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from transformers import pipeline
# Initialize the HuggingFace pipeline for text generation
generator = pipeline("text-generation", model="gpt-3")
def generate_resume(name, job_title, skills, experiences, education):
resume_template = f"""
Name: {name}
Job Title: {job_title}
Skills: {skills}
Work Experience: {experiences}
Education: {education}
"""
# Use the generator to enhance the resume
resume = generator(resume_template, max_length=400, num_return_sequences=1)[0]['generated_text']
return resume
# Example usage
name = "John Doe"
job_title = "Software Engineer"
skills = "Python, Java, Machine Learning, Data Analysis"
experiences = "Worked as a software engineer at ABC Corp, developed web applications using Python."
education = "BSc in Computer Science from XYZ University."
resume = generate_resume(name, job_title, skills, experiences, education)
print(resume)
from transformers import pipeline
# Initialize the HuggingFace pipeline for text generation
generator = pipeline("text-generation", model="gpt-3")
def generate_interview_questions(job_role):
prompt = f"Generate a list of interview questions for a {job_role} role."
# Generate the questions using GPT
questions = generator(prompt, max_length=100, num_return_sequences=1)[0]['generated_text']
return questions
# Example usage
job_role = "Data Scientist"
interview_questions = generate_interview_questions(job_role)
print(interview_questions)
from transformers import pipeline
# Initialize the HuggingFace pipeline for text generation
generator = pipeline("text-generation", model="gpt-3")
def generate_interview_questions(job_role):
prompt = f"Generate a list of interview questions for a {job_role} role."
# Generate the questions using GPT
questions = generator(prompt, max_length=100, num_return_sequences=1)[0]['generated_text']
return questions
# Example usage
job_role = "Data Scientist"
interview_questions = generate_interview_questions(job_role)
print(interview_questions)
from transformers import pipeline
# Initialize the HuggingFace pipeline for text generation
generator = pipeline("text-generation", model="gpt-3")
def generate_career_advice(skills, interests):
prompt = f"Given the skills {skills} and interests {interests}, suggest some career paths and advice."
# Generate personalized career coaching advice
career_advice = generator(prompt, max_length=200, num_return_sequences=1)[0]['generated_text']
return career_advice
# Example usage
skills = "Data Science, Python, Machine Learning"
interests = "Artificial Intelligence, Data Analytics"
career_advice = generate_career_advice(skills, interests)
print(career_advice)
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