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Create introduction_page.py
Browse files- pages/introduction_page.py +153 -0
pages/introduction_page.py
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import streamlit as st
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from streamlit_lottie import st_lottie
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import requests
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# Function to load Lottie animations
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def load_lottie_url(url: str):
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r = requests.get(url)
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if r.status_code == 200:
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return r.json()
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else:
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return None
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# Function to display the content of each page
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def show_content(topic):
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if topic == "Introduction":
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st.title("Understanding Data Science and Artificial Intelligence π")
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st.subheader("Overview of AI and Data Science")
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st.write("""
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Artificial Intelligence (AI) and Data Science have become buzzwords in today's tech-driven world.
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But what do they really mean, and why are they so significant? Letβs explore these fascinating concepts step by step!
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""")
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# Load the Lottie animation
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lottie_url = "https://assets4.lottiefiles.com/packages/lf20_tcbkqj.json"
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animation_data = load_lottie_url(lottie_url)
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if animation_data:
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st_lottie(animation_data, speed=1, width=600, height=400)
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else:
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st.write("Unable to load animation.")
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elif topic == "Understanding Intelligence":
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st.title("Understanding Intelligence")
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st.subheader("What is Natural Intelligence? πΎ")
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st.write("""
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**Definition**: NI refers to the intelligence naturally present in living beings.
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**Examples**:
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- A dog learning a trick. π
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- Humans solving puzzles or making everyday decisions. π§
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""")
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st.subheader("What is Artificial Intelligence? π€")
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st.write("""
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**Definition**: Artificial intelligence (AI) is man-made intelligence where machines mimic human intelligence to perform tasks.
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**Real-Life Examples**:
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- Netflix recommending shows youβd love. π¬
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- Google Maps finding the fastest route. πΊοΈ
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- Alexa answering your questions. ποΈ
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""")
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elif topic == "AI Tools: ML, DL, and Gen-AI":
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st.title("AI Tools: ML, DL, and Gen-AI")
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st.subheader("Machine Learning (ML) π₯οΈ")
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st.write("""
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- **What It Does**: ML enables machines to learn from patterns in data and make decisions.
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- **How It Works**: Similar to teaching a toddler to recognize fruits, ML algorithms process large datasets to "learn" and predict outcomes.
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- **Real-Life Applications**: Spam email detection π§, Predicting stock prices π.
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""")
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st.subheader("Deep Learning (DL) π€Ώ")
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st.write("""
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- **What It Does**: DL uses neural networks to process and analyze complex data.
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- **How It Works**: DL processes data in layers, enabling machines to perform sophisticated tasks like facial recognition and medical imaging.
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- **Real-Life Applications**: Self-driving cars π, Virtual assistants like Siri and Alexa. ποΈ
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""")
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st.subheader("Generative AI (Gen-AI) π¨")
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st.write("""
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- **What It Does**: Gen-AI enables machines to generate new content like text, images, and music.
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- **How It Works**: By learning patterns from data, Gen-AI creates outputs that feel original and human-like.
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- **Real-Life Applications**: ChatGPT (text generation), DALLΒ·E (image creation).
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""")
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elif topic == "Real-Life Analogies and Examples":
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st.title("Real-Life Analogies and Examples")
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st.subheader("Analogy: Tools Are Like Pens and Pencils")
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st.write("""
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- ML: Learns patterns (like sketching with a pencil).
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- DL: Adds depth and detail (like using a pen).
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- Gen-AI: Creates entirely new outputs (like turning sketches into colorful artwork).
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""")
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st.subheader("Learning vs. Generating: The Art Example π©βπ¨")
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st.write("""
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Think of a child learning to draw:
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- First, they learn the basics of drawing.
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- Then they generate their own unique artwork.
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AI follows the same process:
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- Learning: ML and DL handle this part.
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- Generating: Gen-AI takes over to create new outputs.
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""")
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elif topic == "What is Data Science?":
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st.title("What is Data Science? π")
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st.write("""
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Data Science is the art of extracting meaningful insights from raw data. It combines AI with statistics, computer science, and domain expertise to solve real-world problems.
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**Key Components of Data Science**:
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- **Data Collection**: Gathering information from various sources.
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- **Data Analysis**: Using tools to find patterns and trends.
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- **Data Visualization**: Presenting findings through charts and graphs.
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""")
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elif topic == "The Role of a Data Scientist":
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st.title("The Role of a Data Scientist")
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st.write("""
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A Data Scientist plays a crucial role in:
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- Building predictive models.
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- Analyzing customer behavior.
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- Designing solutions for business challenges.
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**Tools Used**: Python, R, SQL, Tableau, etc.
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""")
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elif topic == "Why AI and Data Science Matter":
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st.title("Why AI and Data Science Matter")
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st.write("""
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AI and Data Science are transforming industries by:
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- Automating tasks.
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- Enhancing decision-making.
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- Unlocking creative possibilities.
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**Fun Fact**: By 2030, AI is expected to add $15.7 trillion to the global economy. π
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""")
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elif topic == "Did You Know?":
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st.title("Did You Know?")
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st.write("""
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**AI is already being used to**:
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- Detect diseases in medical imaging.
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- Automate farming for higher crop yields.
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- Generate movie scripts and music albums.
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""")
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# Set up sidebar navigation
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topics = [
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"Introduction",
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"Understanding Intelligence",
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"AI Tools: ML, DL, and Gen-AI",
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"Real-Life Analogies and Examples",
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"What is Data Science?",
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"The Role of a Data Scientist",
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"Why AI and Data Science Matter",
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"Did You Know?"
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]
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st.sidebar.title("Topics")
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selection = st.sidebar.radio("Go to", topics)
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# Initialize session state with the first topic as the default
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if "page" not in st.session_state:
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st.session_state.page = topics[0]
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# Update session state automatically when sidebar selection changes
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st.session_state.page = selection
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# Display the selected content
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show_content(st.session_state.page)
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