Spaces:
Running
Running
| import streamlit as st | |
| import requests | |
| import pandas as pd | |
| from sklearn.feature_extraction.text import TfidfVectorizer | |
| from sklearn.metrics.pairwise import cosine_similarity | |
| # Set up the Streamlit page | |
| st.title("AI Opportunity Finder for Youth") | |
| st.write("Find Scholarships, Internships, Online Courses, and more!") | |
| # Function to get scholarships data from a mock API | |
| def get_scholarships(location, interests): | |
| # Example: Using a mock API or replace it with a real API URL | |
| url = "https://jsonplaceholder.typicode.com/posts" # Mock API | |
| response = requests.get(url) | |
| if response.status_code == 200: | |
| # Convert the response to a list and limit to the first 5 items | |
| posts = response.json()[:5] | |
| # Construct scholarships list | |
| return [{"title": f"Scholarship {i+1}", "description": post['body'], "eligibility": "Any student from any background."} for i, post in enumerate(posts)] | |
| else: | |
| return [] | |
| # Function to get internships data from a mock API | |
| def get_internships(): | |
| # Example: Using a mock API for Internships (replace with a real API) | |
| url = "https://jsonplaceholder.typicode.com/posts" # Mock API for testing | |
| response = requests.get(url) | |
| if response.status_code == 200: | |
| # Return a list of mock internships | |
| return [{"jobtitle": f"Internship {i+1}", "company": "Sample Company", "location": "Remote", "snippet": "Description of the internship."} for i in range(5)] | |
| else: | |
| return [] | |
| # Function to recommend opportunities based on user input | |
| def recommend_opportunities(user_interests, user_skills, opportunities): | |
| # Combine user profile into a single string | |
| user_profile = [f"{user_interests} {user_skills}"] | |
| # Create text data for opportunities based on description & eligibility | |
| opportunities_text = [f"{opportunity['description']} {opportunity['eligibility']}" for opportunity in opportunities] | |
| # Vectorize the text using TF-IDF | |
| vectorizer = TfidfVectorizer(stop_words='english') | |
| tfidf_matrix = vectorizer.fit_transform(opportunities_text + user_profile) | |
| # Compute cosine similarity | |
| cosine_sim = cosine_similarity(tfidf_matrix[-1], tfidf_matrix[:-1]) | |
| # Get the indices of the top 5 recommended opportunities | |
| recommendations = cosine_sim[0].argsort()[-5:][::-1] | |
| # Return recommended opportunities | |
| return [opportunities[i] for i in recommendations] | |
| # User input for profile | |
| st.sidebar.header("User Profile") | |
| location = st.sidebar.text_input("Location", "Pakistan") # Default to 'Pakistan' | |
| skills = st.sidebar.text_input("Skills (e.g., Python, Marketing)") | |
| interests = st.sidebar.text_input("Interests (e.g., Technology, Science)") | |
| # Fetch scholarships based on user input | |
| scholarships = get_scholarships(location, interests) | |
| # Display scholarships if available | |
| if scholarships: | |
| st.write("Scholarships found:") | |
| for scholarship in scholarships: | |
| st.write(f"Title: {scholarship['title']}") | |
| st.write(f"Description: {scholarship['description']}") | |
| st.write(f"Eligibility: {scholarship['eligibility']}") | |
| st.write("---") | |
| else: | |
| st.write("No scholarships found based on your criteria.") | |
| # Fetch internships based on user input | |
| internships = get_internships() | |
| # Display internships if available | |
| if internships: | |
| st.write("Internships found:") | |
| for internship in internships: | |
| st.write(f"Title: {internship['jobtitle']}") | |
| st.write(f"Company: {internship['company']}") | |
| st.write(f"Location: {internship['location']}") | |
| st.write(f"Snippet: {internship['snippet']}") | |
| st.write("---") | |
| else: | |
| st.write("No internships found.") | |
| # AI-based recommendations for opportunities | |
| if st.sidebar.button("Get AI Recommendations"): | |
| # Combine scholarships and internships for recommendations | |
| all_opportunities = scholarships + internships | |
| # Get AI recommendations based on user input | |
| recommended_opportunities = recommend_opportunities(interests, skills, all_opportunities) | |
| # Display recommended opportunities | |
| st.write("Recommended Opportunities based on your profile:") | |
| for opportunity in recommended_opportunities: | |
| st.write(f"Title: {opportunity['title']}") | |
| st.write(f"Description: {opportunity['description']}") | |
| st.write(f"Eligibility: {opportunity.get('eligibility', 'Not available')}") | |
| st.write("---") | |