[RESUNINJA-32] Resume Section Extraction Module Development

app/models/dataPreprocessing/sectionExtrator.py

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+import re
+
+"""
+    Extracts specific sections from a given text, including skills, education, and work experience.
+
+    Parameters:
+    - text (str): The input text containing information about skills, education, and work experience.
+
+    Returns:
+    A tuple containing three elements:
+    - skills (str or None): Extracted skills section from the input text.
+    - education (str or None): Extracted education section from the input text.
+    - experience (str or None): Extracted work experience section from the input text.
+
+    The function uses regular expressions to identify and extract each section based on predefined patterns.
+
+    Example:
+    >>> resume_text = "Skills: Python, Java. Education: Master's in Computer Science. Company: ABC Inc."
+    >>> extract_sections(resume_text)
+    ('Python, Java', "Master's in Computer Science", 'ABC Inc.')
+
+    Note:
+    Ensure that the regular expressions cover various cases and variations in the input text to improve accuracy.
+"""
+
+
+# Defining a function to extract the specific section of the text.
+def extract_sections(text: str) -> tuple:
+    # Define regular expressions for skills, education, and work experience
+    skills_pattern = re.compile(r'\bSkills\b(.*?)(?=\w+\s*-\s*Exprience|\bEducation\b|\bCompany\b|$)',
+                                re.DOTALL | re.IGNORECASE)
+    education_pattern = re.compile(r'\bEducation\b(.*?)(?=\bSkills\b|\bCompany\b|$)', re.DOTALL | re.IGNORECASE)
+    work_experience_pattern = re.compile(r'\bCompany\b(.*?)(?=\bSkills\b|\bEducation\b|$)', re.DOTALL | re.IGNORECASE)
+
+    # Extract matches
+    skills_match = skills_pattern.search(text)
+    education_match = education_pattern.search(text)
+    work_experience_match = work_experience_pattern.search(text)
+
+    skills = skills_match.group(1).strip() if skills_match else None
+    education = education_match.group(1).strip() if education_match else None
+    experience = work_experience_match.group(1).strip() if work_experience_match else None
+
+    # Return the extracted sections
+    return skills, education, experience
+
+
+# Example usage of the extract_sections function
+resume_text = """
+Skills * Programming Languages: Python (pandas, numpy, scipy, scikit-learn, matplotlib), Sql, Java, JavaScript/JQuery.
+* Machine learning: Regression, SVM, Naïve Bayes, KNN, Random Forest, Decision Trees, Boosting techniques, Cluster Analysis,
+Word Embedding, Sentiment Analysis, Natural Language processing, Dimensionality reduction, Topic Modelling (LDA, NMF), PCA & Neural Nets.
+* Database Visualizations: Mysql, SqlServer, Cassandra, Hbase, ElasticSearch D3.js, DC.js, Plotly, kibana, matplotlib, ggplot, Tableau.
+* Others: Regular Expression, HTML, CSS, Angular 6, Logstash, Kafka, Python Flask, Git, Docker, computer vision - Open CV and understanding of Deep learning.
+Education Details
+Data Science Assurance Associate
+Data Science Assurance Associate - Ernst & Young LLP
+Skill Details
+JAVASCRIPT- Experience - 24 months
+jQuery- Experience - 24 months
+Python- Experience - 24 months
+Company Details
+"""
+
+# Call the function to extract sections
+skills_demo, education_demo, experience_demo = extract_sections(resume_text)
+
+# Display the extracted sections
+print("Skills:", skills_demo)
+print("Education:", education_demo)
+print("Experience:", experience_demo)