Duplicate from aieye/aieye_tutorial_template

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

.sessions/johndoe/level.txt

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+3

0_Introduction.py

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+import streamlit as st
+from utils.levels import complete_level, initialize_level, render_page, get_level
+from utils.login import initialize_login
+
+initialize_login()
+initialize_level()
+
+LEVEL=0
+
+def intro_page():
+    st.header("AI Eye Tutorial Template")
+    st.subheader("Level 0: Introduction")
+
+    st.write("""Welcome to the AI Eye Tutorial Template! This template is designed to help you create your own AI Eye tutorial.
+    The template is divided into levels, and each level has a set of tasks that you need to complete before you can move on to the next level.
+    You can use this template to create your own tutorial by completing the tasks in each level and adding your own content. You can also use 
+    this template to learn how to use AI Eye by completing the tasks in each level.""")
+
+    st.info(f"Current Level: {get_level()}")
+
+    if st.button("Complete"):
+        complete_level(LEVEL)
+
+render_page(intro_page, LEVEL)

README.md

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+---
+title: Aieye Tutorial Template
+emoji: 🌍
+colorFrom: green
+colorTo: purple
+sdk: streamlit
+sdk_version: 1.21.0
+app_file: 0_Introduction.py
+pinned: false
+license: openrail
+duplicated_from: aieye/aieye_tutorial_template
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

assets/quiz.json

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+[
+    {
+        "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/1_Step_1.py

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+import streamlit as st
+from utils.levels import complete_level, render_page, initialize_level
+LEVEL = 1
+
+initialize_level()
+
+def step1_page():
+    st.header("Step 1")
+    st.subheader("Level 1: Introduction")
+    st.write("This is the first step of the tutorial. You can add your own content here.")
+
+    if st.button("Complete"):
+        complete_level(LEVEL)
+
+render_page(step1_page, LEVEL)

pages/2_Step_2.py

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+import streamlit as st
+from utils.levels import complete_level, render_page, initialize_level
+
+initialize_level()
+
+LEVEL = 2
+
+def step2_page():
+    st.header("Step 2")
+    st.subheader("Level 2: Introduction")
+    st.write("This is the second step of the tutorial. You can add your own content here.")
+
+    if st.button("Complete"):
+        complete_level(LEVEL)
+
+render_page(step2_page, LEVEL)

pages/3_Quiz.py

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+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 = 3
+
+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/4_Congratulations.py

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+import streamlit as st
+from utils.levels import complete_level, render_page, initialize_level
+
+initialize_level()
+
+LEVEL = 4
+
+def step1_page():
+    st.header("Congratulations!")
+    st.subheader("You have completed the tutorial!")
+    st.write("You can now use this template to create your own tutorial.")
+    st.balloons()
+
+render_page(step1_page, LEVEL)

utils/levels.py

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+import streamlit as st
+from utils.login import get_login
+import os
+
+def initialize_level():
+    if 'level' not in st.session_state:
+        if get_login()["status"]:
+            if not os.path.exists(f".sessions/{get_login()['username']}/level.txt"):
+                with open(f".sessions/{get_login()['username']}/level.txt", "w") as f:
+                    f.write("0")
+                st.session_state['level'] = 0
+            else:
+                with open(f".sessions/{get_login()['username']}/level.txt", "r") as f:
+                    st.session_state['level'] = int(f.read())
+
+def get_level():
+    return st.session_state['level']
+
+def render_page(page, level):
+    if get_login()["status"]:
+        if st.session_state['level'] < level:
+            st.error(f"You need to complete Level {st.session_state['level']} first!")
+        else:
+            page()
+    else:
+        st.error("You need to login first!")
+
+def complete_level(level):
+    if st.session_state['level'] > level:
+        st.info(f'You have Already completed Level {level}!')
+    else:
+        st.session_state['level'] = level + 1
+        with open(f".sessions/{get_login()['username']}/level.txt", "w") as f:
+            f.write(str(st.session_state['level']))
+        st.balloons()
+        st.success(f'You have completed Level {level}! You can now move on to the next level.')

utils/login.py

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+import streamlit as st
+import os
+
+
+def initialize_login():
+    if "login" not in st.session_state:
+        st.columns(3)[1].image("assets/logo.png")
+        username = st.text_input("Username")
+        password = st.text_input("Password", type="password")
+        if st.button("Login"):
+            # TODO: replace with actual authorization check
+            authorized = {"status": True, "Name": "John Doe", "username": "johndoe"}
+            if authorized["status"]:
+                st.session_state["login"] = authorized
+                os.makedirs(
+                    os.path.join(".sessions", st.session_state["login"]["username"]),
+                    exist_ok=True,
+                )
+                st.success("Login Successful!")
+                st.experimental_rerun()
+            else:
+                st.error("Invalid username or password")
+    else:
+        st.sidebar.success(f'Hello, {st.session_state["login"]["Name"]}!')
+
+
+def get_login():
+    return st.session_state.get("login", {"status": False})