├── LICENSE
├── README.md
├── bgs
    └── bg5.png
├── main.py
├── model
    ├── labels.txt
    └── pneumonia_classifier.h5
├── requirements.txt
└── util.py


/LICENSE:
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 1 | MIT License
 2 | 
 3 | Copyright (c) 2023 Computer vision engineer
 4 | 
 5 | Permission is hereby granted, free of charge, to any person obtaining a copy
 6 | of this software and associated documentation files (the "Software"), to deal
 7 | in the Software without restriction, including without limitation the rights
 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 | 
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 | 
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
22 | 


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/README.md:
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 1 | # pneumonia-classification-web-app-python-streamlit
 2 | 
 3 | 
 4 | <p align="center">
 5 | <a href="https://www.youtube.com/watch?v=n_eMARPqBZI">
 6 |     <img width="600" src="https://utils-computervisiondeveloper.s3.amazonaws.com/thumbnails/with_play_button/pneumonia_classifier_streamlit.jpg" alt="Watch the video">
 7 |     </br>Watch on YouTube: Pneumonia classification web app with Python and Streamlit !
 8 | </a>
 9 | </p>
10 | 
11 | ## model
12 | 
13 | A pneumonia classifier was used to classify X-RAY images into {PNEUMONIA, NORMAL}.
14 | 
15 | The model was trained using the data provided in the next section and following [this step by step tutorial on how to train an image classifier with Teachable Machine](https://youtu.be/ybh9p3QOYrs).
16 | 
17 | The trained model is available in my [Patreon](https://www.patreon.com/ComputerVisionEngineer).
18 | 
19 | ## data
20 | 
21 | - Data: https://data.mendeley.com/datasets/rscbjbr9sj/2
22 | - License: CC BY 4.0
23 | - Citation: http://www.cell.com/cell/fulltext/S0092-8674(18)30154-5
24 | 


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/bgs/bg5.png:
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https://raw.githubusercontent.com/computervisioneng/pneumonia-classification-web-app-python-streamlit/16ced15a382fe2befada1b238b130ed702add3d7/bgs/bg5.png


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/main.py:
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 1 | import streamlit as st
 2 | from keras.models import load_model
 3 | from PIL import Image
 4 | import numpy as np
 5 | 
 6 | from util import classify, set_background
 7 | 
 8 | 
 9 | set_background('./bgs/bg5.png')
10 | 
11 | # set title
12 | st.title('Pneumonia classification')
13 | 
14 | # set header
15 | st.header('Please upload a chest X-ray image')
16 | 
17 | # upload file
18 | file = st.file_uploader('', type=['jpeg', 'jpg', 'png'])
19 | 
20 | # load classifier
21 | model = load_model('./model/pneumonia_classifier.h5')
22 | 
23 | # load class names
24 | with open('./model/labels.txt', 'r') as f:
25 |     class_names = [a[:-1].split(' ')[1] for a in f.readlines()]
26 |     f.close()
27 | 
28 | # display image
29 | if file is not None:
30 |     image = Image.open(file).convert('RGB')
31 |     st.image(image, use_column_width=True)
32 | 
33 |     # classify image
34 |     class_name, conf_score = classify(image, model, class_names)
35 | 
36 |     # write classification
37 |     st.write("## {}".format(class_name))
38 |     st.write("### score: {}%".format(int(conf_score * 1000) / 10))
39 | 


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/model/labels.txt:
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1 | 0 PNEUMONIA
2 | 1 NORMAL
3 | 


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/model/pneumonia_classifier.h5:
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https://raw.githubusercontent.com/computervisioneng/pneumonia-classification-web-app-python-streamlit/16ced15a382fe2befada1b238b130ed702add3d7/model/pneumonia_classifier.h5


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/requirements.txt:
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1 | numpy==1.23.5
2 | streamlit==1.22.0
3 | Pillow==9.5.0
4 | keras==2.12.0
5 | tensorflow==2.12.0
6 | 


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/util.py:
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 1 | import base64
 2 | 
 3 | import streamlit as st
 4 | from PIL import ImageOps, Image
 5 | import numpy as np
 6 | 
 7 | 
 8 | def set_background(image_file):
 9 |     """
10 |     This function sets the background of a Streamlit app to an image specified by the given image file.
11 | 
12 |     Parameters:
13 |         image_file (str): The path to the image file to be used as the background.
14 | 
15 |     Returns:
16 |         None
17 |     """
18 |     with open(image_file, "rb") as f:
19 |         img_data = f.read()
20 |     b64_encoded = base64.b64encode(img_data).decode()
21 |     style = f"""
22 |         <style>
23 |         .stApp {{
24 |             background-image: url(data:image/png;base64,{b64_encoded});
25 |             background-size: cover;
26 |         }}
27 |         </style>
28 |     """
29 |     st.markdown(style, unsafe_allow_html=True)
30 | 
31 | 
32 | def classify(image, model, class_names):
33 |     """
34 |     This function takes an image, a model, and a list of class names and returns the predicted class and confidence
35 |     score of the image.
36 | 
37 |     Parameters:
38 |         image (PIL.Image.Image): An image to be classified.
39 |         model (tensorflow.keras.Model): A trained machine learning model for image classification.
40 |         class_names (list): A list of class names corresponding to the classes that the model can predict.
41 | 
42 |     Returns:
43 |         A tuple of the predicted class name and the confidence score for that prediction.
44 |     """
45 |     # convert image to (224, 224)
46 |     image = ImageOps.fit(image, (224, 224), Image.Resampling.LANCZOS)
47 | 
48 |     # convert image to numpy array
49 |     image_array = np.asarray(image)
50 | 
51 |     # normalize image
52 |     normalized_image_array = (image_array.astype(np.float32) / 127.5) - 1
53 | 
54 |     # set model input
55 |     data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
56 |     data[0] = normalized_image_array
57 | 
58 |     # make prediction
59 |     prediction = model.predict(data)
60 |     # index = np.argmax(prediction)
61 |     index = 0 if prediction[0][0] > 0.95 else 1
62 |     class_name = class_names[index]
63 |     confidence_score = prediction[0][index]
64 | 
65 |     return class_name, confidence_score
66 | 


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