Merge pull request #13 from DerikVo/streamlit

created a streamlit app to predict classes and created a page to show…

Author: DerikVo

Streamlit/app.py

@@ -0,0 +1,137 @@
+import streamlit as st
+import numpy as np
+import os
+import pandas as pd
+
+from tensorflow import keras
+from tensorflow.keras.models import load_model
+from PIL import Image, ImageOps
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+from sklearn.metrics import confusion_matrix, precision_score, recall_score, f1_score, accuracy_score
+
+import time
+import sys
+sys.path.append('../modules/')
+import model as m
+
+_ = """
+All comments will be assigned to the underscore variable so they dont get rendered in streamlit
+as mention in this discussion form:
+https://discuss.streamlit.io/t/any-way-to-prevent-commented-out-code-via-triple-quotes-to-be-displayed-in-streamlit/8821/6
+
+This code takes heavy influece from a previous project.
+https://github.com/DerikVo/NN_hackathon
+
+There were many changes to the code to get it to work with this data set,
+but the general structure remains the same
+"""
+
+
+# function to load and cache pretrained model
+@st.cache_resource
+def load_model_stream():
+    path = "../Models/CNN_base.h5"
+    model = load_model(path)
+    return model
+
+# function to preprocess an image and get a prediction from the model
+def get_prediction(model, image):
+	open_image = Image.open(image)
+	resized_image = open_image.resize((256, 256))
+	grayscale_image = resized_image.convert('L')
+	img = np.expand_dims(grayscale_image, axis=0)
+	predicted_prob = model.predict(img)[0]
+	classes = ['glioma', 'meningioma', 'notumor', 'pituitary']
+	probabilities = dict(zip(classes, predicted_prob))
+	sorted_probabilities = sorted(probabilities.items(), key=lambda x: x[1], reverse=True)
+	return sorted_probabilities
+def upload_mode():
+
+  st.header("Classification Mode")
+  st.subheader("Upload an Image to Make a Prediction")
+
+  # upload an image
+  uploaded_image = st.file_uploader("Upload your own image to test the model:", type=['jpg', 'jpeg', 'png'])
+
+  # when an image is uploaded, display image and run inference
+  if uploaded_image is not None:
+    st.image(uploaded_image)
+    st.text(get_prediction(classifier, uploaded_image))
+
+st.set_page_config(layout="wide")
+
+# load model
+classifier = load_model_stream()
+
+st.title("Brain Tumor Classifier")
+
+st.write('Use the sidebar to select a page to view.')
+
+page = st.sidebar.selectbox('Select Mode',['Upload Image','Model Evaluation']) 
+
+_ ='''
+This portion of the code was taken from the moduels function py file
+this code also brows ideas from previous projects and intergrates it into a function.
+Espically the model evaluation notebook.
+'''
+def model_Evaluation(path):
+	'''
+	Calculate accuracy, precision, recall, and F1 score.
+	'''
+	model = keras.models.load_model(path)
+	testing_folder_path = '../Images/Testing'
+	datagen = ImageDataGenerator()
+	test_ds = datagen.flow_from_directory(
+		testing_folder_path,
+		target_size=(256, 256),
+		color_mode='grayscale',
+		class_mode='categorical',
+		seed=42,
+		shuffle=False
+		)	
+	true_classes = test_ds.classes
+	y_pred = model.predict(test_ds)
+	predicted_classes = np.argmax(y_pred, axis=1)
+	accuracy = accuracy_score(true_classes, predicted_classes)
+	precision = precision_score(true_classes, predicted_classes, average='weighted')
+	recall = recall_score(true_classes, predicted_classes, average='weighted')
+	f1 = f1_score(true_classes, predicted_classes, average='weighted')
+	data = {'Accuracy': round(accuracy,4), 'Precision': round(precision,4), 'Recall': round(recall,4), 'F1 Score': round(f1,4)}
+	return data
+_ ='''
+This code utilized the streamlit documentation to implement columns
+and displaying images. In the future I want users to be able to upload their model and have it
+automatically be evaluated by the app.
+
+The links are as folows:
+https://docs.streamlit.io/library/api-reference/layout/st.columns
+https://docs.streamlit.io/library/api-reference/media/st.image
+'''
+
+if page == 'Model Evaluation':
+	path = ('../Models/CNN_base.h5')
+	data = model_Evaluation(path)
+	
+	reg_path = ('../Models/CNN_regularization.h5')
+	reg_data = model_Evaluation(path)
+	st.write("CNN base Metrics:\n")
+	
+	col1, col2 = st.columns(2)
+	with col1:
+		for metric_name, metric_value in data.items():
+			st.write(f"{metric_name}: {metric_value}")
+	with col2:
+		st.image("../Created_images/Neural Network confusion matrix.png", caption = "No regularization")
+	#add white space
+	st.write("")
+	st.write("")
+	st.write("\n CNN regularization Metrics:")
+	col3, col4 = st.columns(2)
+	with col3:
+		for metric_name, metric_value in reg_data.items():
+			st.write(f"{metric_name}: {metric_value}")
+	with col4:
+		st.image("../Created_images/Neural Network with regularization confusion matrix.png", caption = "With regularization")
+
+else:
+  upload_mode()