Update app.py

app.py

@@ -1,3 +1,73 @@
+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)
@@ -11,33 +81,24 @@ 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}")
 
-        # Submit button for each question, with single-click prevention
-        submit_button = st.button("Submit Answer", key=f"submit{st.session_state.question_index}")
-        if submit_button and not st.session_state.get("submitted", False):
+        # Submit button for each question
+        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["submitted"] = True  # Set submission flag to prevent double-click
-                st.experimental_rerun()  # Refresh page to load the next question
+                # Trigger page refresh using st.session_state change
+                st.session_state.updated = True  # Indicate that a change has occurred
+                st.query_params = {"updated": "true"}  # Update query params to indicate change
             else:
                 st.warning("Please enter an answer before submitting.")
-                st.session_state["submitted"] = False  # Allow re-click if no answer is provided
-
-    # Reset the submission flag after the page refreshes
-    if "submitted" in st.session_state:
-        del st.session_state["submitted"]
 
     # 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.")
-        generate_button = st.button("Generate Response")
-        if generate_button and not st.session_state.get("generated", False):
+        if st.button("Generate Response"):
             # Save all answers in the profile data
             st.session_state.profile_data.update(st.session_state.answers)
 
-            # Set generated flag to prevent double-click on the "Generate Response" button
-            st.session_state["generated"] = True
-
             # Career and Job Recommendations Section
             st.header("Job Recommendations")
             with st.spinner('Generating job recommendations...'):
@@ -92,41 +153,35 @@ if "profile_data" in st.session_state and "question_index" in st.session_state:
                             "url": row.get("Links", "#")
                         })
 
-                # Remove duplicates from course recommendations
+                # 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})
 
-                # Display recommended courses
+                # 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]})")
 
-                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})")
+# 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})")
 
-            # Clear flags after generating response
-            if "generated" in st.session_state:
-                del st.session_state["generated"]
+# Conclusion
+st.write("Thank you for using the Career Counseling Application!")