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| import streamlit as st | |
| import pandas as pd | |
| from transformers import pipeline | |
| import time | |
| # Load datasets from CSV files | |
| def load_csv_datasets(): | |
| jobs_data = pd.read_csv("job_descriptions.csv") | |
| courses_data = pd.read_csv("courses_data.csv") | |
| return jobs_data, courses_data | |
| jobs_data, courses_data = load_csv_datasets() | |
| # Constants | |
| universities_url = "https://www.4icu.org/top-universities-world/" | |
| # Initialize the text generation pipeline | |
| def load_pipeline(): | |
| return pipeline("text2text-generation", model="google/flan-t5-large") | |
| qa_pipeline = load_pipeline() | |
| # Streamlit App Interface | |
| st.markdown( | |
| """ | |
| <div style="display: flex; align-items: center; gap: 10px; flex-wrap: wrap;"> | |
| <h1 style="font-size: 29px; display: inline-block; margin-right: 10px;"> | |
| <img src="https://img.icons8.com/ios-filled/50/000000/graduation-cap.png" width="40" alt="Degree icon"/> | |
| Confused about which career to pursue? | |
| </h1> | |
| <h2 style="font-size: 25px; display: inline-block; margin: 0;">Let CareerCompass help you decide in two simple steps</h2> | |
| </div> | |
| """, | |
| unsafe_allow_html=True, | |
| ) | |
| # Display the appropriate subheader based on profile data status | |
| if "profile_data" not in st.session_state or not st.session_state.get("profile_data_saved", False): | |
| st.markdown("<h3 style='font-size: 20px;'>Step 1: Find out profile questions on the left sidebar and follow the instructions.</h3>", unsafe_allow_html=True) | |
| # Sidebar for Profile Setup | |
| st.sidebar.header("Profile Setup") | |
| educational_background = st.sidebar.selectbox("Educational Background", [ | |
| "Computer Science", "Engineering", "Business Administration", "Life Sciences", | |
| "Social Sciences", "Arts and Humanities", "Mathematics", "Physical Sciences", | |
| "Law", "Education", "Medical Sciences", "Other" | |
| ]) | |
| 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)") | |
| # Profile validation and saving | |
| def are_profile_fields_filled(): | |
| return all([educational_background, interests.strip(), tech_skills.strip(), soft_skills.strip()]) | |
| if st.sidebar.button("Save Profile"): | |
| if are_profile_fields_filled(): | |
| with st.spinner('Saving your profile...'): | |
| time.sleep(2) | |
| st.session_state.profile_data = { | |
| "educational_background": educational_background, | |
| "interests": interests, | |
| "tech_skills": tech_skills, | |
| "soft_skills": soft_skills | |
| } | |
| st.session_state.profile_data_saved = True # Set the profile data saved flag | |
| st.session_state.question_index = 0 # Initialize question index | |
| st.session_state.answers = {} # Initialize dictionary for answers | |
| st.session_state.ask_additional_questions = None # Reset question flag | |
| st.session_state.show_additional_question_buttons = True # Show buttons after profile save | |
| st.sidebar.success("Profile saved successfully!") | |
| st.markdown("<h2 style='font-size: 25px;'>Step 2: For more Accurate Analysis, Do you wish to provide more information?</h2>", unsafe_allow_html=True) | |
| else: | |
| st.sidebar.error("Please fill in all the fields before saving your profile.") | |
| # Button actions | |
| if "show_additional_question_buttons" in st.session_state: | |
| if st.session_state.show_additional_question_buttons: | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| if st.button("Yes, ask me more questions"): | |
| st.session_state.ask_additional_questions = True | |
| st.session_state.show_additional_question_buttons = False # Hide buttons after click | |
| with col2: | |
| if st.button("Skip and generate recommendations"): | |
| st.session_state.ask_additional_questions = False | |
| st.session_state.show_additional_question_buttons = False # Hide buttons after click | |
| # Additional questions for more tailored recommendations | |
| additional_questions = [ | |
| "What subjects do you enjoy learning about the most, and why?", | |
| "What activities or hobbies do you find most engaging and meaningful outside of school?", | |
| "Can you describe a perfect day in your dream career? What tasks would you be doing?", | |
| "Are you more inclined towards working independently or as part of a team?", | |
| "Do you prefer structured schedules or flexibility in your work?", | |
| "What values are most important to you in a career (e.g., creativity, stability, helping others)?", | |
| "How important is financial stability to you in your future career?", | |
| "Are you interested in pursuing a career that involves working with people, technology, or the environment?", | |
| "Would you prefer a career with a clear progression path or one with more entrepreneurial freedom?", | |
| "What problems or challenges do you want to solve or address through your career?" | |
| ] | |
| # Display dynamic questions or proceed to generating recommendations | |
| if "profile_data" in st.session_state: | |
| if st.session_state.get("ask_additional_questions") is True: | |
| total_questions = len(additional_questions) | |
| if "question_index" not in st.session_state: | |
| st.session_state.question_index = 0 | |
| if st.session_state.question_index < total_questions: | |
| question_number = st.session_state.question_index + 1 | |
| question = additional_questions[st.session_state.question_index] | |
| # Display question number and question text | |
| st.markdown(f"""### Question {question_number}: | |
| {question}""") | |
| answer = st.text_input("Your Answer", key=f"q{st.session_state.question_index}") | |
| # Display progress bar with formatted text showing "current/total" | |
| progress = (st.session_state.question_index + 1) / total_questions | |
| st.progress(progress) | |
| st.write(f"Progress: {question_number}/{total_questions}") | |
| if st.button("Submit Answer", key=f"submit{st.session_state.question_index}"): | |
| if answer: | |
| st.session_state.answers[question] = answer | |
| st.session_state.question_index += 1 | |
| st.rerun() # Trigger app rerun to show the next question | |
| else: | |
| st.warning("Please enter an answer before submitting.") | |
| else: | |
| st.success("All questions have been answered. Click below to generate your recommendations.") | |
| if st.button("Generate Response"): | |
| st.session_state.profile_data.update(st.session_state.answers) | |
| st.session_state.ask_additional_questions = False | |
| st.rerun() # Trigger app rerun after completing all questions | |
| elif st.session_state.get("ask_additional_questions") is False: | |
| # Directly generate recommendations | |
| st.header("Generating Recommendations") | |
| with st.spinner('Generating recommendations...'): | |
| time.sleep(2) # Simulate processing time | |
| # Extracting user profile data | |
| profile = st.session_state.profile_data | |
| user_tech_skills = set(skill.strip().lower() for skill in profile["tech_skills"].split(",")) | |
| user_soft_skills = set(skill.strip().lower() for skill in profile["soft_skills"].split(",")) | |
| user_interests = set(interest.strip().lower() for interest in profile["interests"].split(",")) | |
| user_answers = st.session_state.get('answers', {}) | |
| # Job Recommendations using refined scoring logic | |
| def match_job_criteria(row, profile, user_answers): | |
| job_title = row['Job Title'].lower() | |
| job_description = row['Job Description'].lower() | |
| qualifications = row['Qualifications'].lower() | |
| skills = row['skills'].lower() | |
| role = row['Role'].lower() | |
| educational_background = profile['educational_background'].lower() | |
| tech_skills = set(skill.strip().lower() for skill in profile["tech_skills"].split(",")) | |
| soft_skills = set(skill.strip().lower() for skill in profile["soft_skills"].split(",")) | |
| interests = set(interest.strip().lower() for interest in profile["interests"].split(",")) | |
| user_answers_text = ' '.join(user_answers.values()).lower() | |
| score = 0 | |
| if educational_background in qualifications or educational_background in job_description: | |
| score += 2 | |
| if any(skill in skills for skill in tech_skills): | |
| score += 3 | |
| if any(skill in job_description or role for skill in soft_skills): | |
| score += 1 | |
| if any(interest in job_title or job_description for interest in interests): | |
| score += 2 | |
| if any(answer in job_description or qualifications for answer in user_answers_text.split()): | |
| score += 2 | |
| return score >= 5 | |
| # Get unique job recommendations | |
| job_recommendations = jobs_data[jobs_data.apply(lambda row: match_job_criteria(row, profile, user_answers), axis=1)] | |
| unique_jobs = job_recommendations.drop_duplicates(subset=['Job Title']) | |
| # Display Job Recommendations in a table with bold job titles | |
| st.subheader("Job Recommendations") | |
| if not unique_jobs.empty: | |
| job_list = unique_jobs.head(5)[['Job Title', 'Job Description']].reset_index(drop=True) | |
| job_list['Job Title'] = job_list['Job Title'].apply(lambda x: f"<b>{x}</b>") | |
| job_list_html = job_list.to_html(index=False, escape=False, justify='left').replace( | |
| '<th>', '<th style="text-align: left; font-weight: bold;">') | |
| st.markdown(job_list_html, unsafe_allow_html=True) | |
| else: | |
| st.write("No specific job recommendations found matching your profile.") | |
| st.write("Here are some general job recommendations:") | |
| fallback_jobs = jobs_data.drop_duplicates(subset=['Job Title']).head(3) | |
| fallback_jobs['Job Title'] = fallback_jobs['Job Title'].apply(lambda x: f"<b>{x}</b>") | |
| fallback_list_html = fallback_jobs[['Job Title', 'Job Description']].to_html( | |
| index=False, escape=False, justify='left').replace( | |
| '<th>', '<th style="text-align: left; font-weight: bold;">') | |
| st.markdown(fallback_list_html, unsafe_allow_html=True) | |
| # Course Recommendations using RAG technique | |
| course_recommendations = courses_data[courses_data['Course Name'].apply( | |
| lambda name: any(interest in name.lower() for interest in user_interests) | |
| )] | |
| # Display Course Recommendations | |
| st.subheader("Recommended Courses") | |
| if not course_recommendations.empty: | |
| for _, row in course_recommendations.head(5).iterrows(): | |
| st.write(f"- [{row['Course Name']}]({row['Links']})") | |
| else: | |
| st.write("No specific course recommendations found matching your interests.") | |
| st.write("Here are some general course recommendations aligned with your profile:") | |
| fallback_courses = courses_data[ | |
| courses_data['Course Name'].apply( | |
| lambda name: any( | |
| word in name.lower() for word in profile["educational_background"].lower().split() + | |
| [skill.lower() for skill in profile["tech_skills"].split(",")] | |
| ) | |
| ) | |
| ] | |
| if not fallback_courses.empty: | |
| for _, row in fallback_courses.head(3).iterrows(): | |
| st.write(f"- [{row['Course Name']}]({row['Links']})") | |
| else: | |
| st.write("Consider exploring courses in fields related to your educational background or technical skills.") | |
| # 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})") | |
| st.write("Thank you for using the Career Counseling Application with RAG!") |