added quiz page

.sessions/johndoe/level.txt

@@ -0,0 +1 @@
+6

assets/quiz.json

@@ -0,0 +1,92 @@
+[
+    {
+        "question": "Which of the following best describes emotion detection?",
+        "options": [
+            "Teaching computers to understand human emotions",
+            "Teaching humans to understand computer languages",
+            "Teaching computers to create video games",
+            "Teaching humans to recognize facial features"
+        ],
+        "answer": "Teaching computers to understand human emotions"
+    },
+    {
+        "question": "What programming language is commonly used in developing emotion detection applications?",
+        "options": [
+            "Python",
+            "Java",
+            "C++",
+            "Ruby"
+        ],
+        "answer": "Python"
+    },
+    {
+        "question": "What is the purpose of OpenCV in an emotion detection application?",
+        "options": [
+            "To analyze and manipulate images and videos",
+            "To recognize and understand human emotions",
+            "To create graphical user interfaces",
+            "To generate statistical reports"
+        ],
+        "answer": "To analyze and manipulate images and videos"
+    },
+    {
+        "question": "Why is it important to have a diverse dataset when training an emotion detection model?",
+        "options": [
+            "It helps the model better understand different facial expressions",
+            "It improves the performance of the computer's processor",
+            "It makes the application run faster",
+            "It reduces the training time for the model"
+        ],
+        "answer": "It helps the model better understand different facial expressions"
+    },
+    {
+        "question": "What is the final step after training the model in an emotion detection application?",
+        "options": [
+            "Collect more data for training",
+            "Test the model's accuracy and performance",
+            "Install additional software plugins",
+            "Optimize the application's user interface"
+        ],
+        "answer": "Test the model's accuracy and performance"
+    },
+    {
+        "question": "How does the inference process work in an emotion detection application?",
+        "options": [
+            "It analyzes facial features and predicts the associated emotion",
+            "It collects user feedback and improves the model's accuracy",
+            "It converts emotions into numerical values for analysis",
+            "It adjusts the application's settings based on user preferences"
+        ],
+        "answer": "It analyzes facial features and predicts the associated emotion"
+    },
+    {
+        "question": "What is an example of a real-world application of emotion detection technology?",
+        "options": [
+            "Virtual reality gaming",
+            "Weather forecasting",
+            "Online shopping",
+            "Recipe suggestions"
+        ],
+        "answer": "Virtual reality gaming"
+    },
+    {
+        "question": "What is the importance of ethical considerations in emotion detection applications?",
+        "options": [
+            "Ensuring privacy and consent when collecting data",
+            "Optimizing the application's performance",
+            "Reducing the complexity of the model",
+            "Enhancing the visual appearance of the application"
+        ],
+        "answer": "Ensuring privacy and consent when collecting data"
+    },
+    {
+        "question": "What can students do to further explore and improve their emotion detection application?",
+        "options": [
+            "Experiment with different image preprocessing techniques",
+            "Rewrite the entire code from scratch",
+            "Avoid using real-time video feeds for testing",
+            "Skip the testing phase and move directly to deployment"
+        ],
+        "answer": "Experiment with different image preprocessing techniques"
+    }
+]

pages/5_Quiz.py

@@ -0,0 +1,51 @@
+import streamlit as st
+from utils.levels import complete_level, render_page, initialize_level
+from utils.login import initialize_login
+import random
+import json
+
+LEVEL = 5
+
+initialize_login()
+initialize_level()
+
+
+if "questions" not in st.session_state:
+    with open("assets/quiz.json") as f:
+        questions = json.load(f)
+
+    for i in range(len(questions)):
+        random.shuffle(questions[i]["options"])
+    random.shuffle(questions)
+
+    st.session_state["questions"] = questions
+
+
+def step_page():
+    st.header("Quiz")
+    st.markdown(
+        """Now that you've learned about how Face Recognition work, let's test your knowledge with a quiz!"""
+    )
+
+    for i in range(len(st.session_state["questions"])):
+        st.subheader(f"Question {i + 1}")
+        question = st.session_state["questions"][i]
+        st.markdown(question["question"])
+        answer = st.radio("Select an answer:", question["options"], key=f"radio{i}")
+
+        if st.session_state.get("EVALUATE", False):
+            if answer == question["answer"]:
+                st.success("Correct!")
+            else:
+                st.error("Incorrect! Try Again")
+
+    if st.button("Evaluate"):
+        st.session_state["EVALUATE"] = True
+        st.experimental_rerun()
+
+    st.info("Click on the button below to complete the tutorial!")
+    if st.button("Complete"):
+        complete_level(LEVEL)
+
+
+render_page(step_page, LEVEL)

pages/{5_Congratulations.py → 6_Congratulations.py}

@@ -3,10 +3,15 @@ from utils.levels import render_page, initialize_level
 
 initialize_level()
 
-LEVEL = 5
+LEVEL = 6
+
 
 def complete_page():
     st.header("Congratulations!")
-    st.subheader("You have completed the tutorial!")
+    st.subheader(
+        "You have completed the tutorial! Now you know how to use Face Recognition to detect faces in images and videos!"
+    )
+    st.balloons()
+
 
-render_page(complete_page, LEVEL)
+render_page(complete_page, LEVEL)