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introduction page added
Browse files- pages/01_introduction.py +0 -155
pages/01_introduction.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 🌟")
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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? Let’s 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? 🐾")
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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. 🐕
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# - Humans solving puzzles or making everyday decisions. 🧠
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# """)
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# st.subheader("What is Artificial Intelligence? 🤖")
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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 you’d love. 🎬
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# - Google Maps finding the fastest route. 🗺️
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# - Alexa answering your questions. 🎙️
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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) 🖥️")
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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 📧, Predicting stock prices 📈.
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# """)
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# st.subheader("Deep Learning (DL) 🤿")
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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 🚗, Virtual assistants like Siri and Alexa. 🎙️
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# """)
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# st.subheader("Generative AI (Gen-AI) 🎨")
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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), DALL·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 👩🎨")
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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? 📊")
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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. 🚀
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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 if not already done
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# if "page" not in st.session_state:
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# st.session_state.page = selection
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# # Update session state if sidebar selection is changed
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# if st.sidebar.button("Navigate"):
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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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import streamlit as st
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from streamlit_lottie import st_lottie
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import requests
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1 |
import streamlit as st
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from streamlit_lottie import st_lottie
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import requests
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