the resume text into named sections by detecting section headers.
+    Returns a dict of section_name → list of content lines.
+    """
+    lines = text.split("\n")
+    sections: dict[str, list[str]] = {}
+    current_section: str | None = None
+
+    for line in lines:
+        stripped = line.strip()
+        if not stripped:
+            continue
+
+        detected = _match_section_header(stripped)
+
+        if detected:
+            current_section = detected
+            if current_section not in sections:
+                sections[current_section] = []
+        elif current_section:
+            sections[current_section].append(stripped)
+
+    return sections
+
+
+def _match_section_header(line: str) -> str | None:
+    """
+    Returns the canonical section name if the line looks like a section header,
+    otherwise returns None.
+    A line is treated as a header if it's short (≤50 chars) and matches a keyword.
+    """
+    if len(line) > 50:
+        return None
+
+    lower = line.lower().rstrip(":").strip()
+
+    for section_name, keywords in SECTION_KEYWORDS.items():
+        for kw in keywords:
+            # Exact match or the line IS the keyword phrase
+            if re.fullmatch(re.escape(kw), lower):
+                return section_name
+
+    return None