app.py

import streamlit as st
import numpy as np
import pandas as pd


# Load the dataset
fruits = pd.read_table('fruit_data_with_colors.txt')

# Streamlit app title
st.title("Fruit Classification 🍓🍉🍒🍑")
st.write("Classifying different types of fruits based on various attributes.")

# Create two columns with different widths
col1, col2 = st.columns([2, 1])  # 2:1 ratio for column widths

# Part 1: Display dataset and its shape
with col1:
    st.subheader("Dataset Preview")
    st.write(fruits.head())
    st.write("Shape of the dataset:", fruits.shape)

# Part 2: Display unique fruit names
with col2:
    st.subheader("Fruit Names")
    st.write(fruits['fruit_name'].unique())



col3, col4 = st.columns(2) 

with col3:

    # Display counts of each fruit
    st.subheader("Fruit Counts")
    fruit_counts = fruits.groupby('fruit_name').size()
    st.bar_chart(fruit_counts)

    # Display the KNN plot
    st.subheader("KNN Decision Boundary")
    st.image('knn.png')

with col4:
    st.subheader("Box Plot")
    st.image('fruits_boxfig.png')

    st.subheader("Scatter-Matrix")
    st.image('scatter.png')