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e70f183 5e8ee1a e70f183 5e8ee1a e70f183 5e8ee1a e70f183 903a1b0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 | import os
import shutil
import gradio as gr
from fastapi import FastAPI, UploadFile, File, Form
from resume_parser import parse_resume
from model_logic import ResumeScorer
from logger import log_decision
app = FastAPI()
scorer = ResumeScorer()
UPLOAD_DIR = "uploads"
os.makedirs(UPLOAD_DIR, exist_ok=True)
def process_resume(file, title, level):
import uuid
temp_name = f"{uuid.uuid4()}.pdf"
path = os.path.join(UPLOAD_DIR, temp_name)
# CASE 1: FastAPI UploadFile
if hasattr(file, "read"):
with open(path, "wb") as f:
shutil.copyfileobj(file, f)
# CASE 2: Gradio file path
else:
shutil.copy(file.name if hasattr(file, "name") else file, path)
text = parse_resume(path)
result = scorer.score_resume_by_title(text, title, level)
log_decision(title, result["decision"])
os.remove(path)
return result
@app.post("/analyze_resume")
async def analyze_resume(
file: UploadFile = File(...),
title: str = Form(...),
level: str = Form(...)
):
result = process_resume(file.file, title, level)
return result
def gradio_interface(file, title, level):
result = process_resume(file, title, level)
return result
demo = gr.Interface(
fn=gradio_interface,
inputs=[
gr.File(label="Upload Resume PDF/docx", file_types=[".pdf",".docx"]),
gr.Textbox(label="Job Title"),
gr.Dropdown(
["entry","junior","mid","senior"],
label="Job Level"
)
],
outputs="json",
title="AI Resume Screening System"
)
if __name__ == "__main__":
demo.launch() |