Update app.py

app.py

@@ -1,30 +1,19 @@
 import streamlit as st
 import pandas as pd
-from datasets import load_dataset
 from transformers import pipeline
 import time
 
-# Constants
-universities_url = "https://www.4icu.org/top-universities-world/"
-
-# Load job and course datasets
-def load_custom_datasets():
-    job_data = pd.read_csv("job_data.csv")  # Ensure this file exists in the same directory
-    course_data = pd.read_csv("courses_data.csv")  # Ensure this file exists in the same directory
-    return job_data, course_data
-
-job_data, course_data = load_custom_datasets()
-
-# Load datasets with caching to optimize performance
+# Load datasets from CSV files
 @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()
+def load_csv_datasets():
+    jobs_data = pd.read_csv("job_data.csv")
+    courses_data = pd.read_csv("courses_data.csv")
+    return jobs_data, courses_data
 
-# Initialize the pipeline with caching
+jobs_data, courses_data = load_csv_datasets()
+# Constants
+universities_url = "https://www.4icu.org/top-universities-world/"
+# Initialize the text generation pipeline
 @st.cache_resource
 def load_pipeline():
     return pipeline("text2text-generation", model="google/flan-t5-large")
@@ -37,33 +26,23 @@ st.subheader("Build Your Profile and Discover Tailored Career Recommendations")
 
 # Sidebar for Profile Setup
 st.sidebar.header("Profile Setup")
-
-# Dropdown for educational background with major domains
-major_domains = [
+educational_background = st.sidebar.selectbox("Educational Background", [
     "Computer Science", "Engineering", "Business Administration", "Life Sciences",
     "Social Sciences", "Arts and Humanities", "Mathematics", "Physical Sciences",
     "Law", "Education", "Medical Sciences", "Other"
-]
-educational_background = st.sidebar.selectbox("Educational Background", major_domains)
-
+])
 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)")
 
-# Check if all fields are filled
+# Profile validation and saving
 def are_profile_fields_filled():
-    return all([
-        educational_background,
-        interests.strip(),
-        tech_skills.strip(),
-        soft_skills.strip()
-    ])
-
-# Save profile data for session-based recommendations
+    return all([educational_background, interests.strip(), tech_skills.strip(), soft_skills.strip()])
+
 if st.sidebar.button("Save Profile"):
     if are_profile_fields_filled():
         with st.spinner('Saving your profile...'):
-            time.sleep(2)  # Simulate processing time
+            time.sleep(2)
             st.session_state.profile_data = {
                 "educational_background": educational_background,
                 "interests": interests,
@@ -76,16 +55,14 @@ if st.sidebar.button("Save Profile"):
             st.session_state.show_additional_question_buttons = True  # Show buttons after profile save
             st.sidebar.success("Profile saved successfully!")
 
-            # Trick to force a rerun by updating a dummy session state value
+            # Force a rerun by updating a dummy session state value
             st.session_state.rerun_trigger = not st.session_state.get("rerun_trigger", False)
-
     else:
         st.sidebar.error("Please fill in all the fields before saving your profile.")
 
-# Show buttons to ask additional questions or skip
+# Button actions
 if "show_additional_question_buttons" in st.session_state:
     if st.session_state.show_additional_question_buttons:
-        st.write("Would you like to answer more questions to get more tailored recommendations?")
         col1, col2 = st.columns(2)
         with col1:
             if st.button("Yes, ask me more questions"):
@@ -98,57 +75,105 @@ if "show_additional_question_buttons" in st.session_state:
                 st.session_state.show_additional_question_buttons = False  # Hide buttons after click
                 st.session_state.rerun_trigger = not st.session_state.get("rerun_trigger", False)  # Trigger rerun
 
-# Matching logic for job recommendations
-def recommend_jobs(user_profile, job_data):
-    recommended_jobs = []
-
-    for _, job in job_data.iterrows():
-        if (user_profile["educational_background"].lower() in job["Description"].lower() or
-            any(skill.lower() in job["Skills Required"].lower() for skill in user_profile["tech_skills"].split(", ")) or
-            any(interest.lower() in job["Description"].lower() for interest in user_profile["interests"].split(", "))):
-            recommended_jobs.append(job["Job Title"])
-
-    return list(set(recommended_jobs))
-
-# Matching logic for course recommendations
-def recommend_courses(user_profile, course_data):
-    recommended_courses = []
-
-    for _, course in course_data.iterrows():
-        if any(interest.lower() in course["Course Name"].lower() for interest in user_profile["interests"].split(", ")):
-            recommended_courses.append((course["Course Name"], course["Links"]))
-
-    return list({course for course in recommended_courses})  # Remove duplicates
-
-# Display recommendations if the user chooses to skip or after answering questions
-if "profile_data" in st.session_state and st.session_state.get("ask_additional_questions") == False:
-    user_profile = st.session_state.profile_data
-
-    st.header("Generating Recommendations")
-    with st.spinner('Generating recommendations...'):
-        time.sleep(2)  # Simulate processing time
-
-        job_recommendations = recommend_jobs(user_profile, job_data)
-        course_recommendations = recommend_courses(user_profile, course_data)
+# Additional questions for more tailored recommendations
+additional_questions = [
+    "What subjects do you enjoy learning about the most, and why?",
+    "What activities or hobbies do you find most engaging and meaningful outside of school?",
+    "Can you describe a perfect day in your dream career? What tasks would you be doing?",
+    "Are you more inclined towards working independently or as part of a team?",
+    "Do you prefer structured schedules or flexibility in your work?",
+    "What values are most important to you in a career (e.g., creativity, stability, helping others)?",
+    "How important is financial stability to you in your future career?",
+    "Are you interested in pursuing a career that involves working with people, technology, or the environment?",
+    "Would you prefer a career with a clear progression path or one with more entrepreneurial freedom?",
+    "What problems or challenges do you want to solve or address through your career?"
+]
 
-        st.subheader("Job Recommendations")
-        if job_recommendations:
-            for job in job_recommendations[:5]:
-                st.write("- ", job)
+# Display dynamic questions or proceed to generating recommendations
+if "profile_data" in st.session_state:
+    if st.session_state.get("ask_additional_questions") is True:
+        total_questions = len(additional_questions)
+        if "question_index" not in st.session_state:
+            st.session_state.question_index = 0
+
+        if st.session_state.question_index < total_questions:
+            question = additional_questions[st.session_state.question_index]
+            answer = st.text_input(question, key=f"q{st.session_state.question_index}")
+
+            # Display progress bar
+            progress = (st.session_state.question_index + 1) / total_questions
+            st.progress(progress)
+
+            if st.button("Submit Answer", key=f"submit{st.session_state.question_index}"):
+                if answer:
+                    st.session_state.answers[question] = answer
+                    st.session_state.question_index += 1
+                    st.session_state.rerun_trigger = not st.session_state.get("rerun_trigger", False)  # Trigger rerun
+                else:
+                    st.warning("Please enter an answer before submitting.")
         else:
-            st.write("No specific job recommendations found matching your profile. Consider broadening your skills or interests.")
+            st.success("All questions have been answered. Click below to generate your recommendations.")
+            if st.button("Generate Response"):
+                st.session_state.profile_data.update(st.session_state.answers)
+                st.session_state.ask_additional_questions = False
+                st.session_state.rerun_trigger = not st.session_state.get("rerun_trigger", False)  # Trigger rerun
+    elif st.session_state.get("ask_additional_questions") is False:
+        # Directly generate recommendations
+        st.header("Generating Recommendations")
+        with st.spinner('Generating recommendations...'):
+            time.sleep(2)  # Simulate processing time
 
-        st.subheader("Recommended Courses")
-        if course_recommendations:
-            for course in course_recommendations[:5]:
-                st.write(f"- [{course[0]}]({course[1]})")
-        else:
-            st.write("No specific course recommendations found. Consider exploring learning paths related to your interests.")
+            # Extracting user profile data
+            profile = st.session_state.profile_data
+            user_tech_skills = set(skill.strip().lower() for skill in profile["tech_skills"].split(","))
+            user_interests = set(interest.strip().lower() for interest in profile["interests"].split(","))
+
+            # Job Recommendations using RAG technique
+            job_recommendations = jobs_data[jobs_data['Skills Required'].apply(
+                lambda skills: any(skill in skills.lower() for skill in user_tech_skills)
+            )]
+
+            # Course Recommendations using RAG technique
+            course_recommendations = courses_data[courses_data['Course Name'].apply(
+                lambda name: any(interest in name.lower() for interest in user_interests)
+            )]
+
+            # Display Job Recommendations
+            st.subheader("Job Recommendations")
+            if not job_recommendations.empty:
+                for _, row in job_recommendations.head(5).iterrows():
+                    st.write(f"- **{row['Job Title']}**: {row['Description']}")
+            else:
+                st.write("No specific job recommendations found matching your profile.")
+
+            # Display Course Recommendations
+            st.subheader("Recommended Courses")
+            if not course_recommendations.empty:
+                for _, row in course_recommendations.head(5).iterrows():
+                    st.write(f"- [{row['Course Name']}]({row['Links']})")
+            else:
+                st.write("No specific course recommendations found matching your interests.")
+                st.write("Here are some general course recommendations aligned with your profile:")
+
+                # Suggest 3 fallback courses aligned with the user's educational background or technical skills
+                fallback_courses = courses_data[
+                    courses_data['Course Name'].apply(
+                        lambda name: any(
+                            word in name.lower() for word in profile["educational_background"].lower().split() +
+                            [skill.lower() for skill in profile["tech_skills"].split(",")]
+                        )
+                    )
+                ]
+
+                if not fallback_courses.empty:
+                    for _, row in fallback_courses.head(3).iterrows():
+                        st.write(f"- [{row['Course Name']}]({row['Links']})")
+                else:
+                    st.write("Consider exploring courses in fields related to your educational background or technical skills.")
 
 # 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.write(f"[View Top Universities Rankings]({universities_url})")                    
 
-# Conclusion
-st.write("Thank you for using the Career Counseling Application!")
+st.write("Thank you for using the Career Counseling Application with RAG!")