582 lines
19 KiB
Python
582 lines
19 KiB
Python
from fastapi import FastAPI, File, HTTPException, UploadFile
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from pydantic import BaseModel, Field
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
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from cachetools import TTLCache
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from PIL import Image
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from pypdf import PdfReader
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from docx import Document
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import fitz
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import hashlib
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import io
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import json
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import os
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import re
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import torch
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import pytesseract
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from urllib import request as urllib_request
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from urllib.error import URLError, HTTPError
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app = FastAPI(title="Local AI Service")
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MODEL_NAME = "sshleifer/distilbart-cnn-12-6"
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MAX_INPUT_CHARS = 20000
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MAX_CONTEXT_CHARS = 2200
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MAX_EXTRACT_FILE_BYTES = 8 * 1024 * 1024
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OCR_LANGUAGES = "eng"
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IMAGE_EXTENSIONS = {".png", ".jpg", ".jpeg", ".webp"}
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OLLAMA_BASE_URL = os.getenv("OLLAMA_BASE_URL", "http://127.0.0.1:11434").rstrip("/")
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OLLAMA_MODEL = os.getenv("OLLAMA_MODEL", "")
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SKIP_MODEL_LOAD = os.getenv("AI_SERVICE_SKIP_MODEL_LOAD", "") == "1"
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def _load_runtime():
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tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
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model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_NAME)
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model.eval()
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has_cuda = torch.cuda.is_available()
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device = torch.device("cuda" if has_cuda else "cpu")
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model.to(device)
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gpu_name = torch.cuda.get_device_name(0) if has_cuda else None
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return tokenizer, model, device, has_cuda, gpu_name
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if SKIP_MODEL_LOAD:
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tokenizer, model, device, GPU_AVAILABLE, GPU_NAME = None, None, torch.device("cpu"), False, None
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else:
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tokenizer, model, device, GPU_AVAILABLE, GPU_NAME = _load_runtime()
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cache = TTLCache(maxsize=1024, ttl=60 * 60)
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class SummarizeRequest(BaseModel):
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text: str = Field(min_length=1, max_length=MAX_INPUT_CHARS)
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max_length: int = Field(default=160, ge=24, le=256)
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min_length: int = Field(default=45, ge=8, le=180)
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top_skills: int = Field(default=8, ge=3, le=12)
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class CvClassifyBlockRequest(BaseModel):
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block: str = Field(min_length=1, max_length=6000)
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def _key(text: str, max_length: int, min_length: int, top_skills: int) -> str:
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h = hashlib.sha256(text.encode("utf-8")).hexdigest()
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return f"{h}:{max_length}:{min_length}:{top_skills}"
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def _ollama_status():
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configured = bool(OLLAMA_MODEL)
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if not configured:
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return {
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"ollama_configured": False,
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"ollama_reachable": False,
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"ollama_model": None,
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"ollama_model_available": False,
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}
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req = urllib_request.Request(f"{OLLAMA_BASE_URL}/api/tags", method="GET")
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try:
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with urllib_request.urlopen(req, timeout=5) as response:
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body = json.loads(response.read().decode("utf-8"))
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except Exception:
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return {
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"ollama_configured": True,
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"ollama_reachable": False,
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"ollama_model": OLLAMA_MODEL,
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"ollama_model_available": False,
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}
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models = body.get("models") or []
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names = {item.get("name") for item in models if isinstance(item, dict)}
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return {
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"ollama_configured": True,
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"ollama_reachable": True,
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"ollama_model": OLLAMA_MODEL,
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"ollama_model_available": OLLAMA_MODEL in names,
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}
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@app.get("/health")
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async def health():
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return {
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"ok": True,
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"model": MODEL_NAME,
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"device": str(device),
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"gpu_available": GPU_AVAILABLE,
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"gpu_name": GPU_NAME,
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"ocr_available": True,
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"ocr_languages": OCR_LANGUAGES,
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**_ollama_status(),
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}
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_TECH = [
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"python", "c#", "dotnet", ".net", "java", "javascript", "typescript", "react", "node", "sql",
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"postgres", "postgresql", "mysql", "sqlite", "mongodb", "redis", "aws", "azure", "gcp",
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"docker", "kubernetes", "terraform", "linux", "git", "ci/cd", "graphql", "rest",
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]
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_SOFT = [
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"communication", "collaboration", "teamwork", "problem solving", "leadership", "mentoring",
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"ownership", "initiative", "adaptability", "stakeholder management", "detail oriented",
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]
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_TECH_PRIORITY = [
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"python", "c#", ".net", "dotnet", "typescript", "javascript", "react", "node",
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"sql", "postgresql", "postgres", "mysql", "sqlite", "docker", "kubernetes",
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"aws", "azure", "gcp", "terraform", "graphql", "rest", "git",
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]
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_MUST_HAVE_HINTS = [
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"must have", "required", "requirements", "you have", "you bring", "essential", "we are looking for",
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]
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_NICE_TO_HAVE_HINTS = [
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"nice to have", "bonus", "preferred", "advantageous", "extra plus",
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]
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_SCREENING_HINTS = [
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"experience with", "hands-on", "demonstrated", "proven", "track record", "delivered",
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]
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def _rank_tech_skills(skills):
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ordered = []
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seen = set()
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for preferred in _TECH_PRIORITY:
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for skill in skills:
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if skill == preferred and skill not in seen:
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ordered.append(skill)
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seen.add(skill)
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for skill in skills:
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if skill not in seen:
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ordered.append(skill)
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seen.add(skill)
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return ordered
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def _strip_html(text: str) -> str:
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text = re.sub(r"<\s*br\s*/?>", "\n", text, flags=re.IGNORECASE)
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text = re.sub(r"</p\s*>", "\n", text, flags=re.IGNORECASE)
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text = re.sub(r"<[^>]+>", " ", text)
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return re.sub(r"\n{3,}", "\n\n", text).strip()
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def _extract_bullets(lines, max_items=8):
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out = []
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for ln in lines:
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s = ln.strip()
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if not s:
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continue
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if re.match(r"^([-*]|\u2022)\s+", s):
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s = re.sub(r"^([-*]|\u2022)\s+", "", s).strip()
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if 3 <= len(s) <= 220:
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out.append(s)
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if len(out) >= max_items:
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break
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return out
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def _top_keywords(text: str, limit=6):
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words = re.findall(r"[a-zA-Z][a-zA-Z+#./-]{2,}", text.lower())
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stop = {
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"with", "from", "that", "this", "will", "have", "your", "their", "about", "role", "team", "work",
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"experience", "skills", "requirements", "responsibilities", "company", "using", "ability", "years",
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"looking", "candidate", "position", "working", "across", "strong", "building", "support",
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}
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counts = {}
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for word in words:
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if word in stop or word in _TECH or word in _SOFT:
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continue
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counts[word] = counts.get(word, 0) + 1
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ordered = sorted(counts.items(), key=lambda item: (-item[1], item[0]))
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return [word for word, _ in ordered[:limit]]
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def _first_matching_sentences(text: str, hints, limit=3):
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sentences = re.split(r"(?<=[.!?])\s+", text)
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found = []
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for sentence in sentences:
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low = sentence.lower()
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if any(hint in low for hint in hints):
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cleaned = sentence.strip()
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if 20 <= len(cleaned) <= 220:
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found.append(cleaned)
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if len(found) >= limit:
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break
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return found
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def _trim_line(text: str, max_len: int = 140) -> str:
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text = re.sub(r"\s+", " ", text).strip(" -•\t")
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if len(text) <= max_len:
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return text
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return text[: max_len - 1].rstrip() + "…"
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def _role_focused_excerpt(text: str) -> dict:
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cleaned = _strip_html(text)
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lines = [ln.strip() for ln in cleaned.splitlines()]
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headings = {
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"responsibilities": ["responsibilities", "what you will do", "what you'll do", "the role", "your role", "you will"],
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"requirements": ["requirements", "what we are looking for", "what we're looking for", "skills", "experience", "must have"],
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"nice": ["nice to have", "bonus", "preferred"],
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}
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def match_heading(s: str):
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sl = s.lower().strip(":-\x7f ")
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for key, words in headings.items():
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for word in words:
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if sl == word or sl.startswith(word + " "):
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return key
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return None
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section = None
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resp_lines = []
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req_lines = []
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nice_lines = []
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for ln in lines:
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if not ln:
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continue
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heading = match_heading(ln)
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if heading:
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section = heading
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continue
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if section == "responsibilities":
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resp_lines.append(ln)
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elif section == "requirements":
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req_lines.append(ln)
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elif section == "nice":
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nice_lines.append(ln)
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responsibilities = _extract_bullets(resp_lines, max_items=7)
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requirements = _extract_bullets(req_lines, max_items=7)
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nice = _extract_bullets(nice_lines, max_items=5)
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tech_found = []
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soft_found = []
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low = cleaned.lower()
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for t in _TECH:
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if t in low:
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tech_found.append(t)
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for s in _SOFT:
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if s in low:
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soft_found.append(s)
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if not responsibilities and not requirements:
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any_bullets = _extract_bullets(lines, max_items=10)
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responsibilities = any_bullets[:6]
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requirements = any_bullets[6:10]
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if not requirements:
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requirements = [_trim_line(x) for x in _first_matching_sentences(cleaned, _MUST_HAVE_HINTS, limit=4)]
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if not nice:
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nice = [_trim_line(x) for x in _first_matching_sentences(cleaned, _NICE_TO_HAVE_HINTS, limit=3)]
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focused_parts = []
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if responsibilities:
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focused_parts.append("Responsibilities:\n- " + "\n- ".join(responsibilities))
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if requirements:
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focused_parts.append("Requirements:\n- " + "\n- ".join(requirements))
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if nice:
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focused_parts.append("Nice to have:\n- " + "\n- ".join(nice))
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focused_parts.append("Context:\n" + cleaned[:MAX_CONTEXT_CHARS])
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screen_focus = []
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for item in requirements[:4]:
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if any(hint in item.lower() for hint in _SCREENING_HINTS) or len(screen_focus) < 2:
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screen_focus.append(_trim_line(item))
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if not screen_focus:
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screen_focus = [_trim_line(x) for x in _first_matching_sentences(cleaned, _SCREENING_HINTS, limit=3)]
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return {
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"cleaned": cleaned,
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"focused_input": "\n\n".join(focused_parts),
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"responsibilities": responsibilities,
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"requirements": requirements,
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"nice": nice,
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"tech": tech_found,
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"soft": soft_found,
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"keywords": _top_keywords(cleaned),
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"screen_focus": screen_focus[:3],
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}
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def _model_summarize(text: str, max_length: int, min_length: int) -> str:
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if tokenizer is None or model is None:
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raise HTTPException(status_code=503, detail="Summarizer model is not loaded.")
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inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=1024)
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input_ids = inputs.input_ids.to(device)
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attention_mask = inputs.attention_mask.to(device) if hasattr(inputs, "attention_mask") else None
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with torch.no_grad():
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outputs = model.generate(
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input_ids,
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attention_mask=attention_mask,
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max_length=max_length,
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min_length=min_length,
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num_beams=3,
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length_penalty=1.0,
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no_repeat_ngram_size=3,
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early_stopping=True,
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)
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return tokenizer.decode(outputs[0], skip_special_tokens=True).strip()
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def _ollama_generate_json(prompt: str):
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if not OLLAMA_MODEL:
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raise HTTPException(status_code=503, detail="OLLAMA_MODEL is not configured.")
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payload = json.dumps({
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"model": OLLAMA_MODEL,
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"prompt": prompt,
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"stream": False,
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"format": "json",
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"options": {"temperature": 0.1}
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}).encode("utf-8")
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req = urllib_request.Request(
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f"{OLLAMA_BASE_URL}/api/generate",
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data=payload,
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headers={"Content-Type": "application/json"},
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method="POST",
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)
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try:
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with urllib_request.urlopen(req, timeout=30) as response:
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body = json.loads(response.read().decode("utf-8"))
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except HTTPError as ex:
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raise HTTPException(status_code=502, detail=f"Ollama request failed with {ex.code}.")
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except URLError as ex:
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raise HTTPException(status_code=503, detail=f"Ollama is unreachable: {ex.reason}.")
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raw = (body.get("response") or "").strip()
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if not raw:
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raise HTTPException(status_code=502, detail="Ollama returned an empty response.")
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try:
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return json.loads(raw)
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except json.JSONDecodeError:
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start = raw.find("{")
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end = raw.rfind("}")
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if start >= 0 and end > start:
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return json.loads(raw[start:end + 1])
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raise HTTPException(status_code=502, detail="Ollama did not return valid JSON.")
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@app.post("/cv/classify-block")
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async def classify_cv_block(req: CvClassifyBlockRequest):
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prompt = f"""
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You classify one CV text block into structured JSON.
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Return ONLY valid JSON with this exact shape:
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{{
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"section": "Contact|Professional Summary|Work Experience|Education|Skills|Languages|Interests|Other",
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"confidence": 0.0,
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"reason": "short reason",
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"title": string|null,
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"company": string|null,
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"location": string|null,
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"start": string|null,
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"end": string|null,
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"bullets": string[]
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}}
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Rules:
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- Preserve facts only.
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- section must be one of the listed values.
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- Use Work Experience only for job/employment blocks.
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- For Contact blocks, keep title/company/start/end null and bullets empty.
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- For non-work blocks, title/company/start/end should usually be null.
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- location must look like a place, not a sentence.
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- dates must be one of: year, month+year, dd/mm/yyyy, Present, Current.
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- bullets should only be job tasks/achievements, not titles, companies, dates, or headings.
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- If unsure, choose Other and keep fields null/empty.
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Block:
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{req.block.strip()}
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""".strip()
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parsed = _ollama_generate_json(prompt)
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return {
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"section": parsed.get("section") or "Other",
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"confidence": parsed.get("confidence"),
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"reason": parsed.get("reason"),
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"title": parsed.get("title"),
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"company": parsed.get("company"),
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"location": parsed.get("location"),
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"start": parsed.get("start"),
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"end": parsed.get("end"),
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"bullets": parsed.get("bullets") or [],
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}
|
|
|
|
|
|
@app.post("/summarize")
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async def summarize(req: SummarizeRequest):
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if req.min_length >= req.max_length:
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raise HTTPException(status_code=400, detail="min_length must be smaller than max_length.")
|
|
|
|
key = _key(req.text, req.max_length, req.min_length, req.top_skills)
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if key in cache:
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return {"summary": cache[key], "cached": True}
|
|
|
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info = _role_focused_excerpt(req.text)
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summary = _model_summarize(info["focused_input"], req.max_length, req.min_length)
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|
|
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ranked_tech = []
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for t in _rank_tech_skills(info["tech"]):
|
|
if t not in ranked_tech:
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ranked_tech.append(t)
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|
|
uniq_soft = []
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for s in info["soft"]:
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if s not in uniq_soft:
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uniq_soft.append(s)
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|
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lines = ["Role summary:", summary]
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|
|
|
if info["requirements"]:
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|
lines.append("")
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|
lines.append("What the company wants most:")
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|
for x in info["requirements"][:5]:
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lines.append(f"- {_trim_line(x)}")
|
|
|
|
if ranked_tech:
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|
lines.append("")
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|
lines.append("Top hard skills:")
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|
for skill in ranked_tech[: req.top_skills]:
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lines.append(f"- {skill}")
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|
|
|
if info["keywords"]:
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|
lines.append("")
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|
lines.append("Keywords to mirror:")
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|
for keyword in info["keywords"][:5]:
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lines.append(f"- {keyword}")
|
|
|
|
if info["responsibilities"]:
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|
lines.append("")
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|
lines.append("What you would be doing:")
|
|
for x in info["responsibilities"][:4]:
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lines.append(f"- {_trim_line(x)}")
|
|
|
|
if info["nice"]:
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|
lines.append("")
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|
lines.append("Nice to have:")
|
|
for x in info["nice"][:3]:
|
|
lines.append(f"- {_trim_line(x)}")
|
|
|
|
if uniq_soft:
|
|
lines.append("")
|
|
lines.append("Relevant soft skills:")
|
|
for soft in uniq_soft[:5]:
|
|
lines.append(f"- {soft}")
|
|
|
|
lines.append("")
|
|
lines.append("Interview focus:")
|
|
if info["screen_focus"]:
|
|
for x in info["screen_focus"]:
|
|
lines.append(f"- Be ready to prove: {_trim_line(x)}")
|
|
elif info["requirements"]:
|
|
for x in info["requirements"][:3]:
|
|
lines.append(f"- Prepare examples that demonstrate: {_trim_line(x)}")
|
|
elif ranked_tech:
|
|
for x in ranked_tech[:3]:
|
|
lines.append(f"- Be ready to explain your hands-on experience with {x}")
|
|
else:
|
|
lines.append("- Prepare examples showing relevant impact, collaboration, and delivery.")
|
|
|
|
out = "\n".join(lines).strip()
|
|
cache[key] = out
|
|
return {"summary": out, "cached": False}
|
|
|
|
|
|
def _normalize_text(value: str) -> str:
|
|
value = value.replace("\x00", " ")
|
|
return re.sub(r"\s+", " ", value).strip()
|
|
|
|
|
|
def _ocr_image(image: Image.Image) -> str:
|
|
if image.mode not in ("RGB", "L"):
|
|
image = image.convert("RGB")
|
|
text = pytesseract.image_to_string(image, lang=OCR_LANGUAGES)
|
|
return _normalize_text(text)
|
|
|
|
|
|
def _extract_pdf_text(data: bytes) -> tuple[str, bool, int]:
|
|
page_count = 0
|
|
extracted_pages = []
|
|
try:
|
|
reader = PdfReader(io.BytesIO(data))
|
|
page_count = len(reader.pages)
|
|
for page in reader.pages:
|
|
extracted_pages.append(page.extract_text() or "")
|
|
except Exception:
|
|
extracted_pages = []
|
|
|
|
text = _normalize_text("\n".join(extracted_pages))
|
|
if len(text) >= 80:
|
|
return text, False, page_count
|
|
|
|
doc = fitz.open(stream=data, filetype="pdf")
|
|
page_count = doc.page_count
|
|
ocr_pages = []
|
|
for page in doc:
|
|
pix = page.get_pixmap(matrix=fitz.Matrix(2, 2), alpha=False)
|
|
image = Image.open(io.BytesIO(pix.tobytes("png")))
|
|
ocr_pages.append(_ocr_image(image))
|
|
doc.close()
|
|
return _normalize_text("\n".join(ocr_pages)), True, page_count
|
|
|
|
|
|
def _extract_docx_text(data: bytes) -> str:
|
|
document = Document(io.BytesIO(data))
|
|
parts = [p.text.strip() for p in document.paragraphs if p.text and p.text.strip()]
|
|
return _normalize_text("\n".join(parts))
|
|
|
|
|
|
def _extract_plain_text(data: bytes) -> str:
|
|
return _normalize_text(data.decode("utf-8", errors="ignore"))
|
|
|
|
|
|
@app.post("/extract-text")
|
|
async def extract_text(file: UploadFile = File(...)):
|
|
filename = file.filename or "document"
|
|
extension = "." + filename.rsplit(".", 1)[1].lower() if "." in filename else ""
|
|
data = await file.read()
|
|
if not data:
|
|
raise HTTPException(status_code=400, detail="The uploaded file was empty.")
|
|
if len(data) > MAX_EXTRACT_FILE_BYTES:
|
|
raise HTTPException(status_code=400, detail="The uploaded file is too large for AI extraction.")
|
|
|
|
try:
|
|
if extension in {".txt", ".md"}:
|
|
text = _extract_plain_text(data)
|
|
ocr_used = False
|
|
page_count = None
|
|
elif extension == ".docx":
|
|
text = _extract_docx_text(data)
|
|
ocr_used = False
|
|
page_count = None
|
|
elif extension == ".pdf":
|
|
text, ocr_used, page_count = _extract_pdf_text(data)
|
|
elif extension in IMAGE_EXTENSIONS:
|
|
image = Image.open(io.BytesIO(data))
|
|
text = _ocr_image(image)
|
|
ocr_used = True
|
|
page_count = 1
|
|
else:
|
|
raise HTTPException(status_code=400, detail="This file type is not supported for AI extraction.")
|
|
except HTTPException:
|
|
raise
|
|
except Exception as exc:
|
|
raise HTTPException(status_code=500, detail=f"AI extraction failed: {exc}") from exc
|
|
|
|
if not text:
|
|
raise HTTPException(status_code=422, detail="AI extraction did not find readable text in the uploaded file.")
|
|
|
|
return {
|
|
"text": text,
|
|
"ocr_used": ocr_used,
|
|
"content_type": file.content_type,
|
|
"page_count": page_count,
|
|
"characters": len(text),
|
|
"file_name": filename,
|
|
}
|