import streamlit as st
from datasets import load_dataset
from transformers import pipeline

# Constants
universities_url = "https://www.4icu.org/top-universities-world/"

# Load datasets with caching to optimize performance
@st.cache_resource
def load_datasets():
    ds_jobs = load_dataset("lukebarousse/data_jobs")
    ds_courses = load_dataset("azrai99/coursera-course-dataset")
    return ds_jobs, ds_courses

ds_jobs, ds_courses = load_datasets()

# Initialize the pipeline with caching, using an accessible model like 'google/flan-t5-large'
@st.cache_resource
def load_pipeline():
    return pipeline("text2text-generation", model="google/flan-t5-large")

qa_pipeline = load_pipeline()

# 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
if st.sidebar.button("Save Profile"):
    st.session_state.profile_data = {
        "educational_background": educational_background,
        "interests": interests,
        "tech_skills": tech_skills,
        "soft_skills": soft_skills
    }
    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" in st.session_state:
    job_recommendations = []
    for job in ds_jobs["train"]:
        # Use an empty string if 'job_skills' is None
        job_skills = job.get("job_skills", "") or ""
        if any(skill.lower() in job_skills.lower() for skill in st.session_state.profile_data["tech_skills"].split(",")):
            job_recommendations.append(job.get("job_title_short", "Unknown 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" in st.session_state:
    course_recommendations = [
        course.get("Title", "Unknown Course Title") for course in ds_courses["train"]
        if any(interest.lower() in course.get("Title", "").lower() for interest in st.session_state.profile_data["interests"].split(","))
    ]

    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!")