Delete bert.py

bert.py

@@ -1,238 +0,0 @@
-# Import necessary libraries
-import streamlit as st
-import nltk
-from gensim.models.doc2vec import Doc2Vec, TaggedDocument
-from nltk.tokenize import word_tokenize
-import PyPDF2
-import pandas as pd
-import re
-import matplotlib.pyplot as plt
-import seaborn as sns
-import spacy
-
-# 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})')
-
-# Function to extract text from a PDF file
-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 tokenize text using the NLP model
-def tokenize_text(text, nlp_model):
-    doc = nlp_model(text, disable=["tagger", "parser"])
-    tokens = [(token.text.lower(), token.label_) for token in doc.ents]
-    return tokens
-
-# Function to extract CGPA from a resume
-def extract_cgpa(resume_text):
-    cgpa_pattern = r'\b(?:CGPA|GPA|C\.G\.PA|Cumulative GPA)\s*:?[\s-]([0-9]+(?:\.[0-9]+)?)\b|\b([0-9]+(?:\.[0-9]+)?)\s(?:CGPA|GPA)\b'
-    match = re.search(cgpa_pattern, resume_text, re.IGNORECASE)
-    if match:
-        cgpa = match.group(1) if match.group(1) else match.group(2)
-        return float(cgpa)
-    else:
-        return None
-
-# Function to extract skills from a resume
-def extract_skills(text, skills_keywords):
-    skills = [skill.lower() for skill in skills_keywords if re.search(r'\b' + re.escape(skill.lower()) + r'\b', text.lower())]
-    return skills
-
-# Function to preprocess text
-def preprocess_text(text):
-    return word_tokenize(text.lower())
-
-# Function to train a Doc2Vec model
-def train_doc2vec_model(documents):
-    model = Doc2Vec(vector_size=20, min_count=2, epochs=50)
-    model.build_vocab(documents)
-    model.train(documents, total_examples=model.corpus_count, epochs=model.epochs)
-    return model
-
-# Function to calculate similarity between two texts
-def calculate_similarity(model, text1, text2):
-    vector1 = model.infer_vector(preprocess_text(text1))
-    vector2 = model.infer_vector(preprocess_text(text2))
-    return model.dv.cosine_similarities(vector1, [vector2])[0]
-
-# Function to calculate accuracy
-def accuracy_calculation(true_positives, false_positives, false_negatives):
-    total = true_positives + false_positives + false_negatives
-    accuracy = true_positives / total if total != 0 else 0
-    return accuracy
-
-# Streamlit Frontend
-st.markdown("# Resume Matching Tool 📃📃")
-st.markdown("An application to match resumes with a job description.")
-
-# Sidebar - File Upload for Resumes
-st.sidebar.markdown("## Upload Resumes PDF")
-resumes_files = st.sidebar.file_uploader("Upload Resumes PDF", type=["pdf"], accept_multiple_files=True)
-
-if resumes_files:
-    # Sidebar - File Upload for Job Descriptions
-    st.sidebar.markdown("## Upload Job Description PDF")
-    job_descriptions_file = st.sidebar.file_uploader("Upload Job Description PDF", type=["pdf"])
-
-    if job_descriptions_file:
-        # Load the pre-trained NLP model
-        nlp_model_path = "en_Resume_Matching_Keywords"
-        nlp = spacy.load(nlp_model_path)
-
-        # Backend Processing
-        job_description_text = extract_text_from_pdf(job_descriptions_file)
-        resumes_texts = [extract_text_from_pdf(resume_file) for resume_file in resumes_files]
-        job_description_text = extract_text_from_pdf(job_descriptions_file)
-        job_description_tokens = tokenize_text(job_description_text, nlp)
-
-        # Initialize counters
-        overall_skill_matches = 0
-        overall_qualification_matches = 0
-
-        # Create a list to store individual results
-        results_list = []
-        job_skills = set()
-        job_qualifications = set()
-
-        for job_token, job_label in job_description_tokens:
-            if job_label == 'QUALIFICATION':
-                job_qualifications.add(job_token.replace('\n', ' '))
-            elif job_label == 'SKILLS':
-                job_skills.add(job_token.replace('\n', ' '))
-
-        job_skills_number = len(job_skills)
-        job_qualifications_number = len(job_qualifications)
-
-        # Lists to store counts of matched skills for all resumes
-        skills_counts_all_resumes = []
-
-        # Iterate over all uploaded resumes
-        for uploaded_resume in resumes_files:
-            resume_text = extract_text_from_pdf(uploaded_resume)
-            resume_tokens = tokenize_text(resume_text, nlp)
-
-            # Initialize counters for individual resume
-            skillMatch = 0
-            qualificationMatch = 0
-            cgpa = ""
-
-            # Lists to store matched skills and qualifications for each resume
-            matched_skills = set()
-            matched_qualifications = set()
-            email = set()
-            phone = set()
-            name = set()
-
-            # Compare the tokens in the resume with the job description
-            for resume_token, resume_label in resume_tokens:
-                for job_token, job_label in job_description_tokens:
-                    if resume_token.lower().replace('\n', ' ') == job_token.lower().replace('\n', ' '):
-                        if resume_label == 'SKILLS':
-                            matched_skills.add(resume_token.replace('\n', ' '))
-                        elif resume_label == 'QUALIFICATION':
-                            matched_qualifications.add(resume_token.replace('\n', ' '))
-                    elif resume_label == 'PHONE' and bool(float_digit_regex.match(resume_token)):
-                        phone.add(resume_token)  
-                    elif resume_label == 'QUALIFICATION':
-                        matched_qualifications.add(resume_token.replace('\n', ' '))
-
-            skillMatch = len(matched_skills)
-            qualificationMatch = len(matched_qualifications)
-
-            # Convert the list of emails to a set
-            email_set = set(re.findall(email_pattern, resume_text.replace('\n', ' ')))
-            email.update(email_set)
-
-            numberphone=""
-            for email_str in email:
-                numberphone = email_with_phone_regex.search(email_str)
-                if numberphone:
-                    email.remove(email_str)
-                    val=numberphone.group(1) or numberphone.group(2)
-                    phone.add(val)
-                    email.add(email_str.strip(val))
-
-            # Increment overall counters based on matches
-            overall_skill_matches += skillMatch
-            overall_qualification_matches += qualificationMatch
-
-            # Add count of matched skills for this resume to the list
-            skills_counts_all_resumes.append([resume_text.count(skill.lower()) for skill in job_skills])
-
-            # Create a dictionary for the current resume and append to the results list
-            result_dict = {
-                "Resume": uploaded_resume.name,
-                "Similarity Score": (skillMatch/job_skills_number)*100,
-                "Skill Matches": skillMatch,
-                "Matched Skills": matched_skills,
-                "CGPA": extract_cgpa(resume_text),
-                "Email": email,
-                "Phone": phone,
-                "Qualification Matches": qualificationMatch,
-                "Matched Qualifications": matched_qualifications
-            }
-
-            results_list.append(result_dict)
-
-        # Display overall matches
-        st.subheader("Overall Matches")
-        st.write(f"Total Skill Matches: {overall_skill_matches}")
-        st.write(f"Total Qualification Matches: {overall_qualification_matches}")
-        st.write(f"Job Qualifications: {job_qualifications}")
-        st.write(f"Job Skills: {job_skills}")
-
-        # Display individual results in a table
-        results_df = pd.DataFrame(results_list)
-        st.subheader("Individual Results")
-        st.dataframe(results_df)
-        tagged_resumes = [TaggedDocument(words=preprocess_text(text), tags=[str(i)]) for i, text in enumerate(resumes_texts)]
-        model_resumes = train_doc2vec_model(tagged_resumes)
-
-        st.subheader("\nHeatmap:")
-       
-        # Get skills keywords from user input
-        skills_keywords_input = st.text_input("Enter skills keywords separated by commas (e.g., python, java, machine learning):")
-        skills_keywords = [skill.strip() for skill in skills_keywords_input.split(',') if skill.strip()]
-
-        if skills_keywords:
-            # Calculate the similarity score between each skill keyword and the resume text
-            skills_similarity_scores = []
-            for resume_text in resumes_texts:
-                resume_text_similarity_scores = []
-                for skill in skills_keywords:
-                    similarity_score = calculate_similarity(model_resumes, resume_text, skill)
-                    resume_text_similarity_scores.append(similarity_score)
-                skills_similarity_scores.append(resume_text_similarity_scores)
-
-            # Create a DataFrame with the similarity scores and set the index to the names of the PDFs
-            skills_similarity_df = pd.DataFrame(skills_similarity_scores, columns=skills_keywords, index=[resume_file.name for resume_file in resumes_files])
-
-            # Plot the heatmap
-            fig, ax = plt.subplots(figsize=(12, 8))
-            sns.heatmap(skills_similarity_df, cmap='YlGnBu', annot=True, fmt=".2f", ax=ax)
-            ax.set_title('Heatmap for Skills Similarity')
-            ax.set_xlabel('Skills')
-            ax.set_ylabel('Resumes')
-
-            # Rotate the y-axis labels for better readability
-            plt.yticks(rotation=0)
-
-            # Display the Matplotlib figure using st.pyplot()
-            st.pyplot(fig)
-        else:
-            st.write("Please enter at least one skill keyword.")
-
-    else:
-        st.warning("Please upload the Job Description PDF to proceed.")
-else:
-    st.warning("Please upload Resumes PDF to proceed.")