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README.md

@@ -1,8 +1,8 @@
 ---
-title: AI Career Counselling Application
-emoji: 📊
-colorFrom: yellow
-colorTo: blue
+title: CareerCounsellingApp
+emoji: 🚀
+colorFrom: blue
+colorTo: indigo
 sdk: streamlit
 sdk_version: 1.39.0
 app_file: app.py

app (2).py

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+import streamlit as st
+import pandas as pd
+from transformers import pipeline
+import time
+
+# Load datasets from CSV files
+@st.cache_resource
+def load_csv_datasets():
+    jobs_data = pd.read_csv("job_descriptions.csv")
+    courses_data = pd.read_csv("courses_data.csv")
+    return jobs_data, courses_data
+
+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")
+
+qa_pipeline = load_pipeline()
+
+# Streamlit App Interface
+st.markdown(
+    """
+    <div style="display: flex; align-items: center; gap: 10px; flex-wrap: wrap;">
+        <h1 style="font-size: 29px; display: inline-block; margin-right: 10px;">
+            <img src="https://img.icons8.com/ios-filled/50/000000/graduation-cap.png" width="40" alt="Degree icon"/>
+            Confused about which career to pursue?
+        </h1>
+        <h2 style="font-size: 25px; display: inline-block; margin: 0;">Let CareerCompass help you decide in two simple steps</h2>
+    </div>
+    """,
+    unsafe_allow_html=True,
+)
+
+# Display the appropriate subheader based on profile data status
+if "profile_data" not in st.session_state or not st.session_state.get("profile_data_saved", False):
+    st.markdown("<h3 style='font-size: 20px;'>Step 1: Find out profile questions on the left sidebar and follow the instructions.</h3>", unsafe_allow_html=True)
+
+# Sidebar for Profile Setup
+st.sidebar.header("Profile Setup")
+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"
+])
+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)")
+
+# Profile validation and saving
+def are_profile_fields_filled():
+    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)
+            st.session_state.profile_data = {
+                "educational_background": educational_background,
+                "interests": interests,
+                "tech_skills": tech_skills,
+                "soft_skills": soft_skills
+            }
+            st.session_state.profile_data_saved = True  # Set the profile data saved flag
+            st.session_state.question_index = 0  # Initialize question index
+            st.session_state.answers = {}  # Initialize dictionary for answers
+            st.session_state.ask_additional_questions = None  # Reset question flag
+            st.session_state.show_additional_question_buttons = True  # Show buttons after profile save
+            st.sidebar.success("Profile saved successfully!")
+            st.markdown("<h2 style='font-size: 25px;'>Step 2: For more Accurate Analysis, Do you wish to provide more information?</h2>", unsafe_allow_html=True)
+    else:
+        st.sidebar.error("Please fill in all the fields before saving your profile.")
+
+# Button actions
+if "show_additional_question_buttons" in st.session_state:
+    if st.session_state.show_additional_question_buttons:
+        col1, col2 = st.columns(2)
+        with col1:
+            if st.button("Yes, ask me more questions"):
+                st.session_state.ask_additional_questions = True
+                st.session_state.show_additional_question_buttons = False  # Hide buttons after click
+        with col2:
+            if st.button("Skip and generate recommendations"):
+                st.session_state.ask_additional_questions = False
+                st.session_state.show_additional_question_buttons = False  # Hide buttons after click
+
+# 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?"
+]
+
+# 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_number = st.session_state.question_index + 1
+            question = additional_questions[st.session_state.question_index]
+
+            # Display question number and question text
+            st.markdown(f"""### Question {question_number}:
+             {question}""")
+
+            answer = st.text_input("Your Answer", key=f"q{st.session_state.question_index}")
+
+            # Display progress bar with formatted text showing "current/total"
+            progress = (st.session_state.question_index + 1) / total_questions
+            st.progress(progress)
+            st.write(f"Progress: {question_number}/{total_questions}")
+
+            if st.button("Submit Answer", key=f"submit{st.session_state.question_index}"):
+                if answer:
+                    st.warning("Data saved successfully. click again to proceed")
+                    # Save the answer and increment the question index
+                    st.session_state.question_index += 1
+                    st.session_state.answers[question] = answer
+
+                    # No need to call a special function; the app will rerun automatically
+                else:
+                    st.warning("Please enter an answer before submitting.")
+        else:
+            st.success("All questions have been answered. Click below to generate your recommendations.")
+            if st.button("Generate Response"):
+                st.warning("Data saved successfully. click again to proceed")
+                st.session_state.profile_data.update(st.session_state.answers)
+                st.session_state.ask_additional_questions = False
+
+    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
+
+            # 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_soft_skills = set(skill.strip().lower() for skill in profile["soft_skills"].split(","))
+            user_interests = set(interest.strip().lower() for interest in profile["interests"].split(","))
+            user_answers = st.session_state.get('answers', {})
+
+            # Job Recommendations using refined scoring logic
+            def match_job_criteria(row, profile, user_answers):
+                job_title = row['Job Title'].lower()
+                job_description = row['Job Description'].lower()
+                qualifications = row['Qualifications'].lower()
+                skills = row['skills'].lower()
+                role = row['Role'].lower()
+
+                educational_background = profile['educational_background'].lower()
+                tech_skills = set(skill.strip().lower() for skill in profile["tech_skills"].split(","))
+                soft_skills = set(skill.strip().lower() for skill in profile["soft_skills"].split(","))
+                interests = set(interest.strip().lower() for interest in profile["interests"].split(","))
+                user_answers_text = ' '.join(user_answers.values()).lower()
+
+                score = 0
+
+                if educational_background in qualifications or educational_background in job_description:
+                    score += 2
+                if any(skill in skills for skill in tech_skills):
+                    score += 3
+                if any(skill in job_description or role for skill in soft_skills):
+                    score += 1
+                if any(interest in job_title or job_description for interest in interests):
+                    score += 2
+                if any(answer in job_description or qualifications for answer in user_answers_text.split()):
+                    score += 2
+
+                return score >= 5
+
+            # Get unique job recommendations
+            job_recommendations = jobs_data[jobs_data.apply(lambda row: match_job_criteria(row, profile, user_answers), axis=1)]
+            unique_jobs = job_recommendations.drop_duplicates(subset=['Job Title'])
+
+            # Display Job Recommendations in a table with bold job titles
+            st.subheader("Job Recommendations")
+            if not unique_jobs.empty:
+                job_list = unique_jobs.head(5)[['Job Title', 'Job Description']].reset_index(drop=True)
+                job_list['Job Title'] = job_list['Job Title'].apply(lambda x: f"<b>{x}</b>")
+                job_list_html = job_list.to_html(index=False, escape=False, justify='left').replace(
+                    '<th>', '<th style="text-align: left; font-weight: bold;">')
+                st.markdown(job_list_html, unsafe_allow_html=True)
+            else:
+                st.write("No specific job recommendations found matching your profile.")
+                st.write("Here are some general job recommendations:")
+                fallback_jobs = jobs_data.drop_duplicates(subset=['Job Title']).head(3)
+                fallback_jobs['Job Title'] = fallback_jobs['Job Title'].apply(lambda x: f"<b>{x}</b>")
+                fallback_list_html = fallback_jobs[['Job Title', 'Job Description']].to_html(
+                    index=False, escape=False, justify='left').replace(
+                    '<th>', '<th style="text-align: left; font-weight: bold;">')
+                st.markdown(fallback_list_html, unsafe_allow_html=True)
+
+            # 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 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:")
+
+                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("Thank you for using the Career Counseling Application with RAG!")

requirements (1).txt

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+streamlit
+transformers
+datasets
+peft
+pandas
+numpy
+huggingface-hub
+torch
+kaggle
+requests