Update app.py

app.py

@@ -1,94 +1,111 @@
 import streamlit as st
-from datasets import load_dataset
 from transformers import pipeline
-import time
-
-# Display app title
-st.title("Career Counseling Application")
-st.subheader("Build Your Profile and Discover Tailored Career Recommendations")
-
-# Loading Spinner for Initial Data and Model Loading
-with st.spinner("Loading datasets and model... please wait"):
-    # Load datasets and handle any potential loading issues
-    try:
-        ds_natural_questions = load_dataset("google-research-datasets/natural_questions", split="train[:10%]")
-        ds_open_questions = load_dataset("launch/open_question_type", split="train[:10%]")
-        ds_question_generator = load_dataset("iarfmoose/question_generator", split="train[:10%]")
-        ds_jobs = load_dataset("lukebarousse/data_jobs", split="train[:10%]")
-        ds_courses = load_dataset("azrai99/coursera-course-dataset", split="train[:10%]")
-        
-        # Initialize a small, efficient pipeline for question answering
-        qa_pipeline = pipeline("text2text-generation", model="google/flan-t5-small")
-    except Exception as e:
-        st.error(f"Error loading resources: {e}")
-        st.stop()
-
-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)")
-
-profile_data = {
-    "educational_background": educational_background,
-    "interests": interests,
-    "tech_skills": tech_skills,
-    "soft_skills": soft_skills
-}
-
-if st.sidebar.button("Save Profile"):
-    st.session_state.profile_data = profile_data
-    st.sidebar.success("Profile saved successfully!")
+from datasets import load_dataset
+import signal
+
+# Timeout and Exception Handling
+class TimeoutException(Exception):
+    pass
+
+def timeout_handler(signum, frame):
+    raise TimeoutException("Operation timed out!")
+
+# Set a timeout handler
+signal.signal(signal.SIGALRM, timeout_handler)
+
+# Caching function for the model to avoid reloading
+@st.cache_resource
+def load_qa_model():
+    with st.spinner("Loading question-answering model..."):
+        try:
+            signal.alarm(30)  # Set a 30-second timeout for model loading
+            model = pipeline("question-answering", model="distilbert-base-uncased")  # Smaller model for faster loading
+            signal.alarm(0)  # Cancel the alarm if loaded successfully
+            st.success("Model loaded successfully!")
+            return model
+        except TimeoutException:
+            st.error("Model loading timed out. Please try again later.")
+            return None
+
+# Load QA Model
+qa_pipeline = load_qa_model()
+
+# Caching function for loading datasets
+@st.cache_data
+def load_job_dataset():
+    with st.spinner("Loading job dataset..."):
+        return load_dataset("lukebarousse/data_jobs", split="train[:100]")  # Smaller sample for faster loading
+
+def load_course_dataset():
+    with st.spinner("Loading course dataset..."):
+        return load_dataset("azrai99/coursera-course-dataset", split="train[:50]")  # Smaller sample for testing
+
+# Load datasets
+job_dataset = load_job_dataset()
+course_dataset = load_course_dataset()
+
+# Helper function for question generation
+def generate_questions(profile_data):
+    questions = [
+        f"What skills do you have in {profile_data.get('tech_skills', 'technology')}?",
+        "What are your preferred working conditions?",
+        "What motivates you in a job?",
+        "Are you interested in work-from-home opportunities?",
+        "Do you have a preference for job location?",
+        "Are you open to roles requiring a degree?",
+        "Do you have a preferred organization size?",
+        "What are your expected salary requirements?",
+        "What challenges do you enjoy solving?",
+        "Do you have experience with any specific programming languages or tools?"
+    ]
+    return questions
+
+# Streamlit Interface
+st.title("Career and Course Recommendations App")
+
+# Profile Setup Section
+st.header("Profile Setup")
+profile_data = {}
+profile_data["name"] = st.text_input("Your Name")
+profile_data["tech_skills"] = st.text_input("Technical Skills (comma-separated)")
+profile_data["preferred_location"] = st.text_input("Preferred Location")
+profile_data["work_preference"] = st.selectbox("Work Preference", ["On-site", "Remote", "Hybrid"])
 
 # Intelligent Q&A Session
 st.header("Intelligent Q&A Session")
-if "profile_data" in st.session_state:
-    st.write("Answer the following career-related questions based on your profile:")
-    questions = [q['question'] for q in ds_open_questions['question'][:10]]
-    responses = []
-
-    for question in questions:
-        st.write(f"Q: {question}")
-        answer = qa_pipeline(question)[0]["generated_text"]
-        responses.append(answer)
-        st.write(f"A: {answer}")
-        time.sleep(0.5)  # To simulate time delay and avoid overwhelming the interface
-
-# Career Recommendations Section
-st.header("Career and Job Recommendations")
-job_recommendations = []
-for job in ds_jobs:
-    job_skills = job.get("job_skills", "")
-    if any(skill.lower() in job_skills.lower() for skill in tech_skills.split(",")):
-        job_recommendations.append(job["job_title"])
-
-if job_recommendations:
-    st.subheader("Job Recommendations")
-    st.write("Based on your profile, here are some potential job roles:")
-    for job in job_recommendations[:5]:  # Show top 5 job recommendations
-        st.write("- ", job)
-else:
-    st.write("No specific job recommendations found matching your profile.")
-
-# Course Suggestions Section
-st.header("Course Suggestions")
-course_recommendations = []
-for course in ds_courses:
-    course_skills = course.get("Skills", "")
-    if any(interest.lower() in course_skills.lower() for interest in interests.split(",")):
-        course_recommendations.append((course["Title"], course["course_url"]))
-
-if course_recommendations:
-    st.subheader("Recommended Courses")
-    st.write("Here are some courses related to your interests:")
-    for title, url in course_recommendations[:5]:  # Show top 5 courses
-        st.write(f"- [{title}]({url})")
-else:
-    st.write("No specific courses found matching your interests.")
-
-# University Recommendations
-st.header("Top Universities")
-universities_url = "https://www.4icu.org/top-universities-world/"
-st.write(f"[View Top Universities Rankings]({universities_url})")
-
-st.write("Thank you for using the Career Counseling Application!")
+if st.button("Start Q&A Session"):
+    questions = generate_questions(profile_data)
+    answers = []
+    for i, question in enumerate(questions):
+        answer = st.text_input(f"Q{i+1}: {question}")
+        answers.append(answer)
+
+    profile_data["answers"] = answers
+    st.success("Q&A Session Completed!")
+
+# Career Recommendations Based on Profile
+st.header("Career Recommendations")
+if qa_pipeline and "tech_skills" in profile_data:
+    job_recommendations = [
+        job["job_title"]
+        for job in job_dataset
+        if "job_skills" in job and any(skill.lower() in job["job_skills"].lower() for skill in profile_data["tech_skills"].split(","))
+    ]
+    st.write("Recommended Jobs:")
+    for job in job_recommendations[:5]:  # Limit to 5 recommendations
+        st.write(f"- {job}")
+
+# Course Recommendations Based on Profile
+st.header("Course Recommendations")
+if qa_pipeline and "tech_skills" in profile_data:
+    course_recommendations = [
+        course["Title"]
+        for course in course_dataset
+        if "Skills" in course and any(skill.lower() in course["Skills"].lower() for skill in profile_data["tech_skills"].split(","))
+    ]
+    st.write("Recommended Courses:")
+    for course in course_recommendations[:5]:  # Limit to 5 recommendations
+        st.write(f"- {course}")
+
+# Debugging: Log Profile Data
+st.write("Profile Data:", profile_data)