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import streamlit as st
import pdfplumber
import torch
from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
# Load Hugging Face model
model_name = "deepset/roberta-base-squad2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForQuestionAnswering.from_pretrained(model_name)
qa_pipeline = pipeline(
"question-answering",
model=model,
tokenizer=tokenizer,
truncation=True,
max_length=1024
)
# Function to extract text from PDF
def extract_text_from_pdf(pdf_file):
with pdfplumber.open(pdf_file) as pdf:
text = "\n".join([page.extract_text() for page in pdf.pages if page.extract_text()])
return text if text.strip() else "No text found in the document."
# Function to extract information using LLM
def extract_info(text, question):
if not text or len(text.strip()) < 50:
return "Insufficient text for analysis."
if not question or len(question.strip()) == 0:
return "No valid question provided."
try:
result = qa_pipeline({
"context": text[:1024],
"question": question
})
return result.get("answer", "No relevant information found.")
except Exception as e:
return f"Error processing request: {str(e)}"
# Function to analyze resume vs job description
def analyze_resume_vs_job(resume_text, job_desc):
job_keywords = set(job_desc.lower().split()) # Extract keywords from JD
resume_keywords = set(resume_text.lower().split()) # Extract keywords from resume
matched_skills = job_keywords.intersection(resume_keywords) # Common words
missing_skills = job_keywords.difference(resume_keywords) # Missing words
return matched_skills, missing_skills
# Streamlit UI
st.title("📄 AI-Powered ATS Resume Parser")
st.write("Upload a resume to extract key details automatically!")
uploaded_file = st.file_uploader("Upload Resume (PDF or TXT)", type=["pdf", "txt"])
if uploaded_file is not None:
file_extension = uploaded_file.name.split(".")[-1]
if file_extension == "pdf":
resume_text = extract_text_from_pdf(uploaded_file)
else:
resume_text = uploaded_file.read().decode("utf-8")
st.subheader("📌 Extracted Resume Information")
name = extract_info(resume_text, "What is the applicant's name?")
job_title = extract_info(resume_text, "What is the applicant's job title?")
skills = extract_info(resume_text, "What are the applicant's skills?")
experience = extract_info(resume_text, "How many years of experience does the applicant have?")
st.write(f"**🧑 Name:** {name}")
st.write(f"**💼 Job Title:** {job_title}")
st.write(f"**🛠 Skills:** {skills}")
st.write(f"**📅 Experience:** {experience} years")
# Job Matching (Enhanced)
st.subheader("🔍 Match Resume with Job Description")
job_desc = st.text_area("Paste the Job Description Here:")
if st.button("Match Resume"):
if not job_desc.strip():
st.warning("⚠️ Please enter a Job Description before matching.")
else:
match_score = extract_info(resume_text, f"How well does this resume match the job description: {job_desc}")
matched_skills, missing_skills = analyze_resume_vs_job(resume_text, job_desc)
st.write(f"**✅ Match Score:** {match_score}")
st.write(f"**✔ Matched Skills:** {', '.join(matched_skills) if matched_skills else 'None'}")
st.write(f"**❌ Missing Skills:** {', '.join(missing_skills) if missing_skills else 'None'}")
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