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from PIL import Image, ImageOps |
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import numpy as np |
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from collections import OrderedDict |
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import seaborn as sns |
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import matplotlib.pyplot as plt |
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import pandas as pd |
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from keras.models import load_model |
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import gradio as gr |
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def create_plot(data): |
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sns.set_theme(style="whitegrid") |
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f, ax = plt.subplots(figsize=(5, 5)) |
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sns.set_color_codes("pastel") |
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sns.barplot(x="Total", y="Labels", data=data,label="Total", color="b") |
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sns.set_color_codes("muted") |
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sns.barplot(x="Confidence Score", y="Labels", data=data,label="Conficence Score", color="b") |
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ax.legend(ncol=2, loc="lower right", frameon=True) |
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sns.despine(left=True, bottom=True) |
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return f |
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def predict_pneumonia(img): |
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np.set_printoptions(suppress=True) |
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model = load_model('keras_model.h5', compile=False) |
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class_names = open('labels.txt', 'r').readlines() |
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data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32) |
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image = img |
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size = (224, 224) |
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image_PIL = Image.fromarray(image) |
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image = ImageOps.fit(image_PIL, size, Image.LANCZOS) |
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image_array = np.asarray(image) |
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normalized_image_array = (image_array.astype(np.float32) / 127.0) - 1 |
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data[0] = normalized_image_array |
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prediction = model.predict(data) |
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index = np.argmax(prediction) |
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class_name = class_names[index] |
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confidence_score = prediction[0][index] |
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c_name = (class_name[2:])[:-1] |
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if c_name == "Normal": |
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pneumonia_prediction = "Chest XRay is normal no signs of pneumonia" |
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other_class = "Pneumonia" |
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else: |
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other_class = "Normal" |
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pneumonia_prediction = "Chest XRay shows signs of pneumonia" |
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res = {"Labels":[c_name,other_class], "Confidence Score":[(confidence_score*100),(1-confidence_score)*100],"Total":100} |
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data_for_plot = pd.DataFrame.from_dict(res) |
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pneumonia_conf_plt = create_plot(data_for_plot) |
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return pneumonia_prediction,pneumonia_conf_plt |
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css = """ |
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footer {display:none !important} |
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.output-markdown{display:none !important} |
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footer {visibility: hidden} |
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.hover\:bg-orange-50:hover { |
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--tw-bg-opacity: 1 !important; |
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background-color: rgb(229,225,255) !important; |
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} |
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img.gr-sample-image:hover, video.gr-sample-video:hover { |
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--tw-border-opacity: 1; |
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border-color: rgb(37, 56, 133) !important; |
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} |
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.gr-button-lg { |
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z-index: 14; |
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width: 113px; |
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height: 30px; |
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left: 0px; |
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top: 0px; |
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padding: 0px; |
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cursor: pointer !important; |
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background: none rgb(17, 20, 45) !important; |
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border: none !important; |
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text-align: center !important; |
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font-size: 14px !important; |
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font-weight: 500 !important; |
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color: rgb(255, 255, 255) !important; |
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line-height: 1 !important; |
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border-radius: 6px !important; |
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transition: box-shadow 200ms ease 0s, background 200ms ease 0s !important; |
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box-shadow: none !important; |
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} |
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.gr-button-lg:hover{ |
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z-index: 14; |
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width: 113px; |
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height: 30px; |
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left: 0px; |
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top: 0px; |
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padding: 0px; |
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cursor: pointer !important; |
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background: none rgb(66, 133, 244) !important; |
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border: none !important; |
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text-align: center !important; |
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font-size: 14px !important; |
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font-weight: 500 !important; |
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color: rgb(255, 255, 255) !important; |
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line-height: 1 !important; |
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border-radius: 6px !important; |
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transition: box-shadow 200ms ease 0s, background 200ms ease 0s !important; |
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box-shadow: rgb(0 0 0 / 23%) 0px 1px 7px 0px !important; |
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} |
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""" |
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with gr.Blocks(title="Pneumonia Detection | Data Science Dojo", css = css) as demo: |
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with gr.Row(): |
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with gr.Column(scale=4): |
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with gr.Row(): |
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imgInput = gr.Image() |
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with gr.Column(scale=1): |
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pneumonia = gr.Textbox(label='Presence of pneumonia') |
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plot = gr.Plot(label="Plot") |
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submit_button = gr.Button(value="Submit") |
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submit_button.click(fn=predict_pneumonia, inputs=[imgInput], outputs=[pneumonia,plot]) |
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gr.Examples( |
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examples=["normal_Sample.jpg","pneumonia_sample.jpg"], |
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inputs=imgInput, |
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outputs=[pneumonia,plot], |
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fn=predict_pneumonia, |
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cache_examples=True, |
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) |
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demo.launch() |
