Okay, let's adjust the application to be more student-friendly for those choosing subjects after 10th or 12th, and structure the output to provide a roadmap. from flask import Flask, render_template, request, redirect, url_for app = Flask(__name__) # Placeholder for the ML model and NLP processing # In a real application, you would load your trained model here # and potentially initialize NLP tools like NLTK or spaCy. def preprocess_user_data(form_data): """ Preprocesses user input data for the ML model. This is a placeholder function. """ print("Preprocessing data:", form_data) # In a real application, you would process the text inputs # (interests, skills, subjects, etc.) using NLP techniques # and convert them into a format suitable for your ML model. # You might also encode the academic stage. processed_data = form_data # Replace with actual processing return processed_data def recommend_careers(processed_data): """ Uses the ML model to recommend career paths and generate roadmaps. This is a placeholder function. """ print("Recommending careers based on:", processed_data) # In a real application, your trained ML model would take # the processed_data and predict suitable career paths/fields. # Based on the predicted path, you would then generate a # detailed roadmap. # Dummy recommendations and roadmaps for demonstration # These are simplified examples; a real system would be more nuanced. dummy_recommendations = [ { "path_title": "Science Stream -> Engineering/Technology", "match_score": 0.92, "roadmap": [ {"step": "Choose Science Stream (Physics, Chemistry, Maths) in 11th/12th.", "details": "Focus on building strong fundamentals in PCM."}, {"step": "Prepare for Engineering Entrance Exams (e.g., JEE, State CETs).", "details": "Practice problem-solving, time management."}, {"step": "Pursue a Bachelor's Degree in Engineering (e.g., Computer Science, IT, Electronics, Mechanical).", "details": "Select a branch based on interest and aptitude."}, {"step": "Develop technical skills (programming, data structures, algorithms, specific domain skills).", "details": "Online courses, projects, internships are key."}, {"step": "Seek Entry-Level Engineering Role.", "details": "Apply for jobs, prepare for technical and behavioral interviews."}, {"step": "Continuous Learning and Specialization.", "details": "Stay updated with technology, consider Master's or specialized certifications."} ], "potential_careers": ["Software Engineer", "Data Scientist", "Web Developer", "Mechanical Engineer", "Electronics Engineer"], "learning_resources": ["Khan Academy (PCM)", "NCERT Textbooks", "Online coding platforms (Codecademy, LeetCode)", "NPTEL courses"] }, { "path_title": "Commerce Stream -> Business/Finance", "match_score": 0.88, "roadmap": [ {"step": "Choose Commerce Stream (Accountancy, Business Studies, Economics) in 11th/12th.", "details": "Understand core business and economic principles."}, {"step": "Pursue a Bachelor's Degree (e.g., B.Com, BBA, B.Ec).", "details": "Build a foundation in accounting, finance, management."}, {"step": "Consider Professional Courses (e.g., CA, CS, CMA, CFA) or MBA.", "details": "These can provide specialized expertise and career acceleration."}, {"step": "Develop analytical, communication, and financial skills.", "details": "Internships in relevant fields are highly beneficial."}, {"step": "Seek Entry-Level Role in Finance, Accounting, or Management.", "details": "Look for roles like Accountant, Financial Analyst, Business Analyst."}, {"step": "Gain Experience and Specialize.", "details": "Focus on a niche area like taxation, auditing, investment banking, or marketing."} ], "potential_careers": ["Accountant", "Financial Analyst", "Business Analyst", "Marketing Manager", "Human Resources Manager"], "learning_resources": ["NCERT Textbooks", "ICAI/ICSI/ICMAI study materials", "Online finance courses (Coursera, Udemy)", "Business news publications"] }, { "path_title": "Humanities Stream -> Civil Services/Social Sciences/Arts", "match_score": 0.85, "roadmap": [ {"step": "Choose Humanities Stream (History, Political Science, Sociology, Psychology, Literature, etc.) in 11th/12th.", "details": "Explore subjects related to human society, culture, and behavior."}, {"step": "Pursue a Bachelor's Degree (e.g., B.A. in relevant subject, B.A. LLB, B.J.M.C.).", "details": "Deepen understanding in chosen area, develop critical thinking and writing skills."}, {"step": "Prepare for Competitive Exams (e.g., Civil Services, State PSCs) or pursue Master's/PhD.", "details": "Focus on general knowledge, current affairs, and analytical writing."}, {"step": "Develop strong research, communication, and analytical skills.", "details": "Participate in debates, writing clubs, research projects."}, {"step": "Seek Entry-Level Role in Government, NGOs, Media, Research, or relevant industry.", "details": "Look for roles like Administrator, Social Worker, Journalist, Researcher, Content Writer."}, {"step": "Gain Experience and Specialize.", "details": "Focus on a specific area like public policy, development studies, journalism, or academic research."} ], "potential_careers": ["IAS/IPS Officer", "Journalist", "Lawyer", "Psychologist", "Sociologist", "Content Writer", "Teacher/Professor"], "learning_resources": ["NCERT Textbooks", "Newspapers and Current Affairs magazines", "Online courses on specific subjects (e.g., History, Psychology)", "Books on Indian Polity, Economy, History"] } # Add more paths covering different combinations and interests ] # In a real system, you would filter and sort these based on processed_data # For this demo, we'll just return the list. # You might sort by match_score if your model provides one. # sorted_recommendations = sorted(dummy_recommendations, key=lambda x: x['match_score'], reverse=True) return dummy_recommendations # Return all dummy paths for demo @app.route('/') def index(): """Renders the main form page.""" return render_template('index.html') @app.route('/recommend', methods=['POST']) def recommend(): """Handles form submission, processes data, and shows recommendations.""" if request.method == 'POST': user_data = { 'academic_stage': request.form.get('academic_stage'), 'favorite_subjects': request.form.get('favorite_subjects'), 'struggled_subjects': request.form.get('struggled_subjects'), 'interests': request.form.get('interests'), 'skills': request.form.get('skills'), 'personality_traits': request.form.get('personality_traits'), 'goals': request.form.get('goals') # Add more fields as needed } # Preprocess data (placeholder) processed_data = preprocess_user_data(user_data) # Get career recommendations and roadmaps (placeholder) recommendations = recommend_careers(processed_data) # Render results page return render_template('results.html', recommendations=recommendations, user_data=user_data) # Redirect to index if not a POST request return redirect(url_for('index')) if __name__ == '__main__': # In a production environment, use a production-ready WSGI server # For development in WebContainer, debug=True is acceptable app.run(debug=True, port=3000) # Use port 3000 for WebContainer preview Student Career Guide

Find Your Path: Student Career Guide

Your Current Academic Stage:

Your Favorite Subjects (e.g., Maths, Physics, History, Economics, English):

Subjects You Struggled With or Disliked:

Your Interests & Hobbies (e.g., Reading, Sports, Coding, Art, Music, Helping People):

Skills You Think You Have (e.g., Good Communication, Problem Solving, Creativity, Leadership, Good with Numbers):

Describe Your Personality (e.g., Introverted, Extroverted, Analytical, Creative, Organized, Adventurous):

Your Future Goals or Aspirations (e.g., Want a stable job, Want to travel, Want to help society, Want to start a business):

Your Career Roadmap

Based on your input:

Academic Stage: {{ user_data.academic_stage }}

Favorite Subjects: {{ user_data.favorite_subjects }}

Subjects Struggled With: {{ user_data.struggled_subjects if user_data.struggled_subjects else 'N/A' }}

Interests: {{ user_data.interests }}

Skills: {{ user_data.skills }}

Personality Traits: {{ user_data.personality_traits if user_data.personality_traits else 'N/A' }}

Goals: {{ user_data.goals if user_data.goals else 'N/A' }}

Recommended Paths & Roadmaps:

{% if recommendations %} {% for rec in recommendations %}

{{ rec.path_title }} (Match: {{ "%.0f" | format(rec.match_score * 100) }}%)

Roadmap:

{% if rec.roadmap %} {% for step in rec.roadmap %}

{{ step.step }}

{% endfor %} {% else %}

No detailed roadmap available for this path.

{% endif %}

Potential Careers:

{% if rec.potential_careers %} {% for career in rec.potential_careers %} {{ career }} {% endfor %} {% else %}

No specific careers listed for this path.

{% endif %}

Learning Resources:

{% if rec.learning_resources %}

{{ resource }}

{% else %}

No specific learning resources listed for this path.

{% endif %}

{% endfor %} {% else %}

No recommendations found based on your input.

{% endif %} Go Back

I have modified the `index.html` form to ask questions more relevant to students after 10th or 12th grade. The `app.py` now includes placeholder logic to return dummy roadmap data for a few common streams/paths. The `results.html` template is updated to display this roadmap structure, including steps, potential careers, and learning resources, while maintaining the inverted brown/beige/white theme.