Main.py

import streamlit as st
from PyPDF2 import PdfReader
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity

# Function to extract text from PDF
def extract_text_from_pdf(file):
    pdf = PdfReader(file)
    text = ""
    for page in pdf.pages:
        page_text = page.extract_text()
        if page_text:
            text += page_text
    return text

# Function to rank resumes based on job description
def rank_resumes(job_description, resumes):
    # Combine job description with resumes
    documents = [job_description] + resumes
    vectorizer = TfidfVectorizer().fit_transform(documents)
    vectors = vectorizer.toarray()

    # Calculate cosine similarity
    job_description_vector = vectors[0]
    resume_vectors = vectors[1:]
    cosine_similarities = cosine_similarity([job_description_vector], resume_vectors).flatten()
    
    # Scale similarity scores to range 1 to 100
    scores = (cosine_similarities * 100).round(2)
    return scores

# Streamlit app
st.title("🤖 AI Resume Screening & Candidate Ranking System")

# Job description input
st.header("📝 Job Description")
job_description = st.text_area("Enter the job description")

# File uploader
st.header("📄 Upload Resumes")
uploaded_files = st.file_uploader("Upload PDF files", type=["pdf"], accept_multiple_files=True)

# Process and rank resumes
if uploaded_files and job_description:
    st.header("📊 Resume Rankings")

    try:
        resumes = []
        for file in uploaded_files:
            text = extract_text_from_pdf(file)
            resumes.append(text)

        # Rank resumes
        scores = rank_resumes(job_description, resumes)

        # Display results
        results = pd.DataFrame({
            "Resume": [file.name for file in uploaded_files], 
            "Score (out of 100)": scores
        }).sort_values(by="Score (out of 100)", ascending=False)

        st.write(results)
    except Exception as e:
        st.error(f"An error occurred: {str(e)}")