app.py

import streamlit as st
import docx2txt
import fitz  # PyMuPDF
import re
import nltk
from collections import Counter
nltk.download('punkt')

# Helper function to extract text from uploaded resume
def extract_text(uploaded_file):
    if uploaded_file.name.endswith('.pdf'):
        pdf_document = fitz.open(stream=uploaded_file.read(), filetype="pdf")
        text = ""
        for page in pdf_document:
            text += page.get_text()
        return text
    elif uploaded_file.name.endswith('.docx'):
        return docx2txt.process(uploaded_file)
    else:
        return None

# Simple JD keyword extractor
def extract_keywords(jd_text):
    words = nltk.word_tokenize(jd_text)
    words = [w.lower() for w in words if w.isalpha()]
    common_words = Counter(words)
    keywords = [word for word, freq in common_words.items() if freq >= 1]
    return keywords

# Simple resume enhancer
def enhance_resume(resume_text, keywords):
    enhanced_text = resume_text
    missing_keywords = []
    for keyword in keywords:
        if keyword.lower() not in resume_text.lower():
            missing_keywords.append(keyword)
    if missing_keywords:
        enhanced_text += "\n\nAdditional Skills: " + ", ".join(missing_keywords)
    return enhanced_text, missing_keywords

# Streamlit App
st.title("JobForge - Resume Optimizer")
st.write("Upload your resume and paste a job description to optimize your resume!")

uploaded_resume = st.file_uploader("Upload Your Resume", type=["pdf", "docx"])

job_description = st.text_area("Paste Job Description Here")

if st.button("Forge My Resume"):
    if uploaded_resume and job_description:
        with st.spinner('Forging your resume...'):
            resume_text = extract_text(uploaded_resume)
            jd_keywords = extract_keywords(job_description)
            enhanced_resume, missing_keywords = enhance_resume(resume_text, jd_keywords)
            
            st.subheader("Optimized Resume Preview:")
            st.text_area("", enhanced_resume, height=400)
            
            st.subheader("Match Score:")
            match_percent = (len(jd_keywords) - len(missing_keywords)) / len(jd_keywords) * 100
            st.write(f"Your resume matches {match_percent:.2f}% of the job description.")
            
            st.subheader("Missing Keywords:")
            if missing_keywords:
                st.write(", ".join(missing_keywords))
            else:
                st.success("Your resume covers all keywords!")
    else:
        st.error("Please upload a resume and paste a job description to proceed.")