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Breast_Cancer_prediction_using_Ultrasound_images
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  1. app.py +52 -0
  2. breast_cancer_detection_model3.h5 +3 -0
  3. cancer_detection_final.ipynb +826 -0
  4. requirements.txt.txt +1 -0
app.py ADDED
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+ import gradio as gr
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+ import numpy as np
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+ import cv2 # Ensure you have opencv-python installed
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+ from tensorflow.keras.models import load_model # Ensure you have TensorFlow installed
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+
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+ # Load your trained model
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+ model = load_model(r"F:\pe udemy\PROJECTS\incomplete\cancer\breast_cancer_detection_model3.h5") # Update this path to your actual model file
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+
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+ # Define class names according to your model
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+ class_names = ['benign', 'malignant', 'normal'] # Update this list if different
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+
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+ # Define the prediction function
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+ def predict_cancer(images):
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+ results = []
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+ for img in images:
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+ # Convert image to grayscale (if it's not already), resize, and normalize
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+ img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) # Convert to grayscale if not already
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+ img = cv2.resize(img, (IMG_SIZE, IMG_SIZE)) # Resize to match model input
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+ img = np.expand_dims(img, axis=-1) # Add channel dimension
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+ img = img / 255.0 # Normalize
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+ img = np.expand_dims(img, axis=0) # Add batch dimension
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+
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+ # Make prediction
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+ prediction = model.predict(img)
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+ class_idx = np.argmax(prediction[0])
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+ class_name = class_names[class_idx]
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+ probability = np.max(prediction[0])
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+ results.append(f"{class_name} (Probability: {probability:.2f})")
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+
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+ return results
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+
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+ # Define Gradio interface
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+ def classify_images(images):
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+ if not isinstance(images, list): # Ensure `images` is a list of images
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+ images = [images]
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+ return predict_cancer(images)
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+
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+ # Define the Gradio interface
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+ input_images = gr.Image(type='numpy', label='Upload Ultrasound Images')
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+ output_labels = gr.Textbox(label='Predictions')
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+
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+ gr_interface = gr.Interface(
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+ fn=classify_images,
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+ inputs=input_images,
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+ outputs=output_labels,
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+ title="Breast Cancer Detection from Ultrasound Images",
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+ description="Upload multiple breast ultrasound images to get predictions on whether they show benign, malignant, or normal conditions."
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+ )
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+
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+ # Launch the Gradio app
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+ if __name__ == "__main__":
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+ gr_interface.launch()
breast_cancer_detection_model3.h5 ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:52cadf570c1291903515c9b2a825ac38781889f9164bbb4e3209892243dd7ba5
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+ size 39706832
cancer_detection_final.ipynb ADDED
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+ {
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+ "nbformat": 4,
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+ "nbformat_minor": 0,
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+ "metadata": {
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+ "colab": {
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+ "provenance": [],
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+ "gpuType": "T4"
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+ },
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+ "kernelspec": {
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+ "name": "python3",
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+ "display_name": "Python 3"
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+ },
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+ "language_info": {
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+ "name": "python"
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+ },
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+ "accelerator": "GPU"
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+ },
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+ "cells": [
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+ {
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+ "cell_type": "code",
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+ "execution_count": 1,
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+ "metadata": {
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+ "colab": {
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+ "base_uri": "https://localhost:8080/"
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+ },
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+ "id": "WSUey7APElVD",
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+ "outputId": "105d40d0-6b44-4751-983f-a10c61aced01"
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+ },
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+ "outputs": [
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+ {
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+ "output_type": "stream",
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+ "name": "stdout",
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+ "text": [
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+ "Mounted at /content/drive\n"
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+ ]
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+ }
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+ ],
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+ "source": [
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+ "from google.colab import drive\n",
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+ "drive.mount('/content/drive')"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "source": [
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+ "import zipfile\n",
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+ "import os\n",
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+ "\n",
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+ "# Define the path to the zip file\n",
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+ "zip_file_path = r\"/content/drive/MyDrive/new image set.zip\"\n",
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+ "# Define the directory where you want to extract the files\n",
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+ "extract_to_path = '/content/sample_data'\n",
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+ "\n",
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+ "# Create the directory if it doesn't exist\n",
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+ "os.makedirs(extract_to_path, exist_ok=True)\n",
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+ "\n",
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+ "# Extract the zip file\n",
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+ "with zipfile.ZipFile(zip_file_path, 'r') as zip_ref:\n",
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+ " zip_ref.extractall(extract_to_path)\n",
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+ "\n",
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+ "print(f'Files extracted to {extract_to_path}')\n"
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+ ],
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+ "metadata": {
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+ "colab": {
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+ "base_uri": "https://localhost:8080/"
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+ },
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+ "id": "wFFWMZf0EpgC",
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+ "outputId": "645ab679-03eb-4f43-9140-5b49486a5f44"
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+ },
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+ "execution_count": 2,
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+ "outputs": [
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+ {
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+ "output_type": "stream",
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+ "name": "stdout",
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+ "text": [
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+ "Files extracted to /content/sample_data\n"
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+ ]
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+ }
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "source": [],
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+ "metadata": {
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+ "id": "7mBWvK5oLUyw"
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+ },
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+ "execution_count": null,
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+ "outputs": []
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+ },
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+ {
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+ "cell_type": "code",
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+ "source": [
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+ "from tensorflow.keras.utils import to_categorical\n",
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+ "\n",
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+ "# Convert labels to one-hot encoded format\n",
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+ "y_train = to_categorical(y_train, num_classes=3)\n",
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+ "y_test = to_categorical(y_test, num_classes=3)\n"
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+ ],
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+ "metadata": {
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+ "id": "fujzpI9DMdS8"
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+ },
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+ "execution_count": 11,
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+ "outputs": []
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+ },
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+ {
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+ "cell_type": "code",
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+ "source": [
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+ "!pip install gradio\n",
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+ "\n"
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+ ],
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+ "metadata": {
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+ "colab": {
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+ "base_uri": "https://localhost:8080/"
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+ },
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+ "id": "aNBOVK_clL5-",
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+ "outputId": "2de76cb6-e5ef-47df-be71-f4af733edbde"
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+ },
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+ "execution_count": 6,
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+ "outputs": [
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+ {
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+ "output_type": "stream",
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+ "name": "stdout",
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+ "text": [
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+ "Collecting gradio\n",
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+ " Using cached gradio-4.37.2-py3-none-any.whl (12.3 MB)\n",
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+ "Collecting aiofiles<24.0,>=22.0 (from gradio)\n",
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+ " Downloading aiofiles-23.2.1-py3-none-any.whl (15 kB)\n",
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+ ]
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+ }
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+ ]
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+ },
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+ {
251
+ "cell_type": "code",
252
+ "source": [
253
+ "import os\n",
254
+ "import cv2\n",
255
+ "import numpy as np\n",
256
+ "from sklearn.model_selection import train_test_split\n",
257
+ "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
258
+ "from tensorflow.keras.models import Sequential\n",
259
+ "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout\n",
260
+ "import gradio as gr\n",
261
+ "import tensorflow as tf\n",
262
+ "from tensorflow.keras.utils import to_categorical\n",
263
+ "\n",
264
+ "# Define paths\n",
265
+ "dataset_folder = r\"/content/sample_data/Dataset_BUSI_with_GT\" # Path to the images folder\n",
266
+ "\n",
267
+ "# Define image size and class labels\n",
268
+ "IMG_SIZE = 128 # Image size for the model input\n",
269
+ "class_names = ['benign', 'malignant', 'normal']\n",
270
+ "class_mapping = {name: idx for idx, name in enumerate(class_names)}\n",
271
+ "\n",
272
+ "# Load images from each class folder\n",
273
+ "def load_images_from_folder(folder, class_label):\n",
274
+ " images = []\n",
275
+ " labels = []\n",
276
+ " for filename in os.listdir(folder):\n",
277
+ " img_path = os.path.join(folder, filename)\n",
278
+ " img = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE) # Load in grayscale\n",
279
+ " if img is not None:\n",
280
+ " img = cv2.resize(img, (IMG_SIZE, IMG_SIZE)) # Resize image\n",
281
+ " img = np.expand_dims(img, axis=-1) # Add channel dimension\n",
282
+ " images.append(img)\n",
283
+ " labels.append(class_label)\n",
284
+ " return images, labels\n",
285
+ "\n",
286
+ "images = []\n",
287
+ "labels = []\n",
288
+ "\n",
289
+ "# Load images from each class folder\n",
290
+ "for class_name, class_label in class_mapping.items():\n",
291
+ " folder_path = os.path.join(dataset_folder, class_name)\n",
292
+ " imgs, lbls = load_images_from_folder(folder_path, class_label)\n",
293
+ " images.extend(imgs)\n",
294
+ " labels.extend(lbls)\n",
295
+ "\n",
296
+ "images = np.array(images)\n",
297
+ "labels = np.array(labels)\n",
298
+ "\n",
299
+ "# Convert labels to one-hot encoded format\n",
300
+ "y_train = to_categorical(labels, num_classes=3)\n",
301
+ "\n",
302
+ "# Split the dataset\n",
303
+ "X_train, X_test, y_train, y_test = train_test_split(images, y_train, test_size=0.2, random_state=42)\n",
304
+ "\n",
305
+ "# Data augmentation for training\n",
306
+ "train_datagen = ImageDataGenerator(\n",
307
+ " rescale=1./255,\n",
308
+ " shear_range=0.2,\n",
309
+ " zoom_range=0.2,\n",
310
+ " horizontal_flip=True\n",
311
+ ")\n",
312
+ "\n",
313
+ "test_datagen = ImageDataGenerator(rescale=1./255)\n",
314
+ "\n",
315
+ "# Create Data Generators for training and testing\n",
316
+ "train_generator = train_datagen.flow(X_train, y_train, batch_size=32)\n",
317
+ "test_generator = test_datagen.flow(X_test, y_test, batch_size=32)\n",
318
+ "\n",
319
+ "# Define the CNN model\n",
320
+ "model = Sequential([\n",
321
+ " Conv2D(32, (3, 3), activation='relu', input_shape=(IMG_SIZE, IMG_SIZE, 1)), # Input is grayscale, hence single channel\n",
322
+ " MaxPooling2D((2, 2)),\n",
323
+ " Conv2D(64, (3, 3), activation='relu'),\n",
324
+ " MaxPooling2D((2, 2)),\n",
325
+ " Conv2D(128, (3, 3), activation='relu'),\n",
326
+ " MaxPooling2D((2, 2)),\n",
327
+ " Flatten(),\n",
328
+ " Dense(128, activation='relu'),\n",
329
+ " Dropout(0.5),\n",
330
+ " Dense(3, activation='softmax') # Three classes: Benign, Malignant, Normal\n",
331
+ "])\n",
332
+ "\n",
333
+ "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
334
+ "model.summary()\n",
335
+ "\n",
336
+ "# Train the model\n",
337
+ "history = model.fit(\n",
338
+ " train_generator,\n",
339
+ " validation_data=test_generator,\n",
340
+ " epochs=25\n",
341
+ ")\n",
342
+ "\n",
343
+ "# Save the model\n",
344
+ "model.save('breast_cancer_detection_model3.h5')\n",
345
+ "\n",
346
+ "# Define the prediction function\n",
347
+ "def predict_cancer(images):\n",
348
+ " results = []\n",
349
+ " for img in images:\n",
350
+ " # Ensure image is in RGB format and convert to grayscale\n",
351
+ " img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) # Convert to grayscale\n",
352
+ " img = cv2.resize(img, (IMG_SIZE, IMG_SIZE)) # Resize to match model input\n",
353
+ " img = np.expand_dims(img, axis=-1) # Add channel dimension\n",
354
+ " img = img / 255.0 # Normalize\n",
355
+ " img = np.expand_dims(img, axis=0) # Add batch dimension\n",
356
+ "\n",
357
+ " # Make prediction\n",
358
+ " prediction = model.predict(img)\n",
359
+ " class_idx = np.argmax(prediction[0])\n",
360
+ " class_name = class_names[class_idx]\n",
361
+ " probability = np.max(prediction[0])\n",
362
+ " results.append(f\"{class_name} (Probability: {probability:.2f})\")\n",
363
+ "\n",
364
+ " return results\n",
365
+ "\n",
366
+ "# Define Gradio interface\n",
367
+ "input_images = gr.Image(type='numpy', label='Upload Ultrasound Images', tool='editor', source='upload', shape=(IMG_SIZE, IMG_SIZE))\n",
368
+ "output_labels = gr.Textbox(label='Predictions')\n",
369
+ "\n",
370
+ "gr_interface = gr.Interface(\n",
371
+ " fn=predict_cancer,\n",
372
+ " inputs=input_images,\n",
373
+ " outputs=output_labels,\n",
374
+ " title=\"Breast Cancer Detection from Ultrasound Images\",\n",
375
+ " description=\"Upload multiple breast ultrasound images to get predictions on whether they show benign, malignant, or normal conditions.\"\n",
376
+ ")\n",
377
+ "\n",
378
+ "# Launch the Gradio app\n",
379
+ "if __name__ == \"__main__\":\n",
380
+ " gr_interface.launch()\n"
381
+ ],
382
+ "metadata": {
383
+ "colab": {
384
+ "base_uri": "https://localhost:8080/",
385
+ "height": 1000
386
+ },
387
+ "id": "14__binmQtHa",
388
+ "outputId": "f3e5b69d-c896-4a28-cc83-38f8c3463144"
389
+ },
390
+ "execution_count": 7,
391
+ "outputs": [
392
+ {
393
+ "output_type": "stream",
394
+ "name": "stdout",
395
+ "text": [
396
+ "Model: \"sequential\"\n",
397
+ "_________________________________________________________________\n",
398
+ " Layer (type) Output Shape Param # \n",
399
+ "=================================================================\n",
400
+ " conv2d (Conv2D) (None, 126, 126, 32) 320 \n",
401
+ " \n",
402
+ " max_pooling2d (MaxPooling2 (None, 63, 63, 32) 0 \n",
403
+ " D) \n",
404
+ " \n",
405
+ " conv2d_1 (Conv2D) (None, 61, 61, 64) 18496 \n",
406
+ " \n",
407
+ " max_pooling2d_1 (MaxPoolin (None, 30, 30, 64) 0 \n",
408
+ " g2D) \n",
409
+ " \n",
410
+ " conv2d_2 (Conv2D) (None, 28, 28, 128) 73856 \n",
411
+ " \n",
412
+ " max_pooling2d_2 (MaxPoolin (None, 14, 14, 128) 0 \n",
413
+ " g2D) \n",
414
+ " \n",
415
+ " flatten (Flatten) (None, 25088) 0 \n",
416
+ " \n",
417
+ " dense (Dense) (None, 128) 3211392 \n",
418
+ " \n",
419
+ " dropout (Dropout) (None, 128) 0 \n",
420
+ " \n",
421
+ " dense_1 (Dense) (None, 3) 387 \n",
422
+ " \n",
423
+ "=================================================================\n",
424
+ "Total params: 3304451 (12.61 MB)\n",
425
+ "Trainable params: 3304451 (12.61 MB)\n",
426
+ "Non-trainable params: 0 (0.00 Byte)\n",
427
+ "_________________________________________________________________\n",
428
+ "Epoch 1/25\n",
429
+ "40/40 [==============================] - 9s 64ms/step - loss: 0.9351 - accuracy: 0.5293 - val_loss: 0.7623 - val_accuracy: 0.6171\n",
430
+ "Epoch 2/25\n",
431
+ "40/40 [==============================] - 2s 49ms/step - loss: 0.7880 - accuracy: 0.6490 - val_loss: 0.6971 - val_accuracy: 0.7120\n",
432
+ "Epoch 3/25\n",
433
+ "40/40 [==============================] - 3s 63ms/step - loss: 0.7158 - accuracy: 0.6609 - val_loss: 0.6900 - val_accuracy: 0.7405\n",
434
+ "Epoch 4/25\n",
435
+ "40/40 [==============================] - 3s 74ms/step - loss: 0.6818 - accuracy: 0.6862 - val_loss: 0.6745 - val_accuracy: 0.7089\n",
436
+ "Epoch 5/25\n",
437
+ "40/40 [==============================] - 2s 49ms/step - loss: 0.6607 - accuracy: 0.6981 - val_loss: 0.6446 - val_accuracy: 0.7563\n",
438
+ "Epoch 6/25\n",
439
+ "40/40 [==============================] - 2s 48ms/step - loss: 0.6371 - accuracy: 0.7084 - val_loss: 0.6247 - val_accuracy: 0.7500\n",
440
+ "Epoch 7/25\n",
441
+ "40/40 [==============================] - 2s 49ms/step - loss: 0.5992 - accuracy: 0.7314 - val_loss: 0.6092 - val_accuracy: 0.7532\n",
442
+ "Epoch 8/25\n",
443
+ "40/40 [==============================] - 2s 48ms/step - loss: 0.5733 - accuracy: 0.7472 - val_loss: 0.5671 - val_accuracy: 0.7911\n",
444
+ "Epoch 9/25\n",
445
+ "40/40 [==============================] - 3s 68ms/step - loss: 0.5612 - accuracy: 0.7417 - val_loss: 0.5484 - val_accuracy: 0.7816\n",
446
+ "Epoch 10/25\n",
447
+ "40/40 [==============================] - 3s 71ms/step - loss: 0.5460 - accuracy: 0.7567 - val_loss: 0.5713 - val_accuracy: 0.7184\n",
448
+ "Epoch 11/25\n",
449
+ "40/40 [==============================] - 2s 48ms/step - loss: 0.5267 - accuracy: 0.7512 - val_loss: 0.5167 - val_accuracy: 0.8133\n",
450
+ "Epoch 12/25\n",
451
+ "40/40 [==============================] - 2s 50ms/step - loss: 0.4947 - accuracy: 0.7876 - val_loss: 0.5295 - val_accuracy: 0.7785\n",
452
+ "Epoch 13/25\n",
453
+ "40/40 [==============================] - 2s 49ms/step - loss: 0.4822 - accuracy: 0.7900 - val_loss: 0.4824 - val_accuracy: 0.7943\n",
454
+ "Epoch 14/25\n",
455
+ "40/40 [==============================] - 3s 65ms/step - loss: 0.4577 - accuracy: 0.8043 - val_loss: 0.5469 - val_accuracy: 0.7532\n",
456
+ "Epoch 15/25\n",
457
+ "40/40 [==============================] - 2s 49ms/step - loss: 0.4479 - accuracy: 0.8154 - val_loss: 0.4808 - val_accuracy: 0.8133\n",
458
+ "Epoch 16/25\n",
459
+ "40/40 [==============================] - 2s 49ms/step - loss: 0.4360 - accuracy: 0.8035 - val_loss: 0.6171 - val_accuracy: 0.7152\n",
460
+ "Epoch 17/25\n",
461
+ "40/40 [==============================] - 2s 47ms/step - loss: 0.4315 - accuracy: 0.8138 - val_loss: 0.6265 - val_accuracy: 0.7152\n",
462
+ "Epoch 18/25\n",
463
+ "40/40 [==============================] - 2s 48ms/step - loss: 0.4108 - accuracy: 0.8344 - val_loss: 0.6563 - val_accuracy: 0.7437\n",
464
+ "Epoch 19/25\n",
465
+ "40/40 [==============================] - 3s 76ms/step - loss: 0.4032 - accuracy: 0.8225 - val_loss: 0.4995 - val_accuracy: 0.7658\n",
466
+ "Epoch 20/25\n",
467
+ "40/40 [==============================] - 2s 48ms/step - loss: 0.3779 - accuracy: 0.8463 - val_loss: 0.4580 - val_accuracy: 0.8165\n",
468
+ "Epoch 21/25\n",
469
+ "40/40 [==============================] - 2s 47ms/step - loss: 0.3660 - accuracy: 0.8447 - val_loss: 0.4400 - val_accuracy: 0.8101\n",
470
+ "Epoch 22/25\n",
471
+ "40/40 [==============================] - 2s 48ms/step - loss: 0.3883 - accuracy: 0.8447 - val_loss: 0.4386 - val_accuracy: 0.8133\n",
472
+ "Epoch 23/25\n",
473
+ "40/40 [==============================] - 3s 71ms/step - loss: 0.3440 - accuracy: 0.8661 - val_loss: 0.4602 - val_accuracy: 0.8038\n",
474
+ "Epoch 24/25\n",
475
+ "40/40 [==============================] - 3s 66ms/step - loss: 0.3326 - accuracy: 0.8605 - val_loss: 0.4593 - val_accuracy: 0.7880\n",
476
+ "Epoch 25/25\n",
477
+ "40/40 [==============================] - 2s 48ms/step - loss: 0.3125 - accuracy: 0.8621 - val_loss: 0.4492 - val_accuracy: 0.8070\n"
478
+ ]
479
+ },
480
+ {
481
+ "output_type": "stream",
482
+ "name": "stderr",
483
+ "text": [
484
+ "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
485
+ " saving_api.save_model(\n"
486
+ ]
487
+ },
488
+ {
489
+ "output_type": "error",
490
+ "ename": "TypeError",
491
+ "evalue": "Image.__init__() got an unexpected keyword argument 'tool'",
492
+ "traceback": [
493
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
494
+ "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
495
+ "\u001b[0;32m<ipython-input-7-5cde0dc5990a>\u001b[0m in \u001b[0;36m<cell line: 115>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 113\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 114\u001b[0m \u001b[0;31m# Define Gradio interface\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 115\u001b[0;31m \u001b[0minput_images\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mImage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'numpy'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Upload Ultrasound Images'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtool\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'editor'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msource\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'upload'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mIMG_SIZE\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIMG_SIZE\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 116\u001b[0m \u001b[0moutput_labels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTextbox\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Predictions'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 117\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
496
+ "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/gradio/component_meta.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 161\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 162\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 163\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 164\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 165\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
497
+ "\u001b[0;31mTypeError\u001b[0m: Image.__init__() got an unexpected keyword argument 'tool'"
498
+ ]
499
+ }
500
+ ]
501
+ },
502
+ {
503
+ "cell_type": "code",
504
+ "source": [
505
+ "# # Save the model\n",
506
+ "# model.save('breast_cancer_detection_model2.h5')"
507
+ ],
508
+ "metadata": {
509
+ "id": "fBLBREI_UHr-"
510
+ },
511
+ "execution_count": 13,
512
+ "outputs": []
513
+ },
514
+ {
515
+ "cell_type": "code",
516
+ "source": [
517
+ "!pip install gradio tensorflow opencv-python-headless\n"
518
+ ],
519
+ "metadata": {
520
+ "colab": {
521
+ "base_uri": "https://localhost:8080/"
522
+ },
523
+ "id": "9tkj3oGeUUyd",
524
+ "outputId": "f3a7a087-06d1-4179-9ff8-feea038d6a15"
525
+ },
526
+ "execution_count": 15,
527
+ "outputs": [
528
+ {
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+ "output_type": "stream",
530
+ "name": "stdout",
531
+ "text": [
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+ "Requirement already satisfied: gradio in /usr/local/lib/python3.10/dist-packages (4.37.2)\n",
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+ "Requirement already satisfied: opencv-python-headless in /usr/local/lib/python3.10/dist-packages (4.10.0.84)\n",
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+ "Requirement already satisfied: referencing>=0.28.4 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=3.0->altair<6.0,>=4.2.0->gradio) (0.35.1)\n",
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+ "Requirement already satisfied: rpds-py>=0.7.1 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=3.0->altair<6.0,>=4.2.0->gradio) (0.18.1)\n",
627
+ "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.19.3->gradio) (3.3.2)\n",
628
+ "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from rich>=10.11.0->typer<1.0,>=0.12->gradio) (3.0.0)\n",
629
+ "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from rich>=10.11.0->typer<1.0,>=0.12->gradio) (2.16.1)\n",
630
+ "Requirement already satisfied: exceptiongroup in /usr/local/lib/python3.10/dist-packages (from anyio->httpx>=0.24.1->gradio) (1.2.1)\n",
631
+ "Requirement already satisfied: httptools>=0.5.0 in /usr/local/lib/python3.10/dist-packages (from uvicorn>=0.14.0->gradio) (0.6.1)\n",
632
+ "Requirement already satisfied: python-dotenv>=0.13 in /usr/local/lib/python3.10/dist-packages (from uvicorn>=0.14.0->gradio) (1.0.1)\n",
633
+ "Requirement already satisfied: uvloop!=0.15.0,!=0.15.1,>=0.14.0 in /usr/local/lib/python3.10/dist-packages (from uvicorn>=0.14.0->gradio) (0.19.0)\n",
634
+ "Requirement already satisfied: watchfiles>=0.13 in /usr/local/lib/python3.10/dist-packages (from uvicorn>=0.14.0->gradio) (0.22.0)\n",
635
+ "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich>=10.11.0->typer<1.0,>=0.12->gradio) (0.1.2)\n",
636
+ "Requirement already satisfied: pyasn1<0.7.0,>=0.4.6 in /usr/local/lib/python3.10/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow) (0.6.0)\n",
637
+ "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<2,>=0.5->tensorboard<2.16,>=2.15->tensorflow) (3.2.2)\n"
638
+ ]
639
+ }
640
+ ]
641
+ },
642
+ {
643
+ "cell_type": "code",
644
+ "source": [
645
+ "# Define the prediction function\n",
646
+ "def predict_cancer(images):\n",
647
+ " results = []\n",
648
+ " for img in images:\n",
649
+ " # Convert image to grayscale (if it's not already), resize, and normalize\n",
650
+ " img = cv2.resize(img, (IMG_SIZE, IMG_SIZE)) # Resize to match model input\n",
651
+ " img = np.expand_dims(img, axis=-1) # Add channel dimension\n",
652
+ " img = img / 255.0 # Normalize\n",
653
+ " img = np.expand_dims(img, axis=0) # Add batch dimension\n",
654
+ "\n",
655
+ " # Make prediction\n",
656
+ " prediction = model.predict(img)\n",
657
+ " class_idx = np.argmax(prediction[0])\n",
658
+ " class_name = class_names[class_idx]\n",
659
+ " probability = np.max(prediction[0])\n",
660
+ " results.append(f\"{class_name} (Probability: {probability:.2f})\")\n",
661
+ "\n",
662
+ " return results\n",
663
+ "\n",
664
+ "# Define Gradio interface\n",
665
+ "def classify_images(images):\n",
666
+ " return predict_cancer(images)\n",
667
+ "\n",
668
+ "# # Update Gradio interface configuration\n",
669
+ "# input_images = gr.Image(type='numpy', label='Upload Ultrasound Images', source='upload', tool=None) # Removed `tool` argument and added `source='upload'`\n",
670
+ "# output_labels = gr.Textbox(label='Predictions')\n",
671
+ "\n",
672
+ "# Define Gradio interface\n",
673
+ "input_images = gr.Image(type='numpy', label='Upload Ultrasound Images')\n",
674
+ "output_labels = gr.Textbox(label='Predictions')\n",
675
+ "\n",
676
+ "gr_interface = gr.Interface(\n",
677
+ " fn=classify_images,\n",
678
+ " inputs=input_images,\n",
679
+ " outputs=output_labels,\n",
680
+ " title=\"Breast Cancer Detection from Ultrasound Images\",\n",
681
+ " description=\"Upload multiple breast ultrasound images to get predictions on whether they show benign, malignant, or normal conditions.\"\n",
682
+ ")\n",
683
+ "\n",
684
+ "# Launch the Gradio app\n",
685
+ "if __name__ == \"__main__\":\n",
686
+ " gr_interface.launch()"
687
+ ],
688
+ "metadata": {
689
+ "colab": {
690
+ "base_uri": "https://localhost:8080/",
691
+ "height": 646
692
+ },
693
+ "id": "u965ysX0UkCB",
694
+ "outputId": "e83b327a-1078-49cb-9583-e112dbab6f09"
695
+ },
696
+ "execution_count": 8,
697
+ "outputs": [
698
+ {
699
+ "output_type": "stream",
700
+ "name": "stdout",
701
+ "text": [
702
+ "Setting queue=True in a Colab notebook requires sharing enabled. Setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n",
703
+ "\n",
704
+ "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
705
+ "Running on public URL: https://fd6a25a058bd67fbe1.gradio.live\n",
706
+ "\n",
707
+ "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
708
+ ]
709
+ },
710
+ {
711
+ "output_type": "display_data",
712
+ "data": {
713
+ "text/plain": [
714
+ "<IPython.core.display.HTML object>"
715
+ ],
716
+ "text/html": [
717
+ "<div><iframe src=\"https://fd6a25a058bd67fbe1.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
718
+ ]
719
+ },
720
+ "metadata": {}
721
+ }
722
+ ]
723
+ },
724
+ {
725
+ "cell_type": "code",
726
+ "source": [
727
+ "import gradio as gr\n",
728
+ "import numpy as np\n",
729
+ "import cv2 # Ensure you have opencv-python installed\n",
730
+ "from tensorflow.keras.models import load_model # Ensure you have TensorFlow installed\n",
731
+ "\n",
732
+ "# Load your trained model\n",
733
+ "model = load_model('/content/breast_cancer_detection_model3.h5') # Update this path to your actual model file\n",
734
+ "\n",
735
+ "# Define class names according to your model\n",
736
+ "class_names = ['benign', 'malignant', 'normal'] # Update this list if different\n",
737
+ "\n",
738
+ "# Define the prediction function\n",
739
+ "def predict_cancer(images):\n",
740
+ " results = []\n",
741
+ " for img in images:\n",
742
+ " # Convert image to grayscale (if it's not already), resize, and normalize\n",
743
+ " img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) # Convert to grayscale if not already\n",
744
+ " img = cv2.resize(img, (IMG_SIZE, IMG_SIZE)) # Resize to match model input\n",
745
+ " img = np.expand_dims(img, axis=-1) # Add channel dimension\n",
746
+ " img = img / 255.0 # Normalize\n",
747
+ " img = np.expand_dims(img, axis=0) # Add batch dimension\n",
748
+ "\n",
749
+ " # Make prediction\n",
750
+ " prediction = model.predict(img)\n",
751
+ " class_idx = np.argmax(prediction[0])\n",
752
+ " class_name = class_names[class_idx]\n",
753
+ " probability = np.max(prediction[0])\n",
754
+ " results.append(f\"{class_name} (Probability: {probability:.2f})\")\n",
755
+ "\n",
756
+ " return results\n",
757
+ "\n",
758
+ "# Define Gradio interface\n",
759
+ "def classify_images(images):\n",
760
+ " if not isinstance(images, list): # Ensure `images` is a list of images\n",
761
+ " images = [images]\n",
762
+ " return predict_cancer(images)\n",
763
+ "\n",
764
+ "# Define the Gradio interface\n",
765
+ "input_images = gr.Image(type='numpy', label='Upload Ultrasound Images')\n",
766
+ "output_labels = gr.Textbox(label='Predictions')\n",
767
+ "\n",
768
+ "gr_interface = gr.Interface(\n",
769
+ " fn=classify_images,\n",
770
+ " inputs=input_images,\n",
771
+ " outputs=output_labels,\n",
772
+ " title=\"Breast Cancer Detection from Ultrasound Images\",\n",
773
+ " description=\"Upload multiple breast ultrasound images to get predictions on whether they show benign, malignant, or normal conditions.\"\n",
774
+ ")\n",
775
+ "\n",
776
+ "# Launch the Gradio app\n",
777
+ "if __name__ == \"__main__\":\n",
778
+ " gr_interface.launch()\n"
779
+ ],
780
+ "metadata": {
781
+ "colab": {
782
+ "base_uri": "https://localhost:8080/",
783
+ "height": 646
784
+ },
785
+ "id": "uZYTW1LPW3pg",
786
+ "outputId": "107a6939-64f1-4e3c-cc13-e3af7d7c80c1"
787
+ },
788
+ "execution_count": 9,
789
+ "outputs": [
790
+ {
791
+ "output_type": "stream",
792
+ "name": "stdout",
793
+ "text": [
794
+ "Setting queue=True in a Colab notebook requires sharing enabled. Setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n",
795
+ "\n",
796
+ "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
797
+ "Running on public URL: https://134538bfc74bbc1b4e.gradio.live\n",
798
+ "\n",
799
+ "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
800
+ ]
801
+ },
802
+ {
803
+ "output_type": "display_data",
804
+ "data": {
805
+ "text/plain": [
806
+ "<IPython.core.display.HTML object>"
807
+ ],
808
+ "text/html": [
809
+ "<div><iframe src=\"https://134538bfc74bbc1b4e.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
810
+ ]
811
+ },
812
+ "metadata": {}
813
+ }
814
+ ]
815
+ },
816
+ {
817
+ "cell_type": "code",
818
+ "source": [],
819
+ "metadata": {
820
+ "id": "C7XW8IqPVwzj"
821
+ },
822
+ "execution_count": null,
823
+ "outputs": []
824
+ }
825
+ ]
826
+ }
requirements.txt.txt ADDED
@@ -0,0 +1 @@
 
 
1
+ gradio