Added app files

app.py

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+import numpy as np
+import pandas as pd
+import altair as alt
+from PIL import Image
+import streamlit as st
+import tensorflow as tf
+from itertools import chain
+
+col1, col2, col3 = st.columns(3)
+
+with col1:
+    st.write(' ')
+
+with col2:
+    st.image("unilogo.png", width=300)
+    st.markdown("<h6 style='text-align: center; color: grey;'>University of Technology and Applied Sciences, Muscat</h6>",
+            unsafe_allow_html=True)
+    st.markdown("<h1 style='text-align: center; color: grey;'>X-RAY Classsifier</h1>",
+                unsafe_allow_html=True)
+st.markdown("<h6 style='text-align: center; color: grey;'>DEEVYANKAR AGARWAL</h6>",
+            unsafe_allow_html=True)
+st.markdown("<h6 style='text-align: center; color: grey;'>MAATHIR YAHYA ABDULLAH AL HINAI</h6>",
+            unsafe_allow_html=True)
+st.markdown("<h6 style='text-align: center; color: grey;'>MAATHIR YAHYA ABDULLAH AL HINAI</h6>",
+            unsafe_allow_html=True)
+st.markdown("<h6 style='text-align: center; color: grey;'>MARYAM MOHAMMED HILAL AL-HASHAMI</h6>",
+            unsafe_allow_html=True)
+st.markdown("<h6 style='text-align: center; color: grey;'>Shafa NASSER MOHAMMED AL AAMRI</h6>",
+            unsafe_allow_html=True)
+
+with col3:
+    st.write(' ')
+
+
+file_upload = st.file_uploader("Upload the X-RAY Scan ", type=[
+                               "png", "jpg", "jpeg", "heic", "tiff", "bmp", "raw"], accept_multiple_files=False)
+
+
+class_dict = {"0": 'covid19', "1": 'normal', "2": 'pneumonia'}
+
+model = tf.keras.models.load_model('fmodel.h5')
+
+
+def preprocess_image(image_path):
+
+    image = Image.open(image_path)
+
+    image = image.resize((224, 224))
+    image_array = tf.keras.preprocessing.image.img_to_array(image)
+    image_array /= 255.0
+    image_array = tf.expand_dims(image_array, 0)
+
+    return image_array
+
+
+if file_upload:
+    
+    with open(file_upload.name, "wb") as f:
+        f.write(file_upload.getbuffer())
+
+    saved_path = f"{file_upload.name}"
+
+    preprocessed_image = preprocess_image(saved_path)
+    pred_prob = model.predict(preprocessed_image)
+    predicted_class_index = np.argmax(pred_prob, axis=1).item()
+    predicted_class = class_dict[f"{predicted_class_index}"]
+
+    st.write("Predicted Class: ", predicted_class)
+
+    print(predicted_class_index)
+    print(predicted_class)
+    print(pred_prob)
+
+    graph_input = list(chain.from_iterable(pred_prob.tolist()))
+    "Plot depicting Class Probabilities"
+    source = pd.DataFrame({
+        'Class probability': graph_input,
+        'Class': ["COVID-19", "NORMAL", "PNEUMONIA"]
+    })
+
+    bar_chart = alt.Chart(source).mark_bar().encode(
+        y='Class probability:Q',
+        x='Class:O',
+    )
+
+    element5 = st.altair_chart(bar_chart, use_container_width=True)

fmodel.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4e70e0b9cdc0b0f564c3bed77611d742032c5da5fd58664645f2e8ec883ad6c7
+size 59842536