Update pages/Measurement_of_disperssion.py

pages/Measurement_of_disperssion.py

@@ -1,206 +1,167 @@
 import streamlit as st
-import math
-from functools import reduce
+import pandas as pd
 import numpy as np
-st.subheader("Measure Of Disperssion ",divider=True)
-st.markdown("""Measure Of Disperssion will give spread of our collected data around the central value.It's classifed into two types
-""")
-st.markdown(''':violet[Absolute Measure] \n absolute will give the spread of data in one unit.for example if the given data is in 'cm'
-the output will be in cm''')
-st.markdown(''':violet[Relative Measure] \n  Relative will be free from unit's''')
-st.header("**Absolute Measure**")
-st.subheader("Range",divider=True)
-st.subheader("Quartile Deviation",divider=True)
-st.subheader("Varience",divider=True)
-st.subheader("Standard Deviation",divider=True)
-st.header("**Relative Measure**")
-st.subheader("Coefficent Of Range",divider=True)
-st.subheader("Coefficent Of Quartile Deviation",divider=True)
-st.subheader("Coefficent Of Varience",divider=True)
-st.subheader("Coefficent Of Standard Deviation",divider=True)
-st.markdown(''':orange[**Range**] is one of the measure to find the disperssion.But is not at all mostly used beause it don't focus on the entire data.
-''')
-st.subheader("Absolute Range")
-st.latex(r'''
-    \text{Absolute Range} = \text{Maximum Value} - \text{Minimum Value}
-''')
-st.subheader("Relative Range")
-st.latex(r'''
-    \text{Relative Range} = \frac{\text{Absolute Range}}{\text{Mean}} \times 100
-''')
-def absolute_range(list1):
-    max_val = np.max(list1)
-    min_val = np.min(list1)
-    return max_val - min_val
-st.title("Calculate Absolute Range")
-num_input = st.text_input("Enter the values separated by commas (e.g., 1,2,3,4)", key="num_input")
-list1 = []
-value = num_input.split(",")
-for i in value:
-    if i.isdigit():
-        list1.append(int(i))
-if list1:
-    result = absolute_range(list1)
-    st.write("Absolute Range:", result)
-else:
-    st.write("Please enter valid numbers.") 
-def relative_range(list1):
-  max=np.max(list1)
-  min=np.min(list1)
-  return round((max-min)/(max+min)*100,2)
-st.title("Calculate Relative Range")
-num_input1=st.text_input("Enter the values separated by commas (e.g., 1,2,3,4)", key="num_input1")
-value=num_input1.split(",")
-list1=[]
-for i in value:
-    if i.isdigit():
-        list1.append(int(i))
-    else:
-        pass
-if list1:
-    result=relative_range(list1)
-    st.write("Relative Range",result)
-else:
-    st.write("Please enter valid numbers.")
-st.markdown(''':orange[**Quartile Deviation**] is one of the measure to find the disperssion.In this type the data is divided into 4 equal parts.
-It will mostly focus on the central data.
-''')
-st.subheader("Absolute Quartile Deviation")
-st.latex(r'''
-    QD = \frac{Q3 - Q1}{2}
-''')
-st.subheader("Relative Quartile Deviation")
-st.latex(r'''
-    \text{Relative QD} = \frac{Q3 - Q1}{Q3 + Q1} \times 100
-''')
-def abs_quartile_dev(list1):
-    q1=np.percentile(list1,25)
-    q3=np.percentile(list1,75)
-    return round((q3-q1)/2,2)
-st.title("Caluculate Absolute Quartile Deviation")
-num_input2=st.text_input("Enter the values separated by commas (e.g., 1,2,3,4)", key="num_input2")
-value=num_input2.split(",")
-list1=[]
-for i in value:
-    if i.isdigit():
-        list1.append(int(i))
-    else:
-        pass
-if list1:
-    result=abs_quartile_dev(list1)
-    st.write("Absolute Quartile Deviation",result)
-else:
-    st.write("Please enter valid numbers.")
-def rel_quartile_dev(list1):
-    q1=np.percentile(list1,25)
-    q3=np.percentile(list1,75)
-    return round(((q3-q1)/(q3+q1))*100,2) 
-st.title("Caluculate Relative Quartile Deviation")
-num_input3=st.text_input("Enter the values separated by commas (e.g., 1,2,3,4)", key="num_input3")
-value=num_input3.split(",")
-list1=[]
-for i in value:
-    if i.isdigit():
-        list1.append(int(i))
-    else:
-        pass
-if list1:
-    result=rel_quartile_dev(list1)
-    st.write("Relative Quartile Deviation",result)
-else:
-    st.write("Please enter valid numbers.")
-st.markdown(''':orange[**Varience**] is one of the measure to find the disperssion.It is one of the best measure to find the disperssion.The only
-drawback is when in Varience is in order to overcome negitive value we square them thus the distance is doubled
-''')
-st.subheader("Absolute Variance")
-st.latex(r'''
-    \text{Var} = \frac{1}{N} \sum_{i=1}^{N} (x_i - \bar{x})^2
-''')
-st.subheader("Relative Variance")
-st.latex(r'''
-    \text{Relative Var} = \frac{\text{Var}}{\bar{x}} \times 100
-''')
-def absolute_varience(list1):
-    return np.var(list1)
-st.title("Caluculate Absolute varience")
-num_input4=st.text_input("Enter the values separated by commas (e.g., 1,2,3,4)", key="num_input4")
-value=num_input4.split(",")
-list1=[]
-for i in value:
-    if i.isdigit():
-        list1.append(int(i))
-    else:
-        pass
-if list1:
-    result=absolute_varience(list1)
-    st.write("Absolute Varience",result)
-else:
-    st.write("Please enter valid numbers.")
-def relative_varience(list1):
-    mean=round(np.mean(list1),2)
-    std_dev=np.std(list1,ddof=1)
-    return round((std_dev/mean)*100,2)
-st.title("Caluculate Relative Varience")
-num_input5=st.text_input("Enter the values separated by commas (e.g., 1,2,3,4)", key="num_input5")
-value=num_input5.split(",")
-list1=[]
-for i in value:
-    if i.isdigit():
-        list1.append(int(i))
-    else:
-        pass
-if list1:
-    result=relative_varience(list1)
-    st.write("Relative Varience",result)
-else:
-    st.write("Please enter valid numbers.")
-st.markdown(''':orange[**Standard Deviation**] is one of the measure to find the disperssion.It is one of the best measure to find the disperssion.It over comes the 
-disadvantage occured in varience by square rooting it.
-''')
-st.subheader("Absolute Standard Deviation")
-st.latex(r'''
-    \sigma = \sqrt{\frac{1}{N} \sum_{i=1}^{N} (x_i - \bar{x})^2}
-''')
-st.subheader("Relative Standard Deviation")
-st.latex(r'''
-    \text{Relative SD} = \frac{\sigma}{\bar{x}}
-''')
-def absolute_std(list1):
-    return round(np.std(list1),2)
-st.title("Caluculate Absolute Standard Deviation")
-num_input6=st.text_input("Enter the values separated by commas (e.g., 1,2,3,4)", key="num_input6")
-value=num_input6.split(",")
-list1=[]
-for i in value:
-    if i.isdigit():
-        list1.append(int(i))
-    else:
-        pass
-if list1:
-    result=absolute_std(list1)
-    st.write("Absolute Standard Deviation",result)
-else:
-    st.write("Please enter valid numbers.")
-def relative_std(list1):
-    mean=round(np.mean(list1),2)
-    std_dev=np.std(list1,ddof=1)
-    return round((std_dev/mean),2)
-st.title("Caluculate Relative Standard Deviation")
-num_input7=st.text_input("Enter the values separated by commas (e.g., 1,2,3,4)", key="num_input7")
-value=num_input7.split(",")
-list1=[]
-for i in value:
-    if i.isdigit():
-        list1.append(int(i))
-    else:
-        pass
-if list1:
-    result=relative_std(list1)
-    st.write("Relative Standard Deviation",result)
-else:
-    st.write("Please enter valid numbers.")
-st.subheader("Distribution",divider=True)
-st.markdown(''':blue[**Distribution**] is a measure will will tell how the shape of data or in which shape the data is spread.It will help in 
-analysis.There are few types of distribution \n *  Normal Distribution \n * Uniform Distribution \n * Binomial Distribution \n * Poisson Distribution
-\n * Exponential Distribution \n * Chi-Square Distribution \n * T-Distribution
-''')
+import matplotlib.pyplot as plt
+import random
+
+# Custom CSS for styling
+st.markdown("""
+    <style>
+    /* Set a soft background color */
+    body {
+        background-color: #eef2f7;
+    }
+    /* Style for main title */
+    h1 {
+        color: #00FFFF;
+        font-family: 'Roboto', sans-serif;
+        font-weight: 700;
+        text-align: center;
+        margin-bottom: 25px;
+    }
+    /* Style for headers */
+    h2 {
+        color: #FFFACD;
+        font-family: 'Roboto', sans-serif;
+        font-weight: 600;
+        margin-top: 30px;
+    }
+    
+    /* Style for subheaders */
+    h3 {
+        color: #ba95b0;
+        font-family: 'Roboto', sans-serif;
+        font-weight: 500;
+        margin-top: 20px;
+    }
+    .custom-subheader {
+        color: #00FFFF;
+        font-family: 'Roboto', sans-serif;
+        font-weight: 600;
+        margin-bottom: 15px;
+    }
+    /* Paragraph styling */
+    p {
+        font-family: 'Georgia', serif;
+        line-height: 1.8;
+        color: #FFFFFF; /* Darker text color for better visibility */
+        margin-bottom: 20px;
+    }
+    /* List styling with checkmark bullets */
+    .icon-bullet {
+        list-style-type: none;
+        padding-left: 20px;
+    }
+    .icon-bullet li {
+        font-family: 'Georgia', serif;
+        font-size: 1.1em;
+        margin-bottom: 10px;
+        color: #FFFFF0; /* Darker text color for better visibility */
+    }
+    .icon-bullet li::before {
+        content: "✔️";
+        padding-right: 10px;
+        color: #b3b3ff;
+    }
+    /* Sidebar styling */
+    .sidebar .sidebar-content {
+        background-color: #ffffff;
+        border-radius: 10px;
+        padding: 15px;
+    }
+    .sidebar h2 {
+        color: #495057;
+    }
+    </style>
+    """, unsafe_allow_html=True)
+
+# Introduction section
+st.markdown("""Before jumping into the context, let's understand some important words..! 🌟""", unsafe_allow_html=True)
+
+st.header("Natural Intelligence 🧠")
+st.markdown("""**Natural Intelligence** is the ability of living beings, like humans and animals, to think, learn, and solve problems using their brains or minds. 🐾""", unsafe_allow_html=True)
+
+st.header("Artificial Intelligence 🤖")
+st.markdown("""
+The term 'Artificial' refers to **man-made**, and 'Intelligence' is the ability to learn, understand, and apply knowledge to solve problems. This can be simplified as **man-made intelligence**.  
+In this, machines try to **mimic/copy** natural intelligence. 🧠 ➡️ 🤖
+""", unsafe_allow_html=True)
+
+# Interactive section for choosing AI tools
+st.markdown("""
+To mimic/copy natural intelligence, we have 3 powerful tools:  
+<ul class="icon-bullet">
+  <li>🖥️ Machine Learning</li>
+  <li>🤿 Deep Learning</li>
+  <li>🎨 Generative AI</li>
+</ul>
+""", unsafe_allow_html=True)
+
+# Let the user choose an AI tool to learn more about
+tool = st.selectbox("Which AI tool would you like to learn more about?", 
+                    ["Machine Learning", "Deep Learning", "Generative AI"])
+
+if tool == "Machine Learning":
+    st.subheader("Machine Learning 🖥️")
+    st.markdown("""
+    Machine Learning is a tool that helps us mimic/copy natural intelligence 🧠 to create artificial intelligence 🤖.  
+    It's like teaching machines how to learn from data and make decisions! 📊
+    """)
+    # Interactive chart for machine learning
+    data = np.random.randn(100)
+    st.line_chart(data)
+    st.markdown("Here is a simple data visualization related to Machine Learning. 📊")
+
+elif tool == "Deep Learning":
+    st.subheader("Deep Learning 🤿")
+    st.markdown("""
+    Deep Learning is a powerful tool that mimics/copies natural intelligence 🧠 to create artificial intelligence 🤖.  
+    It goes deeper by using neural networks to solve complex problems! 🌐
+    """)
+    # Interactive slider to simulate Deep Learning training
+    epochs = st.slider("Select the number of epochs (training cycles):", min_value=1, max_value=20, value=5)
+    st.markdown(f"Training for {epochs} epochs...")
+    # Simulate a training graph
+    training_data = np.linspace(0, epochs, epochs)
+    accuracy = np.random.rand(epochs)
+    st.line_chart(accuracy)
+    st.markdown("This is a simulated training accuracy plot.")
+
+elif tool == "Generative AI":
+    st.subheader("Generative AI 🎨")
+    st.markdown("""
+    Generative AI refers to the ability of machines to create new, never-before-seen data.  
+    It mimics the generative process of natural intelligence, but in a digital form! 🎨
+    """)
+    # Interactive input for generating text or art
+    user_input = st.text_input("Enter a prompt for Generative AI to create text:")
+    if user_input:
+        st.markdown(f"Generative AI is generating text for: {user_input}")
+        generated_text = f"This is a generated response to your input: {user_input}"
+        st.markdown(generated_text)
+        st.markdown("Imagine if this was a generated artwork instead! 🎨")
+
+# Additional sections about AI and deep learning
+st.subheader("Why ML? Why DL? Why Generative AI? 🤔")
+st.markdown("""
+Thanks to **natural intelligence**, we have two precious abilities:  
+<ul class="icon-bullet">
+  <li>📚 Learn</li>
+  <li>✨ Generate</li>
+</ul>
+""", unsafe_allow_html=True)
+
+st.subheader("Whenever machines want to adopt the learning ability 🧠")
+st.markdown("""
+<ul class="icon-bullet">
+  <li>🖥️ Machine Learning</li>
+  <li>🤿 Deep Learning</li>
+</ul>
+""", unsafe_allow_html=True)
+
+st.subheader("Whenever machines want to adopt the generating ability ✨")
+st.markdown("""
+<ul class="icon-bullet">
+  <li>🎨 Generative AI</li>             
+</ul>
+""", unsafe_allow_html=True)