# Import necessary libraries import streamlit as st import nltk from nltk.tokenize import word_tokenize import PyPDF2 import pandas as pd import re import matplotlib.pyplot as plt import seaborn as sns from transformers import AutoTokenizer, AutoModel import torch from sklearn.metrics.pairwise import cosine_similarity # Download necessary NLTK data nltk.download('punkt') # Define regular expressions for pattern matching float_regex = re.compile(r'^\d{1,2}(\.\d{1,2})?$') email_pattern = r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b' float_digit_regex = re.compile(r'^\d{10}$') email_with_phone_regex = re.compile(r'(\d{10}).|.(\d{10})') # Load Phi-3 model and tokenizer @st.cache_resource def load_model(): model_name = "microsoft/Phi-4-mini-instruct" tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) model = AutoModel.from_pretrained(model_name, trust_remote_code=True) return tokenizer, model tokenizer, model = load_model() # Function to extract text from PDF def extract_text_from_pdf(pdf_file): pdf_reader = PyPDF2.PdfReader(pdf_file) text = "" for page_num in range(len(pdf_reader.pages)): text += pdf_reader.pages[page_num].extract_text() return text # Function to generate embeddings using Phi-3 def get_embeddings(text): inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=4096) with torch.no_grad(): outputs = model(**inputs) embeddings = outputs.last_hidden_state.mean(dim=1).squeeze() return embeddings.numpy() # Function to calculate similarity between texts def calculate_similarity(text1, text2): emb1 = get_embeddings(text1) emb2 = get_embeddings(text2) return cosine_similarity([emb1], [emb2])[0][0] # Function to extract entities using Phi-3 def extract_entities(text): prompt = f"""Extract entities from this text in JSON format with keys: skills, education, experience. Text: {text[:3000]}""" inputs = tokenizer(prompt, return_tensors="pt", return_attention_mask=False) outputs = model.generate(**inputs, max_length=500) return tokenizer.decode(outputs[0], skip_special_tokens=True) # Streamlit Frontend st.markdown("# Resume Matching Tool 📃📃") st.markdown("An application to match resumes with job descriptions using Phi-3") # File Upload resumes_files = st.sidebar.file_uploader("Upload Resumes PDF", type=["pdf"], accept_multiple_files=True) job_descriptions_file = st.sidebar.file_uploader("Upload Job Description PDF", type=["pdf"]) if resumes_files and job_descriptions_file: # Process documents job_description_text = extract_text_from_pdf(job_descriptions_file) resumes_texts = {file.name: extract_text_from_pdf(file) for file in resumes_files} # Generate embeddings job_embedding = get_embeddings(job_description_text) resume_embeddings = {name: get_embeddings(text) for name, text in resumes_texts.items()} # Calculate similarities results = [] for name, emb in resume_embeddings.items(): similarity = cosine_similarity([emb], [job_embedding])[0][0] * 100 results.append({ "Resume": name, "Similarity Score": f"{similarity:.2f}%", "Details": "View Details" }) # Show results st.dataframe(pd.DataFrame(results)) # Detailed analysis st.subheader("Detailed Analysis") selected_resume = st.selectbox("Select Resume", list(resumes_texts.keys())) if selected_resume: resume_text = resumes_texts[selected_resume] # Entity extraction using Phi-3 st.write("### Extracted Entities") entities = extract_entities(resume_text) st.code(entities, language="json") # Skills matching st.write("### Skills Matching") job_entities = extract_entities(job_description_text) # Simple text-based matching resume_skills = re.findall(r'"skills": \[(.*?)\]', entities, re.DOTALL) job_skills = re.findall(r'"skills": \[(.*?)\]', job_entities, re.DOTALL) if resume_skills and job_skills: resume_skills_list = [s.strip().lower() for s in resume_skills[0].split(',')] job_skills_list = [s.strip().lower() for s in job_skills[0].split(',')] matched_skills = list(set(resume_skills_list) & set(job_skills_list)) st.write(f"**Matched Skills ({len(matched_skills)}):** {', '.join(matched_skills)}") # Visualization st.write("### Similarity Heatmap") skills_keywords = st.text_input("Enter skills for heatmap (comma-separated):").split(',') if skills_keywords: heatmap_data = [] for skill in skills_keywords: skill_emb = get_embeddings(skill.strip()) row = [] for name, emb in resume_embeddings.items(): row.append(cosine_similarity([emb], [skill_emb])[0][0]) heatmap_data.append(row) plt.figure(figsize=(12, 8)) sns.heatmap(pd.DataFrame(heatmap_data, columns=list(resumes_texts.keys()), index=skills_keywords), annot=True, cmap="YlGnBu") st.pyplot(plt) else: st.warning("Please upload both resumes and job description to proceed.")