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| import streamlit as st | |
| from datasets import load_dataset | |
| from transformers import pipeline | |
| import requests | |
| # Load necessary datasets from Hugging Face | |
| ds_natural_questions = load_dataset("google-research-datasets/natural_questions", "default") | |
| ds_open_questions = load_dataset("launch/open_question_type") | |
| ds_question_generator = load_dataset("iarfmoose/question_generator") | |
| ds_jobs = load_dataset("lukebarousse/data_jobs") | |
| ds_courses = load_dataset("azrai99/coursera-course-dataset") | |
| universities_url = "https://www.4icu.org/top-universities-world/" | |
| # Initialize the LLaMA model pipeline for text-to-text generation | |
| qa_pipeline = pipeline("text2text-generation", model="llama-3.1-70b-versatile", tokenizer="llama-3.1-70b-versatile") | |
| # Streamlit App Interface | |
| st.title("Career Counseling Application") | |
| st.subheader("Build Your Profile and Discover Tailored Career Recommendations") | |
| # Sidebar for Profile Setup | |
| st.sidebar.header("Profile Setup") | |
| educational_background = st.sidebar.text_input("Educational Background (e.g., Degree, Major)") | |
| interests = st.sidebar.text_input("Interests (e.g., AI, Data Science, Engineering)") | |
| tech_skills = st.sidebar.text_area("Technical Skills (e.g., Python, SQL, Machine Learning)") | |
| soft_skills = st.sidebar.text_area("Soft Skills (e.g., Communication, Teamwork)") | |
| # Save profile data for session-based recommendations | |
| profile_data = { | |
| "educational_background": educational_background, | |
| "interests": interests, | |
| "tech_skills": tech_skills, | |
| "soft_skills": soft_skills | |
| } | |
| if st.sidebar.button("Save Profile"): | |
| st.session_state.profile_data = profile_data | |
| st.sidebar.success("Profile saved successfully!") | |
| # Intelligent Q&A Section | |
| st.header("Intelligent Q&A") | |
| question = st.text_input("Ask a career-related question:") | |
| if question: | |
| answer = qa_pipeline(question)[0]["generated_text"] | |
| st.write("Answer:", answer) | |
| # Career and Job Recommendations Section | |
| st.header("Career and Job Recommendations") | |
| if profile_data: | |
| job_recommendations = [] | |
| for job in ds_jobs["train"]: | |
| if any(skill.lower() in job["description"].lower() for skill in tech_skills.split(',')): | |
| job_recommendations.append(job["title"]) | |
| if job_recommendations: | |
| st.subheader("Job Recommendations") | |
| st.write("Based on your profile, here are some potential job roles:") | |
| for job in job_recommendations[:5]: # Limit to top 5 job recommendations | |
| st.write("- ", job) | |
| else: | |
| st.write("No specific job recommendations found matching your profile.") | |
| # Course Suggestions Section | |
| st.header("Course Suggestions") | |
| if profile_data: | |
| course_recommendations = [] | |
| for course in ds_courses["train"]: | |
| if any(interest.lower() in course["title"].lower() for interest in interests.split(',')): | |
| course_recommendations.append(course["title"]) | |
| if course_recommendations: | |
| st.subheader("Recommended Courses") | |
| st.write("Here are some courses related to your interests:") | |
| for course in course_recommendations[:5]: # Limit to top 5 course recommendations | |
| st.write("- ", course) | |
| else: | |
| st.write("No specific courses found matching your interests.") | |
| # University Recommendations Section | |
| st.header("Top Universities") | |
| st.write("For further education, you can explore the top universities worldwide:") | |
| st.write(f"[View Top Universities Rankings]({universities_url})") | |
| # Conclusion | |
| st.write("Thank you for using the Career Counseling Application!") | |