Delete PneumoniaDetection.py

PneumoniaDetection.py

@@ -1,86 +0,0 @@
-# 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)