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from fastapi import FastAPI, UploadFile, File, Form
from typing import List
import os
import shutil
from inference import run_single_similarity, run_batch_similarity
from data import load_text
from utils import save_result
from explanation import result_explanation
from model import load_model
import yaml
app = FastAPI()
# Load LLM model once
llm_pipe = load_model()
UPLOAD_DIR = "uploads"
os.makedirs(UPLOAD_DIR, exist_ok=True)
# Default section weights (you can customize this)
DEFAULT_SECTION_WEIGHTS = {
"Experience & Achievements": 0.2,
"Skills & Competencies": 0.2,
"Culture Fit": 0.2,
"Personal Traits": 0.2,
"Education": 0.2
}
def load_weights_from_upload(file: UploadFile):
if file is None:
return None
content = file.file.read().decode("utf-8")
return yaml.safe_load(content)
@app.post('/compare/single')
async def compare_single(
jd_file: UploadFile = File(...),
cv_file: UploadFile = File(...),
weights_file: UploadFile = File(None),
model_id: str = Form("sentence-transformers/all-MiniLM-L6-v2")
):
jd_path = os.path.join(UPLOAD_DIR, jd_file.filename)
cv_path = os.path.join(UPLOAD_DIR, cv_file.filename)
with open(jd_path, "wb") as f:
shutil.copyfileobj(jd_file.file, f)
with open(cv_path, "wb") as f:
shutil.copyfileobj(cv_file.file, f)
section_weights = load_weights_from_upload(weights_file) or DEFAULT_SECTION_WEIGHTS
# Run similarity
final_score, section_scores = run_single_similarity(
jd_path=jd_path,
cv_path=cv_path,
SECTION_WEIGHTS=section_weights,
model_id=model_id,
pipe=llm_pipe
)
# Explanation
jd_text = load_text(jd_path)
cv_text = load_text(cv_path)
prompt = result_explanation(cv_text, jd_text)
explanation = llm_pipe(prompt, max_new_tokens=1024)
return {
"resume": os.path.basename(cv_path),
"jd": os.path.basename(jd_path),
"similarity_score": round(final_score, 4),
"section_scores": {k: round(v, 4) for k, v in section_scores.items()},
"explanation": explanation[0]["generated_text"][2]['content'].strip()
}
@app.post("/compare/batch/")
async def compare_batch(
jd_file: UploadFile = File(...),
resumes: List[UploadFile] = File(...),
model_id: str = Form("sentence-transformers/all-MiniLM-L6-v2")
):
jd_path = os.path.join(UPLOAD_DIR, jd_file.filename)
with open(jd_path, "wb") as f:
shutil.copyfileobj(jd_file.file, f)
cv_folder = os.path.join(UPLOAD_DIR, "batch_cvs")
os.makedirs(cv_folder, exist_ok=True)
for file in resumes:
dest_path = os.path.join(cv_folder, file.filename)
with open(dest_path, "wb") as f:
shutil.copyfileobj(file.file, f)
section_weights = load_weights_from_upload(weights_file) or DEFAULT_SECTION_WEIGHTS
results = run_batch_similarity(
jd_path=jd_path,
cv_input=cv_folder,
SECTION_WEIGHTS=section_weights,
model_id=model_id,
pipe=llm_pipe
)
return {"results": results}
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