UI for Pneumonia Detection

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