app.py

import streamlit as st
import pandas as pd
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
import nltk
import sklearn
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
import re
from collections import defaultdict
import os
from docx import Document
import PyPDF2
import shutil
import zipfile


nltk.download('punkt', quiet=True)
nltk.download('stopwords', quiet=True)

def extract_text_from_docx(docx_path):
    doc = Document(docx_path)
    return " ".join([paragraph.text for paragraph in doc.paragraphs])

def extract_text_from_pdf(pdf_path):
    with open(pdf_path, 'rb') as file:
        reader = PyPDF2.PdfReader(file)
        return " ".join([page.extract_text() for page in reader.pages])

def process_resume_directory(directory_path):
    resume_texts = {}
    for filename in os.listdir(directory_path):
        file_path = os.path.join(directory_path, filename)
        try:
            if filename.endswith(".docx"):
                text = extract_text_from_docx(file_path)
            elif filename.endswith(".pdf"):
                text = extract_text_from_pdf(file_path)
            else:
                continue  # Skip files that are neither DOCX nor PDF
            resume_texts[filename] = text
        except Exception as e:
            st.error(f"Error processing {filename}: {str(e)}")
    return resume_texts

def preprocess_text(text):
    tokens = word_tokenize(str(text).lower())
    stop_words = set(stopwords.words('english'))
    tokens = [token for token in tokens if token.isalpha() and token not in stop_words]
    return ' '.join(tokens)

def classify_resume(text):
    classification = defaultdict(str)

    job_roles = {
        "software": ["software engineer", "developer", "programmer"],
        "data": ["data scientist", "data analyst", "machine learning"],
        "marketing": ["marketing", "seo", "social media"],
        "finance": ["accountant", "financial analyst", "bookkeeper"],
    }
    
    for role, keywords in job_roles.items():
        if any(keyword in text.lower() for keyword in keywords):
            classification["job role"] = role
            break

    education_levels = ["High School", "Associate", "Bachelor", "Master", "PhD"]
    for level in education_levels:
        if level.lower() in text.lower():
            classification["education"] = level
            break

    experience_match = re.search(r"(\d+)\s*(?:years?|yrs?)(?:\s+of)?\s+experience", text, re.IGNORECASE)
    if experience_match:
        classification["years_experience"] = experience_match.group(1)

    skills = ["Python", "Java", "C++", "JavaScript", "SQL", "AWS", "Docker", "Kubernetes",
              "Machine Learning", "Data Analysis", "Project Management", "Agile", "Scrum"]
    found_skills = [skill for skill in skills if skill.lower() in text.lower()]
    classification["skills"] = ", ".join(found_skills)

    phone_pattern = r'\b(?:\+?1[-.\s]?)?(?:\(\d{3}\)|\d{3})[-.\s]?\d{3}[-.\s]?\d{4}\b'
    phone_match = re.search(phone_pattern, text)
    if phone_match:
        classification["phone number"] = phone_match.group()

    address_pattern = r'\d{1,5}\s\w+\s\w+\.?(?:\s\w+\.?)?\s*,?\s*\w+\s*,?\s*[A-Z]{2}\s*\d{5}'
    address_match = re.search(address_pattern, text)
    if address_match:
        classification["address"] = address_match.group()

    return classification

def create_resume_ranking_model(job_description, resume_directory):
    # Process resumes
    resume_texts = process_resume_directory(resume_directory)

    classified_resumes = {filename: classify_resume(text) for filename, text in resume_texts.items()}

    df = pd.DataFrame.from_dict(classified_resumes, orient='index')
    df['filename'] = df.index
    df.reset_index(drop=True, inplace=True)

    df['combined_text'] = df[['education', 'job role', 'skills']].apply(lambda x: ' '.join(x.dropna().astype(str)), axis=1)

    df['combined_text'] += ' ' + df['years_experience'].astype(str) + ' years experience'

    preprocessed_jd = preprocess_text(job_description)
    preprocessed_resumes = df['combined_text'].apply(preprocess_text)

    vectorizer = TfidfVectorizer()

    tfidf_matrix = vectorizer.fit_transform([preprocessed_jd] + list(preprocessed_resumes))

    cosine_similarities = cosine_similarity(tfidf_matrix[0:1], tfidf_matrix[1:]).flatten()

    df['similarity_score'] = cosine_similarities

    ranked_resumes = df.sort_values('similarity_score', ascending=False).reset_index(drop=True)
    
    return ranked_resumes


import streamlit as st
import tempfile
import os

st.title('Resume Ranking System')

st.write("""
This app ranks resumes based on their similarity to a given job description.
Upload resume files (PDF and DOCX formats) and enter a job description to get started.
""")

job_description = st.text_area("Enter the job description:", height=200)

uploaded_files = st.file_uploader("Upload resume files", accept_multiple_files=True, type=['pdf', 'docx'])

if st.button('Rank Resumes'):
    if job_description and uploaded_files:
        try:
            with tempfile.TemporaryDirectory() as temp_dir:
                for uploaded_file in uploaded_files:
                    file_path = os.path.join(temp_dir, uploaded_file.name)
                    with open(file_path, "wb") as f:
                        f.write(uploaded_file.getbuffer())
                
                with st.spinner('Processing resumes...'):
                    ranked_resumes = create_resume_ranking_model(job_description, temp_dir)
                
                st.success('Resumes ranked successfully!')
                
                st.write("Top 5 Ranked Resumes:")
                st.dataframe(ranked_resumes.head())
                
                output_folder = "ranked_resumes"
                if os.path.exists(output_folder):
                    shutil.rmtree(output_folder)
                os.makedirs(output_folder)
                
                for index, row in ranked_resumes.iterrows():
                    src_file = os.path.join(temp_dir, row['filename'])
                    dst_file = os.path.join(output_folder, f"{index+1:03d}_{row['filename']}")
                    shutil.copy2(src_file, dst_file)
               
                shutil.make_archive(output_folder, 'zip', output_folder)
                
                with open(f"{output_folder}.zip", "rb") as file:
                    st.download_button(
                        label="Download ranked resumes as ZIP",
                        data=file,
                        file_name="ranked_resumes.zip",
                        mime="application/zip"
                    )
               
                csv = ranked_resumes.to_csv(index=False)
                st.download_button(
                    label="Download full results as CSV",
                    data=csv,
                    file_name="ranked_resumes.csv",
                    mime="text/csv",
                )
        except Exception as e:
            st.error(f"An error occurred: {str(e)}")
    else:
        st.warning("Please enter a job description and upload at least one resume file.")

st.write("""
### How to use this app:
1. Enter the job description in the text area above.
2. Upload resume files (PDF and DOCX formats) using the file uploader.
3. Click the 'Rank Resumes' button.
4. View the top 5 ranked resumes in the table.
5. Download the ranked resumes as a ZIP file.
6. Download the full results as a CSV file if needed.
""")