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+# Gunakan image Python yang ringan
+FROM python:3.9-slim
+
+# Set working directory
+WORKDIR /code
+
+# Copy file requirements
+COPY ./requirements.txt /code/requirements.txt
+
+# Install dependencies
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+# Copy semua file ke dalam container
+COPY . .
+
+# Jalankan Uvicorn (FastAPI) pada port 7860 (port standar HF Spaces)
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]

main.py

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+import io
+import re
+import os
+import torch
+import PyPDF2
+from fastapi import FastAPI, UploadFile, File, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from sentence_transformers import SentenceTransformer, util
+
+app = FastAPI()
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# --- LOAD MODEL DARI HUGGING FACE ---
+# Mengambil token dari Secret yang nanti kamu setting di HF Spaces
+HF_TOKEN = os.getenv("HF_TOKEN")
+REPO_ID = "lilcoderi/cv-matcher-model"
+
+# Load model langsung dari Hub
+model = SentenceTransformer(REPO_ID, use_auth_token=HF_TOKEN)
+
+THRESHOLD = 0.59
+
+# Pre-compile regex untuk kecepatan eksekusi
+RE_CLEAN = re.compile(r'[•\-*●▪◦☑]')
+RE_SPACES = re.compile(r'\s+')
+RE_NON_ALPHA = re.compile(r'[^\w\s]')
+
+# --- FUNGSI PREPROCESSING OPTIMIZED ---
+
+def clean_text(text: str) -> str:
+    text = text.lower()
+    text = RE_CLEAN.sub(' ', text)
+    text = text.encode("ascii", "ignore").decode()
+    text = RE_NON_ALPHA.sub(' ', text)
+    return RE_SPACES.sub(' ', text).strip()
+
+def standardize_education(text: str) -> str:
+    edu_map = {
+        r'\b(sarjana|s1|strata 1|universitas|politeknik|institut)\b': 's1',
+        r'\b(diploma 3|d3|ahli madya)\b': 'd3',
+        r'\b(sma|smk|stm|smu|ma|sekolah menengah)\b': 'sma_smk',
+    }
+    for pattern, replacement in edu_map.items():
+        text = re.sub(pattern, replacement, text)
+    return text
+
+def clean_job_description(text: str) -> str:
+    noise_patterns = [
+        r'we are hiring', r'send us your cv', r'kirim cv anda',
+        r'hrdptoba@gmail\.com', r'subjek:.*', r'lowongan ini dibuka sampai.*',
+        r'posisi_nama_domisili', r'format pdf'
+    ]
+    for pattern in noise_patterns:
+        text = re.sub(pattern, '', text, flags=re.IGNORECASE)
+    return text
+
+def extract_text_from_pdf(file_bytes, max_pages=3):
+    try:
+        pdf_reader = PyPDF2.PdfReader(io.BytesIO(file_bytes))
+        text = ""
+        pages_to_read = min(len(pdf_reader.pages), max_pages)
+        for i in range(pages_to_read):
+            content = pdf_reader.pages[i].extract_text()
+            if content:
+                text += content + " "
+        return text
+    except Exception:
+        raise HTTPException(status_code=400, detail="Gagal membaca file PDF")
+
+# --- ENDPOINT UTAMA ---
+
+@app.post("/match")
+async def match_cvs(
+    job_file: UploadFile = File(...),
+    cv_files: list[UploadFile] = File(...)
+):
+    # 1. Proses Job Description
+    job_raw = extract_text_from_pdf(await job_file.read(), max_pages=5)
+    job_cleaned = clean_job_description(job_raw)
+    job_final = standardize_education(clean_text(job_cleaned))
+    
+    # 2. Ekstrak teks dari banyak CV
+    cv_texts_processed = []
+    filenames = []
+
+    for cv in cv_files:
+        content = await cv.read()
+        raw_text = extract_text_from_pdf(content, max_pages=3)
+        processed_text = standardize_education(clean_text(raw_text))
+        
+        cv_texts_processed.append(processed_text)
+        filenames.append(cv.filename)
+
+    if not cv_texts_processed:
+        raise HTTPException(status_code=400, detail="Tidak ada CV yang valid")
+
+    # 3. Proses Embedding & Similarity
+    with torch.no_grad():
+        job_embedding = model.encode(job_final, convert_to_tensor=True, normalize_embeddings=True)
+        cv_embeddings = model.encode(cv_texts_processed, convert_to_tensor=True, normalize_embeddings=True)
+        
+        scores = util.cos_sim(job_embedding, cv_embeddings)[0]
+
+    # 4. Susun Hasil & Ranking
+    results = []
+    for i in range(len(filenames)):
+        score_val = float(scores[i])
+        results.append({
+            "filename": filenames[i],
+            "score": round(score_val, 4),
+            "percentage": round(score_val * 100, 2),
+            "status": "Cocok" if score_val >= THRESHOLD else "Tidak Cocok"
+        })
+
+    results.sort(key=lambda x: x['score'], reverse=True)
+    return {"results": results}

requirements.txt

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+fastapi
+uvicorn
+python-multipart
+sentence-transformers
+PyPDF2
+torch --index-url https://download.pytorch.org/whl/cpu