app.py

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)