from fastapi import FastAPI, File, HTTPException, UploadFile from pydantic import BaseModel, Field from transformers import AutoTokenizer, AutoModelForSeq2SeqLM from cachetools import TTLCache from PIL import Image from pypdf import PdfReader from docx import Document import fitz import hashlib import io import re import torch import pytesseract app = FastAPI(title="Local AI Service") MODEL_NAME = "sshleifer/distilbart-cnn-12-6" MAX_INPUT_CHARS = 20000 MAX_CONTEXT_CHARS = 2200 MAX_EXTRACT_FILE_BYTES = 8 * 1024 * 1024 OCR_LANGUAGES = "eng" IMAGE_EXTENSIONS = {".png", ".jpg", ".jpeg", ".webp"} def _load_runtime(): tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME) model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_NAME) model.eval() has_cuda = torch.cuda.is_available() device = torch.device("cuda" if has_cuda else "cpu") model.to(device) gpu_name = torch.cuda.get_device_name(0) if has_cuda else None return tokenizer, model, device, has_cuda, gpu_name tokenizer, model, device, GPU_AVAILABLE, GPU_NAME = _load_runtime() cache = TTLCache(maxsize=1024, ttl=60 * 60) class SummarizeRequest(BaseModel): text: str = Field(min_length=1, max_length=MAX_INPUT_CHARS) max_length: int = Field(default=160, ge=24, le=256) min_length: int = Field(default=45, ge=8, le=180) top_skills: int = Field(default=8, ge=3, le=12) def _key(text: str, max_length: int, min_length: int, top_skills: int) -> str: h = hashlib.sha256(text.encode("utf-8")).hexdigest() return f"{h}:{max_length}:{min_length}:{top_skills}" @app.get("/health") async def health(): return { "ok": True, "model": MODEL_NAME, "device": str(device), "gpu_available": GPU_AVAILABLE, "gpu_name": GPU_NAME, "ocr_available": True, "ocr_languages": OCR_LANGUAGES, } _TECH = [ "python", "c#", "dotnet", ".net", "java", "javascript", "typescript", "react", "node", "sql", "postgres", "postgresql", "mysql", "sqlite", "mongodb", "redis", "aws", "azure", "gcp", "docker", "kubernetes", "terraform", "linux", "git", "ci/cd", "graphql", "rest", ] _SOFT = [ "communication", "collaboration", "teamwork", "problem solving", "leadership", "mentoring", "ownership", "initiative", "adaptability", "stakeholder management", "detail oriented", ] _TECH_PRIORITY = [ "python", "c#", ".net", "dotnet", "typescript", "javascript", "react", "node", "sql", "postgresql", "postgres", "mysql", "sqlite", "docker", "kubernetes", "aws", "azure", "gcp", "terraform", "graphql", "rest", "git", ] _MUST_HAVE_HINTS = [ "must have", "required", "requirements", "you have", "you bring", "essential", "we are looking for", ] _NICE_TO_HAVE_HINTS = [ "nice to have", "bonus", "preferred", "advantageous", "extra plus", ] _SCREENING_HINTS = [ "experience with", "hands-on", "demonstrated", "proven", "track record", "delivered", ] def _rank_tech_skills(skills): ordered = [] seen = set() for preferred in _TECH_PRIORITY: for skill in skills: if skill == preferred and skill not in seen: ordered.append(skill) seen.add(skill) for skill in skills: if skill not in seen: ordered.append(skill) seen.add(skill) return ordered def _strip_html(text: str) -> str: text = re.sub(r"<\s*br\s*/?>", "\n", text, flags=re.IGNORECASE) text = re.sub(r"", "\n", text, flags=re.IGNORECASE) text = re.sub(r"<[^>]+>", " ", text) return re.sub(r"\n{3,}", "\n\n", text).strip() def _extract_bullets(lines, max_items=8): out = [] for ln in lines: s = ln.strip() if not s: continue if re.match(r"^([-*]|\u2022)\s+", s): s = re.sub(r"^([-*]|\u2022)\s+", "", s).strip() if 3 <= len(s) <= 220: out.append(s) if len(out) >= max_items: break return out def _top_keywords(text: str, limit=6): words = re.findall(r"[a-zA-Z][a-zA-Z+#./-]{2,}", text.lower()) stop = { "with", "from", "that", "this", "will", "have", "your", "their", "about", "role", "team", "work", "experience", "skills", "requirements", "responsibilities", "company", "using", "ability", "years", "looking", "candidate", "position", "working", "across", "strong", "building", "support", } counts = {} for word in words: if word in stop or word in _TECH or word in _SOFT: continue counts[word] = counts.get(word, 0) + 1 ordered = sorted(counts.items(), key=lambda item: (-item[1], item[0])) return [word for word, _ in ordered[:limit]] def _first_matching_sentences(text: str, hints, limit=3): sentences = re.split(r"(?<=[.!?])\s+", text) found = [] for sentence in sentences: low = sentence.lower() if any(hint in low for hint in hints): cleaned = sentence.strip() if 20 <= len(cleaned) <= 220: found.append(cleaned) if len(found) >= limit: break return found def _trim_line(text: str, max_len: int = 140) -> str: text = re.sub(r"\s+", " ", text).strip(" -•\t") if len(text) <= max_len: return text return text[: max_len - 1].rstrip() + "…" def _role_focused_excerpt(text: str) -> dict: cleaned = _strip_html(text) lines = [ln.strip() for ln in cleaned.splitlines()] headings = { "responsibilities": ["responsibilities", "what you will do", "what you'll do", "the role", "your role", "you will"], "requirements": ["requirements", "what we are looking for", "what we're looking for", "skills", "experience", "must have"], "nice": ["nice to have", "bonus", "preferred"], } def match_heading(s: str): sl = s.lower().strip(":-\x7f ") for key, words in headings.items(): for word in words: if sl == word or sl.startswith(word + " "): return key return None section = None resp_lines = [] req_lines = [] nice_lines = [] for ln in lines: if not ln: continue heading = match_heading(ln) if heading: section = heading continue if section == "responsibilities": resp_lines.append(ln) elif section == "requirements": req_lines.append(ln) elif section == "nice": nice_lines.append(ln) responsibilities = _extract_bullets(resp_lines, max_items=7) requirements = _extract_bullets(req_lines, max_items=7) nice = _extract_bullets(nice_lines, max_items=5) tech_found = [] soft_found = [] low = cleaned.lower() for t in _TECH: if t in low: tech_found.append(t) for s in _SOFT: if s in low: soft_found.append(s) if not responsibilities and not requirements: any_bullets = _extract_bullets(lines, max_items=10) responsibilities = any_bullets[:6] requirements = any_bullets[6:10] if not requirements: requirements = [_trim_line(x) for x in _first_matching_sentences(cleaned, _MUST_HAVE_HINTS, limit=4)] if not nice: nice = [_trim_line(x) for x in _first_matching_sentences(cleaned, _NICE_TO_HAVE_HINTS, limit=3)] focused_parts = [] if responsibilities: focused_parts.append("Responsibilities:\n- " + "\n- ".join(responsibilities)) if requirements: focused_parts.append("Requirements:\n- " + "\n- ".join(requirements)) if nice: focused_parts.append("Nice to have:\n- " + "\n- ".join(nice)) focused_parts.append("Context:\n" + cleaned[:MAX_CONTEXT_CHARS]) screen_focus = [] for item in requirements[:4]: if any(hint in item.lower() for hint in _SCREENING_HINTS) or len(screen_focus) < 2: screen_focus.append(_trim_line(item)) if not screen_focus: screen_focus = [_trim_line(x) for x in _first_matching_sentences(cleaned, _SCREENING_HINTS, limit=3)] return { "cleaned": cleaned, "focused_input": "\n\n".join(focused_parts), "responsibilities": responsibilities, "requirements": requirements, "nice": nice, "tech": tech_found, "soft": soft_found, "keywords": _top_keywords(cleaned), "screen_focus": screen_focus[:3], } def _model_summarize(text: str, max_length: int, min_length: int) -> str: inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=1024) input_ids = inputs.input_ids.to(device) attention_mask = inputs.attention_mask.to(device) if hasattr(inputs, "attention_mask") else None with torch.no_grad(): outputs = model.generate( input_ids, attention_mask=attention_mask, max_length=max_length, min_length=min_length, num_beams=3, length_penalty=1.0, no_repeat_ngram_size=3, early_stopping=True, ) return tokenizer.decode(outputs[0], skip_special_tokens=True).strip() @app.post("/summarize") async def summarize(req: SummarizeRequest): if req.min_length >= req.max_length: 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) if key in cache: return {"summary": cache[key], "cached": True} info = _role_focused_excerpt(req.text) summary = _model_summarize(info["focused_input"], req.max_length, req.min_length) ranked_tech = [] for t in _rank_tech_skills(info["tech"]): if t not in ranked_tech: ranked_tech.append(t) uniq_soft = [] for s in info["soft"]: if s not in uniq_soft: uniq_soft.append(s) lines = ["Role summary:", summary] if info["requirements"]: lines.append("") lines.append("What the company wants most:") for x in info["requirements"][:5]: lines.append(f"- {_trim_line(x)}") if ranked_tech: lines.append("") lines.append("Top hard skills:") for skill in ranked_tech[: req.top_skills]: lines.append(f"- {skill}") if info["keywords"]: lines.append("") lines.append("Keywords to mirror:") for keyword in info["keywords"][:5]: lines.append(f"- {keyword}") if info["responsibilities"]: lines.append("") lines.append("What you would be doing:") for x in info["responsibilities"][:4]: lines.append(f"- {_trim_line(x)}") if info["nice"]: lines.append("") 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, }