Update app.py

app.py

@@ -2,7 +2,6 @@ import streamlit as st
 from datasets import load_dataset
 import pandas as pd
 from transformers import pipeline
-import time
 
 # Constants
 universities_url = "https://www.4icu.org/top-universities-world/"
@@ -39,110 +38,103 @@ 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"):
-    with st.spinner('Saving your profile...'):
-        time.sleep(2)  # Simulate processing time
-        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!")
+    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!")
+
+# Questions Section (Appears after profile submission)
+if "profile_data" in st.session_state:
+    st.header("Answer the Following Questions:")
+    questions = [
+        "What do you see yourself achieving in the next five years?",
+        "Which skills would you like to develop further? (Examples: leadership, technical expertise, communication, etc.)",
+        "Do you prefer a structured routine or a more flexible, varied work environment?",
+        "What’s most important to you in a job? (e.g., work-life balance, job stability, opportunities for growth, impact on society)",
+        "What types of projects or tasks energize you? (e.g., solving complex problems, helping others, creating something new)",
+        "Are you comfortable with roles that may involve public speaking or presenting ideas?",
+        "How do you handle stress or pressure in a work setting? (Select options: I thrive under pressure, I manage well, I prefer lower-stress environments)",
+        "Would you be open to relocation or travel for your job?",
+        "Do you prioritize high salary potential or job satisfaction when considering a career?",
+        "What kind of work culture are you drawn to? (e.g., collaborative, competitive, mission-driven, innovative)"
+    ]
+    
+    answers = {}
+    for question in questions:
+        answers[question] = st.text_input(question)
+
+    if st.button("Submit Answers"):
+        st.session_state.answers = answers
+        st.success("Your answers have been saved!")
 
 # Intelligent Q&A Section
 st.header("Intelligent Q&A")
 question = st.text_input("Ask a career-related question:")
 if question:
-    with st.spinner('Processing your question...'):
-        answer = qa_pipeline(question)[0]["generated_text"]
-        time.sleep(2)  # Simulate processing time
-        st.write("Answer:", answer)
+    answer = qa_pipeline(question)[0]["generated_text"]
+    st.write("Answer:", answer)
 
 # Career and Job Recommendations Section
-st.header("Job Recommendations")
+st.header("Career and Job Recommendations")
 if "profile_data" in st.session_state:
-    with st.spinner('Generating job recommendations...'):
-        time.sleep(2)  # Simulate processing time
-        job_recommendations = []
-
-        # Find jobs from ds_jobs
-        for job in ds_jobs["train"]:
-            job_title = job.get("job_title_short", "Unknown Job Title")
-            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_title)
-
-        # Find jobs from ds_custom_jobs
-        for _, job in ds_custom_jobs.iterrows():
-            job_title = job.get("job_title", "Unknown Job Title")
-            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_title)
-
-        # Remove duplicates and keep the unique job titles
-        job_recommendations = list(set(job_recommendations))
-
-        if job_recommendations:
-            st.subheader("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. Here are some general recommendations:")
-            for job in ["Data Analyst", "Software Engineer", "Project Manager", "Research Scientist", "Business Analyst"][:5]:
-                st.write("- ", job)
+    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"))
+
+    # Remove duplicates by converting the list to a set and back to a list
+    job_recommendations = list(set(job_recommendations))
+
+    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("Recommended Courses")
+st.header("Course Suggestions")
 if "profile_data" in st.session_state:
-    with st.spinner('Finding courses related to your profile...'):
-        time.sleep(2)  # Simulate processing time
-        course_recommendations = []
-
-        # Find relevant courses in ds_courses
-        for course in ds_courses["train"]:
-            if any(interest.lower() in course.get("Course Name", "").lower() for interest in st.session_state.profile_data["interests"].split(",")):
-                course_recommendations.append({
-                    "name": course.get("Course Name", "Unknown Course Title"),
-                    "url": course.get("Links", "#")
-                })
-
-        # Find relevant courses in ds_custom_courses
-        for _, row in ds_custom_courses.iterrows():
-            if any(interest.lower() in row["Course Name"].lower() for interest in st.session_state.profile_data["interests"].split(",")):
-                course_recommendations.append({
-                    "name": row["Course Name"],
-                    "url": row.get("Links", "#")
-                })
-
-        # Remove duplicates from course recommendations by converting to a set of tuples and back to a list
-        course_recommendations = list({(course["name"], course["url"]) for course in course_recommendations})
-
-        # If there are fewer than 5 exact matches, add nearly related courses
-        if len(course_recommendations) < 5:
-            for course in ds_courses["train"]:
-                if len(course_recommendations) >= 5:
-                    break
-                if any(skill.lower() in course.get("Course Name", "").lower() for skill in st.session_state.profile_data["tech_skills"].split(",")):
-                    course_recommendations.append((course.get("Course Name", "Unknown Course Title"), course.get("Links", "#")))
-
-            for _, row in ds_custom_courses.iterrows():
-                if len(course_recommendations) >= 5:
-                    break
-                if any(skill.lower() in row["Course Name"].lower() for skill in st.session_state.profile_data["tech_skills"].split(",")):
-                    course_recommendations.append((row["Course Name"], row.get("Links", "#")))
-
-            # Remove duplicates again after adding nearly related courses
-            course_recommendations = list({(name, url) for name, url in course_recommendations})
-
-        if course_recommendations:
-            st.write("Here are the top 5 courses related to your interests:")
-            for course in course_recommendations[:5]:  # Limit to top 5 course recommendations
-                st.write(f"- [{course[0]}]({course[1]})")
+    course_recommendations = [
+        course.get("Course Name", "Unknown Course Title") for course in ds_courses["train"]
+        if any(interest.lower() in course.get("Course Name", "").lower() for interest in st.session_state.profile_data["interests"].split(","))
+    ]
+
+    course_recommendations.extend([ 
+        row["Course Name"] for _, row in ds_custom_courses.iterrows()
+        if any(interest.lower() in row["Course Name"].lower() for interest in st.session_state.profile_data["interests"].split(","))
+    ])
+
+    # Remove duplicates from course recommendations
+    course_recommendations = list(set(course_recommendations))
+
+    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!")