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import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import streamlit as st
import plotly.express as px
import os
import tensorflow as tf
from PIL import Image
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import warnings
warnings.filterwarnings('ignore')
st.set_page_config(
page_title= 'Data Klasifikasi Beras',
layout= 'wide',
initial_sidebar_state= 'expanded'
)
def run():
# membuat title
st.title ('Klasifikasi Data Beras')
# buat sub header
st.subheader('Exploratory Data Analysis')
# membuat gambar
st.image('data:image/jpeg;base64,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')
# deskripsi
st.write('# Latar Belakang ')
st.write('''
Beras adalah salah satu makanan pokok di dunia, Namun menjadi salah satu paling penting khususnya Benua Asia, dikarenakan tempat beras menjadi makanan pokok untuk mayoritas penduduk(terutama di kalangan menengah kebawah masyarakat). Benua Asia sendiri menjadi tempat tinggal petani, dapat diperkirakan 90% produksi beras oleh petani beras.
Namun beras tidak memiliki satu tipe beras, melainkan berbagai macam tipe beras. Klasifikasi beras ternyata memiliki pengaruh pada setiap iklimnya masing - masing negara. Maka dari itu dibutuh model, atau deep learning computer vision untuk mempelajari jenis - jenis beras.
Pada Kasus ini akan menggunakan data beras yang terdiri dari lima kelas. Kelas masing - masing beras terdiri dari karacadag,ipsala,jasmine,arborio,dan basmati. Harapannya dengan pembentukan model, Hasil deployment dapat melakukan identifikasi beras tentunya menentukan kelima beras tersebut.''')
st.write('# Problem Statement')
st.write('''
Melakukan klasifikasi beras dan prediksi gambar dengan Computer Vision.''')
st.divider()
# mencoba menampilkan gambar
st.write('# Data Beras')
# fungsi untuk memanggil gambar
def load_images_from_folder(folder):
images = []
for filename in os.listdir(folder):
if filename.endswith(".jpg") or filename.endswith(".png") or filename.endswith(".jpeg"):
img = Image.open(os.path.join(folder, filename))
if img is not None:
images.append((filename, img))
return images
# fungsi memplot gambar untuk streamlit
def plot_images(base_folder_path):
# Path menuju "Training" subfolder
training_folder_path = os.path.join(base_folder_path, "training")
# mengambil subfolder dari training
subfolders = os.listdir(training_folder_path)
for subfolder in subfolders:
subfolder_path = os.path.join(training_folder_path, subfolder)
if not os.path.isdir(subfolder_path):
continue # lalui apabila bukan folder
st.write(f'Class: {subfolder}')
# Load gambar dari subfolder
images = load_images_from_folder(subfolder_path)
# memastikan kita hanya mengambil 5 gambar
images_to_display = images[:5]
fig = plt.figure(figsize=(20, 4)) # penyesuaian gambar
columns = 5
rows = 1
for index in range(1, columns * rows + 1):
if index > len(images_to_display):
break
filename, image = images_to_display[index - 1]
fig.add_subplot(rows, columns, index)
plt.imshow(image)
plt.axis("off")
plt.title(filename, fontsize=8)
st.pyplot(fig)
plt.close(fig) # menutupkan gambar untuk memori
# judul
st.write("### menunjukan kelas beras berbeda")
# lebih spesifik untuk folder
folder_path = r"Rice_Image_Dataset" # Main folder path memiliki "training"
# Plot images dari setiap subfolder didalam "training"
plot_images(folder_path)
st.write('''
**Informasi bentuk Gambar**
- ketiga gambar yang dipisahkan sementar dan dibentuk, masing2 memiliki bentuk yang sama yaitu panjang , dan lebar sebesar **224**. Hal ini akan menjadi acuan sebagai parameter untuk modeling.
**Informasi Dataset,Sampel,Kelas,dan tipe Beras**
- informasi yang diberikan menunjukan bahwwa akan melakukan tiga pembagian dataset yaitu : `Training`,`Validation`, dan `Test`.
- jumlah sample yang dibagi sebesar **3000**,**2000**, dan **1250**.
- memiliki lima kelas, terdiri dari : `arborio:0`,`basmati:1`,`ipsala:2`,`jasmine:3`,`karacadag:4`
''')
if __name__ == '__main__' :
run()