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from fastapi import FastAPI, HTTPException
from models import CVExtracted, InsertedText, JobAndCV, ClassificationResult, InsertedLink
import os
from io import BytesIO
from datetime import datetime
from PyPDF2 import PdfReader
import requests
import logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logging.info("Starting...")
import extractor
import classificator
os.environ['HF_HOME'] = '/transformers_cache'
app = FastAPI()
@app.get("/", response_model=dict[str, str])
def getall():
return {"hello":"world"}
@app.post("/ext", response_model=CVExtracted)
async def extract(text: InsertedText):
dictresult = extractor.predict(text.text)
return CVExtracted(**dictresult)
@app.post("/classify", response_model=ClassificationResult)
async def classify(body:JobAndCV):
mininmal_start = 0
maximal_end = 0
positions = []
userMajors = []
yoe = 0
if len(body.cv.experiences) > 0:
mininmal_start = datetime.strptime(body.cv.experiences[0]['start'], "%Y-%m-%d").date() if body.cv.experiences[0].get('start') != None else datetime.today().date()
maximal_end = datetime.strptime(body.cv.experiences[0]['end'], "%Y-%m-%d").date() if body.cv.experiences[0].get('end') != None else datetime.today().date()
for exp in body.cv.experiences:
positions.append(exp['position'])
if exp.get('end') == None:
exp['end'] = datetime.today().strftime("%Y-%m-%d")
if datetime.strptime(exp['start'], "%Y-%m-%d").date() < mininmal_start:
mininmal_start = datetime.strptime(exp['start'], "%Y-%m-%d").date()
if datetime.strptime(exp['end'], "%Y-%m-%d").date() > maximal_end:
maximal_end = datetime.strptime(exp['end'], "%Y-%m-%d").date()
yoe = (maximal_end - mininmal_start).days//365
for edu in body.cv.educations:
userMajors.append(edu['major'])
cv = {
"experiences": str(body.cv.experiences),
"positions": str(positions),
"userMajors": str(userMajors),
"skills": str(body.cv.skills),
"yoe": yoe
}
job = {
"jobDesc": body.job.jobDesc,
"role": body.job.role,
"majors": str(body.job.majors),
"skills": str(body.job.skills),
"minYoE": body.job.minYoE
}
results = classificator.predict(cv, job)
return ClassificationResult(**results)
@app.post("/cv", response_model=CVExtracted)
async def extract(link: InsertedLink):
response = requests.get(link.link)
if response.status_code == 200:
# Open the PDF from bytes in memory
pdf_reader = PdfReader(BytesIO(response.content))
number_of_pages = len(pdf_reader.pages)
# Optionally, read text from the first page
page = pdf_reader.pages[0]
text = page.extract_text()
for i in range(1, number_of_pages):
text+= '\n' + pdf_reader.pages[i].extract_text()
else:
#return error, make 500 because file server error
raise HTTPException(status_code=response.status_code, detail="File server error")
dictresult = extractor.predict(text)
return CVExtracted(**dictresult) |