Update pages/LIFE_CYCLE_OF_MACHINE_LEARNING.py

pages/LIFE_CYCLE_OF_MACHINE_LEARNING.py

@@ -2,257 +2,10 @@ import streamlit as st
 import pandas as pd
 import json
 import xml.etree.ElementTree as ET
-pip install matplotlib
-import matplotlib.pyplot as plt
-import numpy as np
-
-
-
-# Inject custom CSS to style the buttons
-st.markdown("""
-    <style>
-    .stButton>button {
-        background-color: #4CAF50;
-        color: white;
-        width: 100%;
-    }
-    </style>
-    """, unsafe_allow_html=True)
-
-# Initialize page navigation state
-if 'page' not in st.session_state:
-    st.session_state.page = "home"  # Default page is "home"
-
-# ----------------- Home Page -----------------
-def home_page():
-    st.title(":green[Lifecycle of a Machine Learning Project]")
-    st.markdown("Click on a stage to learn more about it.")
-
-    # Buttons for various stages of the ML project lifecycle
-    if st.button(":blue[📊 Data Collection]"):
-        st.session_state.page = "data_collection"
-
-    if st.button(":blue[🌟 Problem Statement]"):
-        st.markdown("### Problem Statement\nIdentify the problem you want to solve and set clear objectives and success criteria.")
-    
-    if st.button(":blue[🛠️ Simple EDA]"):
-        st.markdown("### Simple EDA\nPerform exploratory data analysis to understand data distributions and relationships.")
-    
-    if st.button(":blue[🧹 Data Pre-Processing]"):
-        st.markdown("### Data Pre-Processing\nConvert raw data into cleaned data.")
-
-    if st.button(":blue[📈 Exploratory Data Analysis (EDA)]"):
-        st.markdown("### Exploratory Data Analysis (EDA)\nVisualize and analyze the data to understand its distributions and relationships.")
-
-    if st.button(":blue[🏋️ Feature Engineering]"):
-        st.markdown("### Feature Engineering\nCreate new features from existing data.")
-
-    if st.button(":blue[🤖 Model Training]"):
-        st.markdown("### Model Training\nTrain the model using the training data and optimize its parameters.")
-
-    if st.button(":blue[🔧 Model Testing]"):
-        st.markdown("### Model Testing\nAssess the model's performance using various metrics and cross-validation techniques.")
-
-    if st.button(":blue[🚀 Model Deployment]"):
-        st.markdown("### Model Deployment\nIntegrate the trained model into a production environment and monitor its performance.")
-
-    if st.button(":blue[📝 Monitoring]"):
-        st.markdown("### Monitoring\nPeriodically retrain the model with new data and update features as needed.")
-
-# ----------------- Data Collection Page -----------------
-def data_collection_page():
-    st.title(":red[Data Collection]")
-    st.markdown("### Data Collection\nThis page discusses the process of Data Collection.")
-    st.markdown("Types of Data: **Structured**, **Unstructured**, **Semi-Structured**")
-
-    if st.button(":blue[🌟 Structured Data]"):
-        st.session_state.page = "structured_data"
-
-    if st.button(":blue[📷 Unstructured Data]"):
-        st.session_state.page = "unstructured_data"
-
-    if st.button(":blue[🗃️ Semi-Structured Data]"):
-        st.session_state.page = "semi_structured_data"
-
-    if st.button("Back to Home"):
-        st.session_state.page = "home"
-
-# ----------------- Structured Data Page -----------------
-def structured_data_page():
-    st.title(":blue[Structured Data]")
-    st.markdown("""
-    Structured data is highly organized and typically stored in tables like spreadsheets or databases. It is easy to search and analyze.
-    """)
-    st.markdown("### Examples: Excel files")
-
-    if st.button(":green[📊 Excel]"):
-        st.session_state.page = "excel"
-
-    if st.button("Back to Data Collection"):
-        st.session_state.page = "data_collection"
-
-# ----------------- Excel Data Page -----------------
-def excel_page():
-    st.title(":green[Excel Data Format]")
-
-    st.write("### What is Excel?")
-    st.write("Excel is a spreadsheet tool for storing data in tabular format with rows and columns. Common file extensions: .xls, .xlsx.")
-
-    st.write("### How to Read Excel Files")
-    st.code("""
-import pandas as pd
-
-# Read an Excel file
-df = pd.read_excel('data.xlsx', sheet_name='Sheet1')
-print(df)
-    """, language='python')
-
-    st.write("### Issues Encountered")
-    st.write("""
-- **File not found**: Incorrect file path.
-- **Sheet name error**: Specified sheet doesn't exist.
-- **Missing libraries**: openpyxl or xlrd might be missing.
-""")
-
-    st.write("### Solutions to These Issues")
-    st.code("""
-# Install required libraries
-# pip install openpyxl xlrd
-
-# Handle missing file
-try:
-    df = pd.read_excel('data.xlsx', sheet_name='Sheet1')
-except FileNotFoundError:
-    print("File not found. Check the file path.")
-
-# List available sheet names
-excel_file = pd.ExcelFile('data.xlsx')
-print(excel_file.sheet_names)
-    """, language='python')
-
-    st.markdown('[Jupyter Notebook](https://colab.research.google.com/drive/1Dv68m9hcRzXsLRlRit0uZc-8CB8U6VV3?usp=sharing)')
-   
-
-    if st.button("Back to Structured Data"):
-        st.session_state.page = "structured_data"
-
-# ----------------- Unstructured Data Page -----------------
-
-from PIL import Image
-
-
-def unstructured_data_page():
-    st.title(":blue[Unstructured Data]")
-    
-    st.markdown("""
-    *Unstructured data* does not have a predefined format. It consists of various data types like text, images, videos, and audio files.
-    Examples include:
-    - Images (e.g., .jpg, .png)
-    - Videos (e.g., .mp4, .avi)
-    - Social media posts
-    """)
-
-    ### Handling Image Data Section
-    st.header("🖼️ Handling Image Data")
-    st.markdown("""
-    Image data can be processed using libraries like OpenCV and PIL (Pillow). Images often need to be preprocessed for tasks like analysis, classification, or feature extraction. Common operations include:
-    - **Reading and displaying images**
-    - **Converting to grayscale**
-    - **Resizing and cropping**
-    - **Rotating and flipping**
-    - **Applying filters**
-    - **Edge detection and other transformations**
-    """)
-
-    st.code("""
 from PIL import Image
 import numpy as np
 import matplotlib.pyplot as plt
 
-# Open an image file
-image = Image.open('sample_image.jpg')
-image.show()
-
-# Convert image to grayscale
-gray_image = image.convert('L')
-gray_image.show()
-
-# Resize the image
-resized_image = image.resize((200, 200))
-resized_image.show()
-
-# Rotate the image by 90 degrees
-rotated_image = image.rotate(90)
-rotated_image.show()
-
-# Convert the image to a NumPy array and display its shape
-image_array = np.array(image)
-print(image_array.shape)
-
-# Display the image array as a plot
-plt.imshow(image)
-plt.title("Original Image")
-plt.axis('off')
-plt.show()
-    """, language='python')
-
-    st.markdown("""
-    **Common Image Processing Techniques:**
-    - **Resizing**: Adjust the dimensions of an image for uniformity in models.
-    - **Cropping**: Extract a region of interest (ROI) from an image.
-    - **Grayscale Conversion**: Simplify image data by reducing it to a single channel.
-    - **Rotation/Flipping**: Perform augmentations to increase the dataset for model training.
-    - **Edge Detection**: Identify edges in images using filters like the Sobel or Canny filters.
-    """)
-
-    ### Challenges and Solutions Section
-    st.markdown("### Challenges with Unstructured Data")
-    st.write("""
-    - *Noise and Inconsistency*: Data is often incomplete or noisy.
-    - *Storage Requirements*: Large size and variability in data types.
-    - *Processing Time*: Analyzing unstructured data is computationally expensive.
-    """)
-
-    st.markdown("### Solutions")
-    st.write("""
-    - *Data Cleaning*: Preprocess data to remove noise.
-    - *Efficient Storage*: Use NoSQL databases (e.g., MongoDB) or cloud storage.
-    - *Parallel Processing*: Utilize frameworks like Apache Spark.
-    """)
-
-    # Button to Navigate to Introduction to Image
-    if st.button("Introduction to Image"):
-        st.session_state.page = "introduction_to_image"
-
-    # Navigation Button
-    if st.button("Back to Data Collection"):
-        st.session_state.page = "data_collection"
-
-
-   
-  
-# ----------------- Semi-Structured Data Page -----------------
-def semi_structured_data_page():
-    st.title(":orange[Semi-Structured Data]")
-    st.markdown("""
-    Semi-structured data does not follow the rigid structure of relational databases but still has some organizational properties. Examples include:
-    - JSON files
-    - XML files
-    """)
-
-    if st.button(":green[💾 JSON]"):
-        st.session_state.page = "json"
-        
-    if st.button(":green[📄 CSV]"):
-        st.session_state.page = "csv"
-
-    if st.button(":green[📄 XML]"):
-        st.session_state.page = "xml"
-import streamlit as st
-import pandas as pd
-import json
-import xml.etree.ElementTree as ET
-
 # Inject custom CSS to style the buttons
 st.markdown("""
     <style>
@@ -382,11 +135,6 @@ print(excel_file.sheet_names)
         st.session_state.page = "structured_data"
 
 # ----------------- Unstructured Data Page -----------------
-
-from PIL import Image
-import numpy as np
-import matplotlib.pyplot as plt
-
 def unstructured_data_page():
     st.title(":blue[Unstructured Data]")
     
@@ -474,9 +222,6 @@ plt.show()
     if st.button("Back to Data Collection"):
         st.session_state.page = "data_collection"
 
-
-   
-  
 # ----------------- Semi-Structured Data Page -----------------
 def semi_structured_data_page():
     st.title(":orange[Semi-Structured Data]")
@@ -504,7 +249,7 @@ def json_page():
 
     st.write("### What is JSON?")
     st.write("""
-    JSON (JavaScript Object Notation) is a lightweight data-interchange format that's easy for humans to read and write, and easy for machines to parse and generate. JSON is often used in APIs, configuration files, and data transfer applications.
+    JSON (JavaScript Object Notation) is a lightweight data-interchange format that's easy for humans to read and write, and easy for machines to parse and generate. JSON is often used in APIs, configuration files,       and data transfer applications.
     """)
 
     st.write("### Reading JSON Files")