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| import numpy as np | |
| import pandas as pd | |
| import tensorflow as tf | |
| from tensorflow import keras | |
| from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout, BatchNormalization, GlobalAveragePooling2D | |
| from tensorflow.keras.models import Model, load_model, Sequential | |
| from tensorflow.keras.optimizers import Adam | |
| from tensorflow.keras.metrics import Precision, Recall | |
| from tensorflow.keras.callbacks import EarlyStopping | |
| from sklearn.utils.class_weight import compute_class_weight | |
| from sklearn.model_selection import train_test_split | |
| from tensorflow.image import resize | |
| from google.colab.patches import cv2_imshow | |
| from tensorflow.keras.utils import to_categorical | |
| import matplotlib.pyplot as plt | |
| import warnings | |
| import warnings | |
| warnings.filterwarnings("ignore") | |
| # print ('modules loaded') | |
| import streamlit as st | |
| import pandas as pd | |
| import numpy as np | |
| from PIL import Image | |
| import tensorflow.keras as keras | |
| st.title("Skin Cancer Classification App") | |
| models = { | |
| "Le_Net": load_model('LeNet_5.h5'), | |
| "Simple_CNN": load_model('Simple CNN.h5'), | |
| "Alex_Net": load_model('AlexNet.h5'), | |
| "Deeper_CNN": load_model('Deeper CNN.h5') | |
| } | |
| # Allow user to select model | |
| model_name = st.selectbox("Choose a model", list(models.keys())) | |
| model = models[model_name] | |
| # Upload Image | |
| file = st.file_uploader("Upload an image", type=["png", "jpg", "jpeg"]) | |
| # file ='hmnist_28_28_RGB.csv' | |
| classes = {4: ('nv', ' melanocytic nevi'), 6: ('mel', 'melanoma'), | |
| 2 :('bkl', 'benign keratosis-like lesions'), 1:('bcc' , ' basal cell carcinoma'), | |
| 5: ('vasc', ' pyogenic granulomas and hemorrhage'), | |
| 0: ('akiec', 'Actinic keratoses and intraepithelial carcinomae'), | |
| 3: ('df', 'dermatofibroma')} | |
| if file is not None: | |
| img = cv2.imread(file) | |
| # cv2_imshow(img) | |
| img1 = cv2.resize(img, (28, 28)) | |
| result = model.predict(img1.reshape(1, 28, 28, 3)) | |
| max_prob = max(result[0]) | |
| class_ind = list(result[0]).index(max_prob) | |
| class_name = classes[class_ind] | |
| # print(class_name) | |
| # count+=1 | |
| # if count>10: | |
| # break | |
| # df = pd.read_csv(file) | |
| # # Get first row | |
| # img_reshaped = image_resize(df) | |
| # # Get prediction | |
| # pred = model.predict(img_reshaped) | |
| # label = np.argmax(pred) | |
| # Display image and result | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| st.header("Input Image") | |
| st.image(img) | |
| with col2: | |
| st.header("Prediction") | |
| st.write(class_name) | |
| st.metric("Category:", class_name) | |
| # from google.colab.patches import cv2_imshow | |
| # srcdir = '/kaggle/input/skin-cancer-mnist-ham10000/HAM10000_images_part_1' | |
| # count=0 | |
| # for temp in os.listdir(srcdir): | |
| # img = cv2.imread(os.path.join(srcdir, temp)) | |
| # cv2.imwrite(temp, img) | |
| # cv2_imshow(img) | |
| # img = cv2.resize(img, (28, 28)) | |
| # result = model.predict(img.reshape(1, 28, 28, 3)) | |
| # max_prob = max(result[0]) | |
| # class_ind = list(result[0]).index(max_prob) | |
| # class_name = classes[class_ind] | |
| # print(class_name) | |
| # count+=1 | |
| # if count>10: | |
| # break | |