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| import streamlit as st | |
| import pdfplumber | |
| import pandas as pd | |
| from sentence_transformers import SentenceTransformer, util | |
| model = SentenceTransformer('all-MiniLM-L6-v2') | |
| def extract_information_from_cv(pdf_content): | |
| with pdfplumber.open(pdf_content) as pdf: | |
| text = "" | |
| for page in pdf.pages: | |
| text += page.extract_text() | |
| return text | |
| def extract_title(text): | |
| # Extract the title section | |
| Title_start = text.find("Title:") | |
| Title_end = text.find("Name:") | |
| return text[Title_start + len("Title:"):Title_end].strip() if Title_start != -1 and Title_end != -1 else None | |
| def extract_name(text): | |
| # Extract the name section | |
| Name_start = text.find("Name:") | |
| Name_end = text.find("Email:") | |
| return text[Name_start + len("Name:"):Name_end].strip() if Name_start != -1 and Name_end != -1 else None | |
| def extract_Email(text): | |
| # Extract the Email section | |
| Email_start = text.find("Email:") | |
| Email_end = text.find("Phone:") | |
| return text[Email_start + len("Email:"):Email_end].strip() if Email_start != -1 and Email_end != -1 else None | |
| def extract_Phone(text): | |
| # Extract the Phone section | |
| Phone_start = text.find("Phone:") | |
| Phone_end = text.find("LinkedIn:") | |
| return text[Phone_start + len("Phone:"):Phone_end].strip() if Phone_start != -1 and Phone_end != -1 else None | |
| def extract_LinkedIn(text): | |
| # Extract the LinkedIn section | |
| LinkedIn_start = text.find("LinkedIn:") | |
| LinkedIn_end = text.find("GitHub:") | |
| return text[LinkedIn_start + len("LinkedIn:"):LinkedIn_end].strip() if LinkedIn_start != -1 and LinkedIn_end != -1 else None | |
| def extract_Github(text): | |
| # Extract the Github section | |
| Github_start = text.find("GitHub:") | |
| Github_end = text.find("Summary:") | |
| return text[Github_start + len("GitHub:"):Github_end].strip() if Github_start != -1 and Github_end != -1 else None | |
| def extract_summary(text): | |
| summary_start = text.find("Summary:") | |
| summary_end = text.find("Education:") | |
| return text[summary_start + len("Summary:"):summary_end].strip() if summary_start != -1 and summary_end != -1 else None | |
| def extract_education(text): | |
| education_start = text.find("Education:") | |
| education_end = text.find("Internship:") | |
| return text[education_start + len("Education:"):education_end].strip() if education_start != -1 and education_end != -1 else None | |
| def extract_Internship(text): | |
| Internship_start = text.find("Internship:") | |
| Internship_end = text.find("Professional Experience:") | |
| return text[Internship_start + len("Internship:"):Internship_end].strip() if Internship_start != -1 and Internship_end != -1 else None | |
| def extract_experience(text): | |
| exp_start = text.find("Professional Experience:") | |
| exp_end = text.find("Projects:") | |
| return text[exp_start + len("Professional Experience:"):exp_end].strip() if exp_start != -1 and exp_end != -1 else None | |
| def extract_projects(text): | |
| projects_start = text.find("Projects:") | |
| projects_end = text.find("Awards and Certifications:") | |
| return text[projects_start + len("Projects:"):projects_end].strip() if projects_start != -1 and projects_end != -1 else None | |
| def extract_certifications(text): | |
| certifications_start = text.find("Awards and Certifications:") | |
| certifications_end = text.find("Skills:") | |
| return text[certifications_start + len("Awards and Certifications:"):certifications_end].strip() if certifications_start != -1 and certifications_end != -1 else None | |
| def extract_skills(text): | |
| skills_start = text.find("Skills:") | |
| return text[skills_start + len("Skills:"):].strip() if skills_start != -1 else None | |
| def main(): | |
| st.title("CV Shortlisting App") | |
| job_description = st.text_area('Job description') | |
| uploaded_files = st.file_uploader("Choose multiple CV files", type="pdf", accept_multiple_files=True) | |
| options = [i+1 for i in range(len(uploaded_files))] | |
| no_of_candidates = st.selectbox('No of candidates need:', options) | |
| if no_of_candidates: | |
| extract_button = st.button("Extract Data") | |
| extracted_data = [] | |
| cv_data = [] | |
| if uploaded_files and extract_button: | |
| for uploaded_file in uploaded_files: | |
| cv_text = extract_information_from_cv(uploaded_file) | |
| cv_data.append(cv_text) | |
| title = extract_title(cv_text) | |
| name = extract_name(cv_text) | |
| phone = extract_Phone(cv_text) | |
| email = extract_Email(cv_text) | |
| linkedin = extract_LinkedIn(cv_text) | |
| github = extract_Github(cv_text) | |
| summary = extract_summary(cv_text) | |
| education = extract_education(cv_text) | |
| internship = extract_Internship(cv_text) | |
| experience = extract_experience(cv_text) | |
| projects = extract_projects(cv_text) | |
| certifications = extract_certifications(cv_text) | |
| skills = extract_skills(cv_text) | |
| data = { | |
| "Title": [title], | |
| "Name": [name], | |
| "Email": [email], | |
| "Phone": [phone], | |
| "LinkedIn": [linkedin], | |
| "Github": [github], | |
| "Summary": [summary], | |
| "Education": [education], | |
| "Internships":[internship], | |
| "Professional Experience": [experience], | |
| "Projects": [projects], | |
| "Awards and Certifications":[certifications], | |
| "Skills": [skills] | |
| } | |
| extracted_data.append(data) | |
| # Two lists of sentences | |
| sentences1 = job_description | |
| sentences2 = cv_data | |
| #Compute embedding for both lists | |
| embeddings1 = model.encode(sentences1, convert_to_tensor=True) | |
| embeddings2 = model.encode(sentences2, convert_to_tensor=True) | |
| #Compute cosine-similarities | |
| cosine_scores = util.cos_sim(embeddings1, embeddings2) | |
| Scores = [] | |
| #Output the pairs with their score | |
| for i in range(len(sentences2)): | |
| score = cosine_scores[0][i] | |
| Scores.append(score) | |
| st.write("### Extracted Data:") | |
| final_df = pd.DataFrame(extracted_data) | |
| final_df['Score'] = Scores | |
| df_sorted = final_df.sort_values(by='Score', ascending=False) | |
| # Extract information for the top students | |
| top_cvs = df_sorted.head(no_of_candidates) | |
| top_cv_list = [] | |
| top_emails = top_cvs['Email'].values | |
| for email in top_emails: | |
| for cv in cv_data: | |
| if email[0] in cv: | |
| top_cv_list.append(cv) | |
| st.write(df_sorted) | |
| st.subheader(f"Top {no_of_candidates} Candidates's cv") | |
| st.write(top_cv_list) | |
| if __name__ == "__main__": | |
| main() |