Create app.py

app.py

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+import streamlit as st
+from datasets import load_dataset
+from transformers import pipeline
+
+# 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="allenai/t5-large-squad2")
+
+# 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 Session
+st.header("Intelligent Q&A Session")
+if "profile_data" in st.session_state:
+    st.write("Please answer the following questions based on your profile to help us suggest suitable careers for you.")
+    
+    questions = []
+    # Generate or select 10 questions based on profile interests, skills, etc.
+    for i in range(10):
+        question_text = ds_natural_questions["train"][i]["question"] if i < len(ds_natural_questions["train"]) else None
+        if question_text:
+            question_text = question_text.replace("interests", interests).replace("skills", tech_skills)
+            questions.append(question_text)
+    
+    # Display questions and collect answers
+    answers = []
+    for i, question in enumerate(questions):
+        answer = st.text_input(f"Q{i+1}: {question}")
+        answers.append(answer)
+
+    # Store answers in session state for further analysis
+    st.session_state.answers = answers
+
+# 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"), job.get("job_url", "#")))
+
+    if job_recommendations:
+        st.subheader("Job Recommendations")
+        st.write("Based on your profile and responses, here are some potential job roles and links to explore:")
+        for job_title, job_url in job_recommendations[:5]:  # Limit to top 5 job recommendations
+            st.write(f"- [{job_title}]({job_url})")
+    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 = []
+    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.append((course.get("Title", "Unknown Course"), course.get("course_url", "#")))
+
+    if course_recommendations:
+        st.subheader("Recommended Courses")
+        st.write("Here are some courses related to your interests:")
+        for course_title, course_url in course_recommendations[:5]:  # Limit to top 5 course recommendations
+            st.write(f"- [{course_title}]({course_url})")
+    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!")