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| import streamlit as st | |
| from transformers import AutoTokenizer, pipeline | |
| from peft import PeftModel, PeftConfig | |
| from transformers import AutoModelForSeq2SeqLM | |
| from datasets import load_dataset | |
| import torch | |
| st.write("Initializing...") # Debugging message | |
| # Load the LoRA configuration and model | |
| config = PeftConfig.from_pretrained("lorahub/flan_t5_large-web_questions_potential_correct_answer") | |
| base_model = AutoModelForSeq2SeqLM.from_pretrained("google/flan-t5-large") | |
| model = PeftModel.from_pretrained(base_model, "lorahub/flan_t5_large-web_questions_potential_correct_answer") | |
| tokenizer = AutoTokenizer.from_pretrained("google/flan-t5-large") | |
| st.write("Model Loaded Successfully!") # Debugging message | |
| qa_pipeline = pipeline("text2text-generation", model=model, tokenizer=tokenizer) | |
| # Load relevant datasets | |
| hotpotqa_dataset = load_dataset("bdsaglam/hotpotqa-distractor") | |
| squad_v2_dataset = load_dataset("tom-010/squad_v2_with_answerable") | |
| bias_professions_dataset = load_dataset("society-ethics/stable-bias-professions") | |
| classifier_dataset = load_dataset("habanoz/classifier_1300_610_url_p") | |
| st.write("Datasets Loaded Successfully!") # Debugging message | |
| # Streamlit App Structure | |
| def main(): | |
| st.title("AI-Powered Career Counseling App with Advanced Q&A") | |
| st.sidebar.title("Navigation") | |
| option = st.sidebar.selectbox("Choose an Option", ["Profile Setup", "Career Q&A", "Career Recommendations", "Resource Library"]) | |
| if option == "Profile Setup": | |
| profile_setup() | |
| elif option == "Career Q&A": | |
| career_qa() | |
| elif option == "Career Recommendations": | |
| career_recommendations() | |
| elif option == "Resource Library": | |
| resource_library() | |
| # Profile Setup Section | |
| def profile_setup(): | |
| st.header("Profile Setup") | |
| st.write("Fill out your details to personalize your experience.") | |
| age = st.number_input("Age", min_value=10, max_value=100) | |
| education = st.selectbox("Education Level", ["High School", "Undergraduate", "Graduate", "Other"]) | |
| interests = st.text_area("Career Interests", "e.g., Data Science, Graphic Design") | |
| skills = st.text_area("Skills (comma-separated)", "e.g., Python, communication, empathy") | |
| if st.button("Save Profile"): | |
| st.session_state["profile"] = { | |
| "age": age, | |
| "education": education, | |
| "interests": interests.split(", "), | |
| "skills": skills.split(", ") | |
| } | |
| st.success("Profile saved successfully!") | |
| # Q&A Section for Career-related questions | |
| def career_qa(): | |
| st.header("Career Q&A") | |
| question = st.text_input("Ask a career-related question") | |
| if st.button("Get Answer"): | |
| if question: | |
| # Prepare question for the model | |
| response = qa_pipeline(question) | |
| st.write("Answer:", response[0]['generated_text']) | |
| else: | |
| st.warning("Please enter a question.") | |
| # Career Recommendations Section (Mockup, Extend as needed) | |
| def career_recommendations(): | |
| st.header("Career Recommendations") | |
| # Mock recommendation - In a real application, this should be based on a recommendation model | |
| st.write("Based on your interests and skills, we recommend:") | |
| st.write("1. Data Scientist") | |
| st.write("2. Software Engineer") | |
| st.write("3. Product Manager") | |
| # Resource Library Section | |
| def resource_library(): | |
| st.header("Resource Library") | |
| st.write("Browse resources related to different careers.") | |
| career_choice = st.selectbox("Choose a career", ["Data Scientist", "Graphic Designer", "Software Engineer", "Nurse"]) | |
| # Display resources for the chosen career | |
| st.write(f"### Resources for {career_choice}") | |
| st.write("1. Article: How to become a successful " + career_choice) | |
| st.write("2. Video: Day in the life of a " + career_choice) | |
| st.write("3. Guide: Top skills for " + career_choice) | |
| if __name__ == "__main__": | |
| main() | |