Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+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")
+    ds_custom_courses = pd.read_csv("final_cleaned_merged_coursera_courses.csv")
+    ds_custom_jobs = pd.read_csv("merged_data_science_jobs.csv")
+    ds_custom_universities = pd.read_csv("merged_university_data_cleaned (1).csv")
+    return ds_jobs, ds_courses, ds_custom_courses, ds_custom_jobs, ds_custom_universities
+
+ds_jobs, ds_courses, ds_custom_courses, ds_custom_jobs, ds_custom_universities = 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"]:
+        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"))
+
+    for _, job in ds_custom_jobs.iterrows():
+        job_skills = job.get("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", "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(","))
+    ]
+
+    course_recommendations.extend([
+        row["course_title"] for _, row in ds_custom_courses.iterrows()
+        if any(interest.lower() in row["course_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})")
+
+st.subheader("Custom University Data")
+if not ds_custom_universities.empty:
+    st.write("Here are some recommended universities based on custom data:")
+    st.dataframe(ds_custom_universities.head())
+
+# Conclusion
+st.write("Thank you for using the Career Counseling Application!")
+