Duplicate from prafullcodes/PrafullInterfaces

Co-authored-by: Prafull Mangla <prafullcodes@users.noreply.huggingface.co>

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README.md

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+---
+title: PrafullInterfaces
+emoji: 📉
+colorFrom: indigo
+colorTo: purple
+sdk: gradio
+sdk_version: 3.28.0
+app_file: app.py
+pinned: false
+license: openrail
+duplicated_from: prafullcodes/PrafullInterfaces
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+# -*- coding: utf-8 -*-
+"""lung cancerdetection.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1f7VybSnYLPbUVLRLMNQboxQkCYaBCXMs
+"""
+
+
+# This Python 3 environment comes with many helpful analytics libraries installed
+# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
+# For example, here's several helpful packages to load
+
+import numpy as np # linear algebra
+import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
+
+# Input data files are available in the read-only "../input/" directory
+# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory
+
+import os
+
+
+# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All" 
+# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session
+
+# importing libraries
+
+import tensorflow as tf
+from tensorflow.keras.layers import Input, Lambda, Dense, Flatten
+from tensorflow.keras.models import Model
+from tensorflow.keras.applications.resnet50 import ResNet50
+from tensorflow.keras.applications.resnet50 import preprocess_input
+from tensorflow.keras.preprocessing import image
+from tensorflow.keras.preprocessing.image import ImageDataGenerator,load_img
+from tensorflow.keras.models import Sequential
+import numpy as np
+from glob import glob
+import matplotlib.pyplot as plt
+
+image_set =  "./lung_image_sets"
+
+SIZE_X = SIZE_Y = 224
+
+datagen = tf.keras.preprocessing.image.ImageDataGenerator(validation_split = 0.2)
+
+train_set = datagen.flow_from_directory(image_set,
+                                       class_mode = "categorical",
+                                       target_size = (SIZE_X,SIZE_Y),
+                                       color_mode="rgb",
+                                       batch_size = 128, 
+                                       shuffle = False,
+                                       subset='training',
+                                       seed = 42)
+
+validate_set = datagen.flow_from_directory(image_set,
+                                       class_mode = "categorical",
+                                       target_size = (SIZE_X, SIZE_Y),
+                                       color_mode="rgb",
+                                       batch_size = 128, 
+                                       shuffle = False,
+                                       subset='validation',
+                                       seed = 42)
+
+
+IMAGE_SIZE = [224, 224]
+
+resnet = ResNet50(input_shape=IMAGE_SIZE + [3], weights='imagenet', include_top=False)
+
+# don't train existing weights
+for layer in resnet.layers:
+    layer.trainable = False
+
+flatten = Flatten()(resnet.output)
+dense = Dense(256, activation = 'relu')(flatten)
+dense = Dense(128, activation = 'relu')(dense)
+prediction = Dense(3, activation = 'softmax')(dense)
+
+#creating a model
+model = Model(inputs = resnet.input, outputs = prediction )
+
+model.summary()
+
+model.compile(loss = 'categorical_crossentropy', optimizer = 'adam', metrics = ['accuracy'])
+
+#executing the model
+history = model.fit(train_set, validation_data = (validate_set), epochs = 8, verbose = 1)
+
+# plotting the loss
+plt.plot(history.history['loss'],label = 'train_loss')
+plt.plot(history.history['val_loss'], label = 'testing_loss')
+plt.title('loss')
+plt.legend()
+plt.show()
+
+# Both Validation and Training accuracy is shown here
+
+plt.plot(history.history['accuracy'], label='training_accuracy')
+plt.plot(history.history['val_accuracy'], label='validation accuracy')
+plt.title('Accuracy')
+plt.legend()
+plt.show()
+
+# CHECKING THE CONFUSION MATRIX
+
+from sklearn.metrics import classification_report
+from sklearn.metrics import confusion_matrix
+from sklearn.metrics import f1_score
+Y_pred = model.predict(validate_set)
+y_pred = np.argmax(Y_pred ,axis =1)
+print('Confusion Matrix')
+confusion_matrix = confusion_matrix(validate_set.classes, y_pred)
+print(confusion_matrix)
+print('Classification Report')
+target_names = ['aca','n', 'scc']
+print(classification_report(validate_set.classes, y_pred, target_names=target_names))
+
+result = model.evaluate(validate_set,batch_size=128)
+print("test_loss, test accuracy",result)
+
+import pickle
+
+with open('model_pkl', 'wb') as files:
+    pickle.dump(model, files)
+
+# img = tf.keras.utils.load_img('/content/lung_colon_image_set/lung_image_sets/lung_aca/lungaca1.jpeg', target_size=(224, 224))
+# img_array = tf.keras.utils.img_to_array(img)
+# img_array = tf.expand_dims(img_array, 0)
+
+# # load saved model
+# with open('model_pkl' , 'rb') as f:
+#     lr = pickle.load(f)
+#     predi=lr.predict(img_array)
+#     print(predi)
+#     image_output_class=target_names[np.argmax(predi)]
+
+# print("The predicted class is", image_output_class)
+
+import gradio as gd
+from PIL import Image
+
+css_class="""
+body{ background-color:rgb(10, 30, 75)}
+ul>li{
+    text-decoration: none;
+    list-style:none;
+    margin: 1px;
+    padding:.5px
+}
+h3{
+    color: rgb(24, 46, 98);
+    margin: 1px;
+    padding:.5px
+    text-align: center;
+}
+h4{
+    text-decoration: underline;
+    color: rgb(218, 57, 57);
+    text-align: center;
+}
+
+"""
+def acaClassOutput():
+    return '''
+    <h3>You CT Scan Report:-</h3>
+    <hr>
+    <h4>You have Adenocarcinoma type cancer</h4>
+    <p>It is Non-small cell type cancer which has effected you 40% of lung cells.</p>
+    <ul>
+    <h4>You can try These cautions</h4>
+    <li>Try Radiation therapy, Chemotherapy, Targeted therapy, Immunotherapy</li>
+    <li>Try to stay away from Smokers and air pollution</li>
+    <li>Concern with your doctor for more details.</li>
+    </ul>
+    '''
+
+def sccClassOutput():
+    return '''
+    <h3>You CT Scan Report:-</h3>
+    <hr>
+    <h4>You have Squamous type cancer</h4>
+    <p>It effects the broncial tube of lungs. You probably have smoke history as it effected your 30% lungs</p>
+    <ul>
+    <h4>You can try These cautions</h4>
+    <li>Try Radiation therapy, Chemotherapy, Targeted therapy, Immunotherapy</li>
+    <li>Try to stay away from Smokers and air pollution</li>
+    <li>Concern with your doctor for more details.</li>
+    '''
+
+def nClassOutput():
+    return '''
+    <h3>You CT Scan Report:-</h3>
+    <hr>
+    <h4>You have Neuroendocrine type cancer</h4>
+    <p>This type of cancer effect neuroendocrine which are responsible for producing harmones. This is less common than other types</p>
+    <ul>
+    <h4>You can try These cautions</h4>
+    <li>Try regular screening if you have smoke history. Try surgeries</li>
+    <li>Try to stay away from Smokers and air pollution</li>
+    <li>Concern with your doctor for more details.</li>
+    '''
+# target_names = ['aca','n', 'scc']
+def predictOutPut(image_class):
+    output=''
+    if(image_class=='aca'):
+        output=acaClassOutput()
+    elif(image_class=='n'):
+        output=nClassOutput()
+    elif(image_class=='scc'):
+        output=sccClassOutput()
+    return output
+
+def greet_user(CTScanImage):
+    image=gd.inputs.Image()
+    pil_image = Image.fromarray(CTScanImage.astype('uint8'), 'RGB')
+    pil_image_resized = pil_image.resize((224,224))
+    img_array = tf.keras.utils.img_to_array(pil_image_resized)
+    img_array = tf.expand_dims(img_array, 0)
+    with open('model_pkl' , 'rb') as f:
+        lr = pickle.load(f)
+        predi=lr.predict(img_array)
+        image_output_class=target_names[np.argmax(predi)]
+    return  predictOutPut(image_output_class)
+
+customInput=gd.inputs.Image(label="Upload You CT Scanned Image")
+customOutput=gd.outputs.HTML(label="Your CT scan Report")
+app =  gd.Interface(fn = greet_user, inputs=customInput, outputs=customOutput,title="Lung Cancer Detection", description="Upload your CT Scan Image to know Whether You have cancer or not",css=css_class)
+app.launch()