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