Create app.py

app.py

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+pip install transformers
+pip install torch
+
+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)