added quiz materials

.sessions/johndoe/level.txt

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

README.md

@@ -6,7 +6,7 @@ colorTo: orange
 sdk: streamlit
 sdk_version: 1.21.0
 app_file: 0_Introduction.py
-pinned: false
+pinned: true
 license: openrail
 duplicated_from: aieye/aieye_tutorial_template
 ---

assets/quiz.json

@@ -0,0 +1,102 @@
+[
+    {
+        "question": "Which of the following best describes speech recognition?",
+        "options": [
+            "Teaching computers to understand human speech",
+            "Teaching humans to understand computer languages",
+            "Teaching computers to write code",
+            "Teaching humans to speak multiple languages"
+        ],
+        "answer": "Teaching computers to understand human speech"
+    },
+    {
+        "question": "What is one of the popular applications of speech recognition?",
+        "options": [
+            "Facial recognition",
+            "Virtual reality gaming",
+            "Voice assistants",
+            "Emotion detection"
+        ],
+        "answer": "Voice assistants"
+    },
+    {
+        "question": "Which decade saw the introduction of Hidden Markov Models (HMMs) in speech recognition?",
+        "options": [
+            "1960s",
+            "1970s",
+            "1980s",
+            "1990s"
+        ],
+        "answer": "1980s"
+    },
+    {
+        "question": "What was the purpose of the DARPA challenges in speech recognition?",
+        "options": [
+            "To promote research and advancements in the field",
+            "To train voice assistants",
+            "To improve internet connectivity",
+            "To develop social media platforms"
+        ],
+        "answer": "To promote research and advancements in the field"
+    },
+    {
+        "question": "What role did neural networks play in speech recognition?",
+        "options": [
+            "They helped in translating spoken language",
+            "They allowed for automatic extraction of features from speech data",
+            "They facilitated handwriting recognition",
+            "They improved internet security"
+        ],
+        "answer": "They allowed for automatic extraction of features from speech data"
+    },
+    {
+        "question": "Which popular voice assistant was introduced in 2011?",
+        "options": [
+            "Siri",
+            "Alexa",
+            "Cortana",
+            "Google Assistant"
+        ],
+        "answer": "Siri"
+    },
+    {
+        "question": "What recent advancements have contributed to the improvement of speech recognition?",
+        "options": [
+            "Neural networks and deep learning",
+            "Augmented reality technology",
+            "Quantum computing",
+            "Robotics"
+        ],
+        "answer": "Neural networks and deep learning"
+    },
+    {
+        "question": "In which field is speech recognition commonly used to convert spoken words into written text?",
+        "options": [
+            "Medical diagnosis",
+            "Automotive engineering",
+            "Transcription services",
+            "Space exploration"
+        ],
+        "answer": "Transcription services"
+    },
+    {
+        "question": "How does speech recognition technology benefit individuals with disabilities?",
+        "options": [
+            "It helps improve memory and cognitive skills",
+            "It enables them to interact with technology through voice commands",
+            "It provides physical therapy",
+            "It helps improve vision and hearing abilities"
+        ],
+        "answer": "It enables them to interact with technology through voice commands"
+    },
+    {
+        "question": "Which speech recognition system developed by OpenAI is known for its state-of-the-art performance?",
+        "options": [
+            "Whisper",
+            "Roomba",
+            "Siri",
+            "Echo"
+        ],
+        "answer": "Whisper"
+    }
+]

pages/3_Training the Model.py

@@ -34,7 +34,7 @@ def step3_page():
     """
     )
     st.image(
-        "https://1.bp.blogspot.com/-Cehw4NbX2L8/X1-DJeQgtmI/AAAAAAAAKeE/fA55q9EXsgQLU0trbgX_vJkiFMwR927yQCLcBGAsYHQ/s853/final.gif",
+        "https://recfaces.com/wp-content/uploads/2021/03/rf-emotion-recognition-rf-830x495-1.jpeg",
         use_column_width=True,
     )
     st.markdown(

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 Emotion Detection 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 +3,13 @@ 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 build an emotion classifier!")
+    st.balloons()
+
 
-render_page(complete_page, LEVEL)
+render_page(complete_page, LEVEL)