Update app.py

app.py

@@ -1,73 +1,3 @@
-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/"
-
-# Load datasets with caching to optimize performance
-@st.cache_resource
-def load_datasets():
-    ds_jobs = load_dataset("lukebarousse/data_jobs")
-    ds_courses = load_dataset("azrai99/coursera-course-dataset")
-    ds_custom_courses = pd.read_csv("final_cleaned_merged_coursera_courses.csv")
-    ds_custom_jobs = pd.read_csv("merged_data_science_jobs.csv")
-    ds_custom_universities = pd.read_csv("merged_university_data_cleaned (1).csv")
-    return ds_jobs, ds_courses, ds_custom_courses, ds_custom_jobs, ds_custom_universities
-
-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'
-@st.cache_resource
-def load_pipeline():
-    return pipeline("text2text-generation", model="google/flan-t5-large")
-
-qa_pipeline = load_pipeline()
-
-# 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
-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.session_state.question_index = 0  # Initialize question index
-        st.session_state.answers = {}  # Initialize dictionary for answers
-        st.session_state.submitted = False  # Track if an answer was just submitted
-        st.sidebar.success("Profile saved successfully!")
-
-        st.write("To provide more personalized job and course recommendations, please answer the following questions one by one:")
-
-# Additional questions for more tailored recommendations
-additional_questions = [
-    "What industry do you prefer working in (e.g., healthcare, finance, tech)?",
-    "What type of job role are you most interested in (e.g., research, management, development)?",
-    "Are you looking for remote, hybrid, or on-site opportunities?",
-    "Do you have any certifications or licenses related to your field?",
-    "What level of experience do you have (e.g., entry-level, mid-level, senior)?",
-    "What languages are you proficient in, apart from English (if any)?",
-    "Do you prefer working for startups, mid-sized companies, or large corporations?",
-    "What is your preferred learning style for courses (e.g., video tutorials, interactive projects, reading material)?",
-    "Are you open to relocation? If yes, to which cities or regions?",
-    "Do you have a preference for job roles in specific countries or regions?"
-]
-
 # Display questions one by one after the profile is saved
 if "profile_data" in st.session_state and "question_index" in st.session_state:
     total_questions = len(additional_questions)
@@ -81,34 +11,21 @@ if "profile_data" in st.session_state and "question_index" in st.session_state:
         question = additional_questions[st.session_state.question_index]
         answer = st.text_input(question, key=f"q{st.session_state.question_index}")
 
-        # Single-click submit button logic
-        if "submitted" not in st.session_state:
-            st.session_state.submitted = False
-        
-        if st.button("Submit Answer", key=f"submit{st.session_state.question_index}") and not st.session_state.submitted:
+        # Submit button for each question, disabled after clicking
+        if st.button("Submit Answer", key=f"submit{st.session_state.question_index}", disabled=st.session_state.get("submitted", False)):
             if answer:
                 st.session_state.answers[question] = answer
                 st.session_state.question_index += 1
-                st.session_state.submitted = True
-                st.session_state.updated = True
-                st.query_params = {"updated": "true"}
+                st.session_state.submitted = True  # Lock the submit button after clicking
+                st.experimental_rerun()  # Refresh the page to unlock the button for the next question
             else:
                 st.warning("Please enter an answer before submitting.")
-        else:
-            st.session_state.submitted = False
 
     # Check if all questions are answered and show the "Generate Response" button
     if st.session_state.question_index == total_questions:
         st.success("All questions have been answered. Click below to generate your recommendations.")
-        
-        # Single-click generate response button logic
-        if "generate_response_clicked" not in st.session_state:
-            st.session_state.generate_response_clicked = False
-        
-        if st.button("Generate Response") and not st.session_state.generate_response_clicked:
-            st.session_state.generate_response_clicked = True
-            
-            # Save all answers in the profile data
+        if st.button("Generate Response", disabled=st.session_state.get("generated_response", False)):
+            st.session_state.generated_response = True  # Lock the generate response button
             st.session_state.profile_data.update(st.session_state.answers)
 
             # Career and Job Recommendations Section
@@ -187,35 +104,10 @@ if "profile_data" in st.session_state and "question_index" in st.session_state:
 
                 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['name']}]({course['url']})")
-
-                else:
-                    st.write("No specific course recommendations found matching your profile. Here are some general recommendations:")
-                    general_courses = [
-                        {"name": "Introduction to Machine Learning", "url": "https://www.coursera.org/learn/machine-learning"},
-                        {"name": "Python for Data Science", "url": "https://www.coursera.org/learn/python-data-science"},
-                        {"name": "Data Structures and Algorithms", "url": "https://www.coursera.org/learn/data-structures-algorithms"},
-                        {"name": "Business Analytics", "url": "https://www.coursera.org/learn/business-analytics"},
-                        {"name": "Digital Marketing Fundamentals", "url": "https://www.coursera.org/learn/digital-marketing"}
-                    ]
-                    for course in general_courses:
-                        st.write(f"- [{course['name']}]({course['url']})")
-
-            # University Suggestions Section
-            st.header("Top Universities")
-            with st.spinner("Finding universities that align with your profile..."):
-                time.sleep(2)  # Simulate processing time
-                university_recommendations = ds_custom_universities.head(5)  # Limit to top 5 universities
-
-                if not university_recommendations.empty:
-                    st.write("Based on your interests, here are some universities to consider:")
-                    for _, row in university_recommendations.iterrows():
-                        st.write(f"- {row['University Name']} ({row['Country']}) - {row['World Rank']}")
-                else:
-                    st.write("No specific university recommendations found. Check out the global top universities:")
-                    st.write(f"[Global Top Universities]({universities_url})")
-
-# End of code
+                    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})")