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import numpy as np
import pandas as pd
import cv2
import torch
from tensorflow import keras
from PIL import Image
from transformers import ViTForImageClassification, ViTFeatureExtractor
from tensorflow.keras.models import load_model
import streamlit as st
st.title("Skin Cancer Classification App")
# Load TensorFlow models
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'),
}
# Load PyTorch ViT model
vit_model = ViTForImageClassification.from_pretrained('google/vit-base-patch16-224', num_labels=7)
vit_model.load_state_dict(torch.load('./vit_skin_cancer_model.pth'))
vit_model.eval() # Set the model to evaluation mode
# Add the PyTorch model to the models dictionary
models["ViT_Model"] = vit_model
# 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"])
true_file = pd.read_csv("HAM10000_metadata.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')
}
classes_map = {
'nv': 'melanocytic nevi',
'mel': 'melanoma',
'bkl': 'benign keratosis-like lesions',
'bcc': 'basal cell carcinoma',
'vasc': 'pyogenic granulomas and hemorrhage',
'akiec': 'Actinic keratoses and intraepithelial carcinomae',
'df': 'dermatofibroma'
}
if file is not None:
file_bytes = np.asarray(bytearray(file.read()), dtype=np.uint8)
opencv_image = cv2.imdecode(file_bytes, 1)
# Resize image for TensorFlow models
img1 = cv2.resize(opencv_image, (32, 32))
if model_name == "ViT_Model":
# PyTorch model inference
feature_extractor = ViTFeatureExtractor.from_pretrained('google/vit-base-patch16-224')
image = feature_extractor(images=opencv_image, return_tensors="pt")['pixel_values']
with torch.no_grad():
outputs = model(image)
class_ind = outputs.logits.argmax(-1).item()
class_name = classes[class_ind]
else:
# TensorFlow model inference
result = model.predict(img1.reshape(1, 32, 32, 3))
max_prob = max(result[0])
class_ind = list(result[0]).index(max_prob)
class_name = classes[class_ind]
# Display image and result
col1, col2 = st.columns(2)
with col1:
st.header("Input Image")
st.image(opencv_image, channels="BGR")
with col2:
st.header("Results")
if file:
name = file.name.split(".")[0]
if name in true_file['image_id'].values:
st.write("True Label: ", classes_map[true_file.loc[true_file['image_id']==name, 'dx'].iloc[0]])
st.write("Prediction:", class_name[1])
else:
st.write("No match")
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