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import streamlit as st
from huggingface_hub import InferenceClient
from dotenv import load_dotenv
import os
import PyPDF2 as pdf
# Load .env file
load_dotenv()
api_key = os.getenv("HUGGINGFACEHUB_API_TOKEN")
# Hugging Face model
MODEL = "nvidia/Llama-3_1-Nemotron-Ultra-253B-v1"
# Set up page
st.set_page_config(page_title="JD Matcher by Jishnu Setia", page_icon="📄")
st.title("📌 Job Description Matcher")
st.text("Find out if your resume matches the job you're targeting!")
# Input fields
jd = st.text_area("📝 Paste the Job Description here:")
uploaded_file = st.file_uploader("📎 Upload Your Resume (PDF only):", type="pdf")
submit = st.button("🚀 Submit")
# Function to read PDF content
def input_pdf_text(uploaded_file):
reader = pdf.PdfReader(uploaded_file)
text = ""
for page in reader.pages:
text += page.extract_text()
return text
# Prompt template
system_prompt = {
"role": "system",
"content": (
"You are a highly experienced ATS (Applicant Tracking System). Evaluate the resume based on the given job description. "
"Be strict, accurate, and helpful. Job market is very competitive. Return your response in this format:\n\n"
"1. JD Match Percentage: \"%\"\n"
"2. Matching Feedback: (e.g., 'Great match!' or 'Needs improvement')\n"
"3. Missing Keywords: [list]\n"
"4. Tips to Improve the Resume:"
)
}
# When submit is clicked
if submit:
if uploaded_file and jd:
with st.spinner("Analyzing your resume..."):
resume_text = input_pdf_text(uploaded_file)
# Prepare context
context = [
system_prompt,
{"role": "user", "content": f"Resume:\n{resume_text}\n\nJob Description:\n{jd}"}
]
try:
client = InferenceClient(
model=MODEL,
provider="nebius",
api_key=api_key
)
completion = client.chat.completions.create(
model=MODEL,
messages=context,
max_tokens=2048,
)
response = completion.choices[0].message.content
st.subheader("📊 ATS Evaluation Result")
st.markdown(response)
except Exception as e:
st.error(f"❌ Error: {e}")
else:
st.warning("Please upload a resume and paste a job description!")
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