Update app.py

app.py

@@ -19,7 +19,7 @@ def load_datasets():
 
 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'
+# Initialize the pipeline with caching
 @st.cache_resource
 def load_pipeline():
     return pipeline("text2text-generation", model="google/flan-t5-large")
@@ -48,101 +48,115 @@ if st.sidebar.button("Save Profile"):
             "soft_skills": soft_skills
         }
         st.sidebar.success("Profile saved successfully!")
+        st.session_state.show_questions = True  # Flag to show the question section after profile is 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)
-
-# Career and Job Recommendations Section
-st.header("Job Recommendations")
+# Check if the profile has been saved
 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)
-
-# Course Suggestions Section
-st.header("Recommended Courses")
-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:
+    # Show question section if profile is saved
+    if "show_questions" in st.session_state and st.session_state.show_questions:
+        st.header("Questionnaire")
+        st.write("Please answer these questions to help us make more accurate recommendations.")
+
+        # List of 10 questions
+        questions = [
+            "What do you see yourself achieving in the next five years?",
+            "Which skills would you like to develop further? (e.g., leadership, technical expertise, communication)",
+            "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)"
+        ]
+        
+        # Collect responses
+        answers = []
+        for i, question in enumerate(questions):
+            answers.append(st.text_input(f"Q{i+1}: {question}", key=f"question_{i}"))
+
+        # Submit questions
+        if st.button("Submit Questionnaire"):
+            st.session_state.answers = answers
+            st.session_state.show_questions = False  # Hide questions after submission
+            st.success("Thank you for submitting your answers!")
+    
+    # Proceed to recommendation sections if questions are answered
+    if "answers" in st.session_state:
+        # 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)
+
+        # Career and Job Recommendations Section
+        st.header("Job Recommendations")
+        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
+            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.")
+
+        # Course Suggestions Section
+        st.header("Recommended Courses")
+        with st.spinner('Finding courses related to your profile...'):
+            time.sleep(2)
+            course_recommendations = []
+
+            # Find relevant courses in ds_courses
             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", "#")))
+                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 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]})")
-
-# 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!")
+                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
+            course_recommendations = list({(course["name"], course["url"]) for course in course_recommendations})
+
+            if course_recommendations:
+                st.write("Here are the top 5 courses related to your interests:")
+                for course in course_recommendations[:5]:
+                    st.write(f"- [{course[0]}]({course[1]})")
+
+        # 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!")