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EDA.py

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+import os
+import streamlit as st
+import glob
+import pandas as pd
+import matplotlib.pyplot as plt
+
+def run_eda():
+    st.title('CNN Image Classifier')
+    
+    # Set directories
+    train_dir = "Data/train"
+
+    
+
+    # train sets
+    for files in os.listdir(train_dir):
+        print(os.path.join(train_dir,files))
+
+    num_A = len(glob.glob(os.path.join(train_dir, "normal", "*.png")))
+    num_ad = len(glob.glob(os.path.join(train_dir, "adenocarcinoma_left.lower.lobe_T2_N0_M0_Ib", "*.png")))
+    num_sq = len(glob.glob(os.path.join(train_dir, "squamous.cell.carcinoma_left.hilum_T1_N2_M0_IIIa", "*.png")))
+    num_lg = len(glob.glob(os.path.join(train_dir, "large.cell.carcinoma_left.hilum_T2_N2_M0_IIIa", "*.png")))
+
+
+    class_names = ['Normal', 'Adenocarcinoma', 'Squamous', 'Large Cell']
+    num_images = [num_A, num_ad, num_sq, num_lg]
+
+    fig, ax = plt.subplots()
+    ax.bar(class_names, num_images)
+    ax.set_title('Banyaknya Data Setiap Kelas pada Dataset Train')
+    ax.set_xlabel('Kelas')
+    ax.set_ylabel('Jumlah Data')
+    st.pyplot(fig)