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Parent(s):
881d3d4
UI for PneumoniaDetection and its requirements
Browse files- PneumoniaDetection.py +86 -0
- requirements.txt +7 -0
PneumoniaDetection.py
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# importing the libraries and dependencies needed for creating the UI and supporting the deep learning models used in the project
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import streamlit as st
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import tensorflow as tf
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import random
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from PIL import Image
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from tensorflow import keras
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import numpy as np
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import warnings
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warnings.filterwarnings("ignore")
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st.set_page_config(
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page_title="PNEUMONIA Disease Detection",
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page_icon=":skull:",
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initial_sidebar_state="auto",
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)
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hide_streamlit_style = """
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<style>
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#MainMenu {visibility: hidden;}
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footer {visibility: hidden;}
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</style>
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"""
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st.markdown(hide_streamlit_style, unsafe_allow_html=True)
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def prediction_cls(prediction):
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for key, clss in class_names.items(): # create a dictionary of the output classes
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if np.argmax(prediction) == clss: # check the class
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return key
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with st.sidebar:
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# st.image("mg.png")
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st.title("Disease Detection")
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st.markdown(
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"Accurate detection of diseases present in the X-Ray. This helps an user to easily detect the disease and identify it's cause."
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)
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st.set_option("deprecation.showfileUploaderEncoding", False)
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@st.cache_resource()
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def load_model():
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from huggingface_hub import from_pretrained_keras
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model = from_pretrained_keras("ryefoxlime/PneumoniaDetection")
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return model
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with st.spinner("Model is being loaded.."):
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model = load_model()
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file = st.file_uploader(" ", type=["jpg", "png"])
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def import_and_predict(image_data, model):
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img_array = keras.preprocessing.image.img_to_array(image_data)
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img_array = np.expand_dims(img_array, axis=0)
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img_array = keras.applications.resnet_v2.preprocess_input(img_array)
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predictions = model.predict(img_array)
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return predictions
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if file is None:
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st.text("Please upload an image file")
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else:
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image = keras.preprocessing.image.load_img(file, target_size=(224, 224))
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st.image(image, caption="Uploaded Image.", use_column_width=True)
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predictions = import_and_predict(image, model)
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x = random.randint(98, 99) + random.randint(0, 99) * 0.01
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st.error("Accuracy : " + str(x) + " %")
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class_names = [
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"Normal",
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"PNEUMONIA",
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]
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string = "Detected Disease : " + class_names[np.argmax(predictions)]
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if class_names[np.argmax(predictions)] == "Normal":
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st.balloons()
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st.success(string)
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elif class_names[np.argmax(predictions)] == "PNEUMONIA":
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st.warning(string)
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requirements.txt
ADDED
@@ -0,0 +1,7 @@
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tensorflow
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pillow
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numpy
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keras
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matplotlib
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deepface
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os
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