Update app.py

app.py

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+import streamlit as st
+from datasets import load_dataset
+from transformers import pipeline
+import requests
+
+# Load necessary datasets from Hugging Face
+ds_natural_questions = load_dataset("google-research-datasets/natural_questions", "default")
+ds_open_questions = load_dataset("launch/open_question_type")
+ds_question_generator = load_dataset("iarfmoose/question_generator")
+ds_jobs = load_dataset("lukebarousse/data_jobs")
+ds_courses = load_dataset("azrai99/coursera-course-dataset")
+universities_url = "https://www.4icu.org/top-universities-world/"
+
+# Initialize the LLaMA model pipeline for text-to-text generation
+qa_pipeline = pipeline("text2text-generation", model="llama-3.1-70b-versatile", tokenizer="llama-3.1-70b-versatile")
+
+# Streamlit App Interface
+st.title("Career Counseling Application")
+st.subheader("Build Your Profile and Discover Tailored Career Recommendations")
+
+# Sidebar for Profile Setup
+st.sidebar.header("Profile Setup")
+educational_background = st.sidebar.text_input("Educational Background (e.g., Degree, Major)")
+interests = st.sidebar.text_input("Interests (e.g., AI, Data Science, Engineering)")
+tech_skills = st.sidebar.text_area("Technical Skills (e.g., Python, SQL, Machine Learning)")
+soft_skills = st.sidebar.text_area("Soft Skills (e.g., Communication, Teamwork)")
+
+# Save profile data for session-based recommendations
+profile_data = {
+    "educational_background": educational_background,
+    "interests": interests,
+    "tech_skills": tech_skills,
+    "soft_skills": soft_skills
+}
+
+if st.sidebar.button("Save Profile"):
+    st.session_state.profile_data = profile_data
+    st.sidebar.success("Profile saved successfully!")
+
+# Intelligent Q&A Section
+st.header("Intelligent Q&A")
+question = st.text_input("Ask a career-related question:")
+if question:
+    answer = qa_pipeline(question)[0]["generated_text"]
+    st.write("Answer:", answer)
+
+# Career and Job Recommendations Section
+st.header("Career and Job Recommendations")
+if profile_data:
+    job_recommendations = []
+    for job in ds_jobs["train"]:
+        if any(skill.lower() in job["description"].lower() for skill in tech_skills.split(',')):
+            job_recommendations.append(job["title"])
+    
+    if job_recommendations:
+        st.subheader("Job Recommendations")
+        st.write("Based on your profile, here are some potential job roles:")
+        for job in job_recommendations[:5]:  # Limit to top 5 job recommendations
+            st.write("- ", job)
+    else:
+        st.write("No specific job recommendations found matching your profile.")
+
+# Course Suggestions Section
+st.header("Course Suggestions")
+if profile_data:
+    course_recommendations = []
+    for course in ds_courses["train"]:
+        if any(interest.lower() in course["title"].lower() for interest in interests.split(',')):
+            course_recommendations.append(course["title"])
+    
+    if course_recommendations:
+        st.subheader("Recommended Courses")
+        st.write("Here are some courses related to your interests:")
+        for course in course_recommendations[:5]:  # Limit to top 5 course recommendations
+            st.write("- ", course)
+    else:
+        st.write("No specific courses found matching your interests.")
+
+# University Recommendations Section
+st.header("Top Universities")
+st.write("For further education, you can explore the top universities worldwide:")
+st.write(f"[View Top Universities Rankings]({universities_url})")
+
+# Conclusion
+st.write("Thank you for using the Career Counseling Application!")