Create app.py

app.py

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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()