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| import streamlit as st | |
| import requests | |
| from transformers import pipeline | |
| # Initialize the language model (Hugging Face's pre-trained model for text generation) | |
| generator = pipeline('text-generation', model='gpt-2') # You can choose any suitable language model | |
| # Title and Description of the App | |
| st.title("AI-powered Opportunity Finder for Youth") | |
| st.write("Find scholarships, internships, competitions, and more based on your skills and interests using AI!") | |
| # Collect user input | |
| interests = st.text_input("Enter your interests (e.g., AI, web development, etc.)") | |
| skills = st.text_input("Enter your skills (e.g., Python, Data Analysis, etc.)") | |
| location = st.text_input("Enter your location") | |
| # Button to trigger the AI recommendations | |
| if st.button("Find Opportunities"): | |
| # Combine the input into a prompt for the language model | |
| prompt = f"Find scholarships, internships, competitions, and online courses for a person interested in {interests}, skilled in {skills}, and located in {location}. Provide recommendations with details like title, link, and description." | |
| # Use the language model to generate recommendations | |
| response = generator(prompt, max_length=150, num_return_sequences=1) | |
| # Parse and display the generated text | |
| recommendations = response[0]['generated_text'] | |
| # Display the generated recommendations | |
| st.write("Recommended Opportunities based on your input:") | |
| st.write(recommendations) | |
| # Example to further integrate Groq API (if you need more personalized results) | |
| # You can replace the following code with an actual API request to Groq for more detailed results | |
| # Make sure to replace the placeholder with your Groq API endpoint and parameters | |
| try: | |
| api_response = requests.post("https://api.groq.com/recommendations", json={ | |
| "interests": interests, | |
| "skills": skills, | |
| "location": location | |
| }) | |
| if api_response.status_code == 200: | |
| api_recommendations = api_response.json() | |
| st.write("Groq API Recommendations:") | |
| for rec in api_recommendations: | |
| st.write(f"- {rec['title']}: {rec['link']}") | |
| else: | |
| st.write("Unable to fetch recommendations from Groq API.") | |
| except Exception as e: | |
| st.write(f"Error while fetching recommendations from Groq API: {e}") | |