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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"Note: you may need to restart the kernel to use updated packages.\n"
]
}
],
"source": [
"pip install tensorflow"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
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"Note: you may need to restart the kernel to use updated packages.\n"
]
}
],
"source": [
"pip install flask"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
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"Requirement already satisfied: numpy in c:\\users\\sedef\\anaconda3\\lib\\site-packages (1.26.4)\n",
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]
}
],
"source": [
"pip install numpy"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
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]
}
],
"source": [
"pip install pillow"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
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"Note: you may need to restart the kernel to use updated packages.\n"
]
}
],
"source": [
"pip install streamlit"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING:absl:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n",
"2024-12-11 16:58:19.616 \n",
" \u001b[33m\u001b[1mWarning:\u001b[0m to view this Streamlit app on a browser, run it with the following\n",
" command:\n",
"\n",
" streamlit run C:\\Users\\Sedef\\AppData\\Roaming\\Python\\Python312\\site-packages\\ipykernel_launcher.py [ARGUMENTS]\n"
]
}
],
"source": [
"import streamlit as st\n",
"from tensorflow.keras.models import load_model\n",
"from PIL import Image\n",
"import numpy as np\n",
"\n",
"model = load_model('cnn_model_epoch_100.h5')\n",
"\n",
"def process_image(img):\n",
" img = img.resize((170, 170)) # Boyutu 170x170 piksel yaptık\n",
" img = np.array(img) / 255.0 # Normalize ettik\n",
" img = np.expand_dims(img, axis=0) # 0. ortada olsun diye sayfada\n",
"\n",
"st.title('Kanser Resmi sınıflandırma :cancer:')\n",
"img_file = st.file_uploader('Bir Resim Seç', type=['jpeg', 'png'])\n",
"\n",
"if img_file is not None:\n",
" img = Image.open(img_file)\n",
" st.image(img, caption='Yüklenen resim')\n",
" prediction = model.predict(img)\n",
"\n",
"st.title('Kanser Resmi Sınıflandırma :cancer:')\n",
"st.write('Resim seç ve model kanser olup olmadığını tahmin etsin.')\n",
"\n",
"file = st.file_uploader('Bir Resim Seç', type=['jpg', 'jpeg', 'png'])\n",
"\n",
"if file is not None:\n",
" img = Image.open(file)\n",
" st.image(img, caption='Yüklenen resim')\n",
" image = process_image(img)\n",
" prediction = model.predict(image)\n",
" predicted_class = np.argmax(prediction)\n",
"\n",
" class_names = ['Kanser Değil', 'Kanser']\n",
" st.write(class_names[predicted_class])\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "base",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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