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| import streamlit as st | |
| from datasets import load_dataset | |
| import pandas as pd | |
| from transformers import pipeline | |
| import time | |
| # Constants | |
| universities_url = "https://www.4icu.org/top-universities-world/" | |
| # Load datasets with caching to optimize performance | |
| def load_datasets(): | |
| ds_jobs = load_dataset("lukebarousse/data_jobs") | |
| ds_courses = load_dataset("azrai99/coursera-course-dataset") | |
| ds_custom_courses = pd.read_csv("final_cleaned_merged_coursera_courses.csv") | |
| ds_custom_jobs = pd.read_csv("merged_data_science_jobs.csv") | |
| ds_custom_universities = pd.read_csv("merged_university_data_cleaned (1).csv") | |
| return ds_jobs, ds_courses, ds_custom_courses, ds_custom_jobs, ds_custom_universities | |
| ds_jobs, ds_courses, ds_custom_courses, ds_custom_jobs, ds_custom_universities = load_datasets() | |
| # Initialize the pipeline with caching, using an accessible model like 'google/flan-t5-large' | |
| def load_pipeline(): | |
| return pipeline("text2text-generation", model="google/flan-t5-large") | |
| qa_pipeline = load_pipeline() | |
| # 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 | |
| if st.sidebar.button("Save Profile"): | |
| with st.spinner('Saving your profile...'): | |
| time.sleep(2) # Simulate processing time | |
| st.session_state.profile_data = { | |
| "educational_background": educational_background, | |
| "interests": interests, | |
| "tech_skills": tech_skills, | |
| "soft_skills": soft_skills | |
| } | |
| 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: | |
| with st.spinner('Processing your question...'): | |
| answer = qa_pipeline(question)[0]["generated_text"] | |
| time.sleep(2) # Simulate processing time | |
| st.write("Answer:", answer) | |
| # Career and Job Recommendations Section | |
| st.header("Job Recommendations") | |
| if "profile_data" in st.session_state: | |
| with st.spinner('Generating job recommendations...'): | |
| time.sleep(2) # Simulate processing time | |
| job_recommendations = [] | |
| # Find jobs from ds_jobs | |
| for job in ds_jobs["train"]: | |
| job_title = job.get("job_title_short", "Unknown Job Title") | |
| job_skills = job.get("job_skills", "") or "" | |
| if any(skill.lower() in job_skills.lower() for skill in st.session_state.profile_data["tech_skills"].split(",")): | |
| job_recommendations.append(job_title) | |
| # Find jobs from ds_custom_jobs | |
| for _, job in ds_custom_jobs.iterrows(): | |
| job_title = job.get("job_title", "Unknown Job Title") | |
| job_skills = job.get("skills", "") or "" | |
| if any(skill.lower() in job_skills.lower() for skill in st.session_state.profile_data["tech_skills"].split(",")): | |
| job_recommendations.append(job_title) | |
| # Remove duplicates and keep the unique job titles | |
| job_recommendations = list(set(job_recommendations)) | |
| if job_recommendations: | |
| st.subheader("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. Here are some general recommendations:") | |
| for job in ["Data Analyst", "Software Engineer", "Project Manager", "Research Scientist", "Business Analyst"][:5]: | |
| st.write("- ", job) | |
| # Course Suggestions Section | |
| st.header("Recommended Courses") | |
| if "profile_data" in st.session_state: | |
| with st.spinner('Finding courses related to your profile...'): | |
| time.sleep(2) # Simulate processing time | |
| course_recommendations = [] | |
| # Find relevant courses in ds_courses | |
| for course in ds_courses["train"]: | |
| if any(interest.lower() in course.get("Course Name", "").lower() for interest in st.session_state.profile_data["interests"].split(",")): | |
| course_recommendations.append({ | |
| "name": course.get("Course Name", "Unknown Course Title"), | |
| "url": course.get("Links", "#") | |
| }) | |
| # Find relevant courses in ds_custom_courses | |
| for _, row in ds_custom_courses.iterrows(): | |
| if any(interest.lower() in row["Course Name"].lower() for interest in st.session_state.profile_data["interests"].split(",")): | |
| course_recommendations.append({ | |
| "name": row["Course Name"], | |
| "url": row.get("Links", "#") | |
| }) | |
| # Remove duplicates from course recommendations by converting to a set of tuples and back to a list | |
| course_recommendations = list({(course["name"], course["url"]) for course in course_recommendations}) | |
| # If there are fewer than 5 exact matches, add nearly related courses | |
| if len(course_recommendations) < 5: | |
| for course in ds_courses["train"]: | |
| if len(course_recommendations) >= 5: | |
| break | |
| if any(skill.lower() in course.get("Course Name", "").lower() for skill in st.session_state.profile_data["tech_skills"].split(",")): | |
| course_recommendations.append((course.get("Course Name", "Unknown Course Title"), course.get("Links", "#"))) | |
| for _, row in ds_custom_courses.iterrows(): | |
| if len(course_recommendations) >= 5: | |
| break | |
| if any(skill.lower() in row["Course Name"].lower() for skill in st.session_state.profile_data["tech_skills"].split(",")): | |
| course_recommendations.append((row["Course Name"], row.get("Links", "#"))) | |
| # Remove duplicates again after adding nearly related courses | |
| course_recommendations = list({(name, url) for name, url in course_recommendations}) | |
| if course_recommendations: | |
| st.write("Here are the top 5 courses related to your interests:") | |
| for course in course_recommendations[:5]: # Limit to top 5 course recommendations | |
| st.write(f"- [{course[0]}]({course[1]})") | |
| # 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!") | |