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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+best_weights.hdf5 filter=lfs diff=lfs merge=lfs -text

app.py

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+import streamlit as st
+import pickle
+from PIL import Image
+import numpy as np
+import cv2
+import os
+import numpy as np
+import pandas as pd 
+import random
+# import cv2
+import logging
+import matplotlib.pyplot as plt
+from PIL import Image
+
+# Deep learning libraries
+import tensorflow as tf
+import keras
+import keras.backend as K
+from keras.models import Model, Sequential
+from keras.layers import Input, Dense, Flatten, Dropout, BatchNormalization
+from keras.layers import Conv2D, SeparableConv2D, MaxPool2D, LeakyReLU, Activation
+from keras.optimizers import Adam
+from keras.preprocessing.image import ImageDataGenerator
+from keras.callbacks import ModelCheckpoint, ReduceLROnPlateau, EarlyStopping
+
+# Setting seeds for reproducibility
+seed = 232
+np.random.seed(seed)
+tf.random.set_seed(seed)
+
+# Define the model architecture
+def build_model(input_shape):
+    inputs = Input(shape=input_shape)
+
+    # First conv block
+    x = Conv2D(filters=64, kernel_size=(3, 3), activation='relu', padding='same')(inputs)
+    x = Conv2D(filters=64, kernel_size=(3, 3), activation='relu', padding='same')(x)
+    x = MaxPool2D(pool_size=(2, 2))(x)
+
+    # Second conv block
+    x = SeparableConv2D(filters=128, kernel_size=(3, 3), activation='relu', padding='same')(x)
+    x = SeparableConv2D(filters=128, kernel_size=(3, 3), activation='relu', padding='same')(x)
+    x = BatchNormalization()(x)
+    x = MaxPool2D(pool_size=(2, 2))(x)
+
+    # Third conv block
+    x = SeparableConv2D(filters=256, kernel_size=(3, 3), activation='relu', padding='same')(x)
+    x = SeparableConv2D(filters=256, kernel_size=(3, 3), activation='relu', padding='same')(x)
+    x = BatchNormalization()(x)
+    x = MaxPool2D(pool_size=(2, 2))(x)
+
+    # Fourth conv block
+    x = SeparableConv2D(filters=512, kernel_size=(3, 3), activation='relu', padding='same')(x)
+    x = SeparableConv2D(filters=512, kernel_size=(3, 3), activation='relu', padding='same')(x)
+    x = BatchNormalization()(x)
+    x = MaxPool2D(pool_size=(2, 2))(x)
+    x = Dropout(rate=0.2)(x)
+
+    # Fifth conv block
+    x = SeparableConv2D(filters=1024, kernel_size=(3, 3), activation='relu', padding='same')(x)
+    x = SeparableConv2D(filters=1024, kernel_size=(3, 3), activation='relu', padding='same')(x)
+    x = BatchNormalization()(x)
+    x = MaxPool2D(pool_size=(2, 2))(x)
+    x = Dropout(rate=0.2)(x)
+
+    # FC layer
+    x = Flatten()(x)
+    x = Dense(units=1024, activation='relu')(x) 
+    x = Dropout(rate=0.7)(x)
+    x = Dense(units=512, activation='relu')(x)
+    x = Dropout(rate=0.5)(x)
+    x = Dense(units=256, activation='relu')(x)  
+    x = Dropout(rate=0.3)(x)
+
+    # Output layer
+    output = Dense(units=1, activation='sigmoid')(x)
+
+    model = Model(inputs=inputs, outputs=output)
+    return model
+
+# Initialize and compile the model
+input_shape = (150, 150, 3)
+model = build_model(input_shape)
+model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
+
+# Load the weights
+model.load_weights('best_weights.hdf5')
+
+
+# Function to preprocess the image
+def preprocess_image(image):
+    image = image.convert('RGB')
+    image = image.resize((150, 150))
+    image = np.array(image) / 255.0
+    image = np.expand_dims(image, axis=0)
+    return image
+
+st.title("Pneumonia Detection from Chest X-ray")
+
+# Upload image
+uploaded_file = st.file_uploader("Choose a chest X-ray image...", type="jpeg")
+
+if uploaded_file is not None:
+    image = Image.open(uploaded_file)
+    st.image(image, caption='Uploaded X-ray.', use_column_width=True)
+    st.write("")
+    st.write("Classifying...")
+    
+    processed_image = preprocess_image(image)
+    prediction = model.predict(processed_image)
+    result = np.round(prediction).astype(int)[0][0]
+    
+    if result == 0:
+        st.write("The image is **not infected**.")
+    else:
+        st.write("The image is **infected**.")
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+# import streamlit as st
+# import numpy as np
+# from PIL import Image
+# import tensorflow as tf
+# from tensorflow.keras.models import load_model
+
+# # Load the model
+# @st.cache(allow_output_mutation=True)
+# def load_model():
+#     model = tf.keras.models.load_model('best_weights.hdf5')
+#     return model
+
+# model = load_model()
+
+# # Function to preprocess the image
+# def preprocess_image(image):
+#     image = image.resize((150, 150))  # Resize the image
+#     image = np.array(image) / 255.0  # Normalize the image
+#     image = np.expand_dims(image, axis=0)  # Add batch dimension
+#     return image
+
+# # Streamlit app
+# st.title('Pneumonia Detection App')
+
+# uploaded_file = st.file_uploader("Choose an image...", type=['jpg', 'jpeg', 'png'])
+
+# if uploaded_file is not None:
+#     # Display the uploaded image
+#     image = Image.open(uploaded_file)
+#     st.image(image, caption='Uploaded Image.', use_column_width=True)
+
+#     # Preprocess the image
+#     processed_image = preprocess_image(image)
+
+#     # Make predictions
+#     prediction = model.predict(processed_image)
+#     result = np.round(prediction).astype(int)[0][0]
+
+#     # Interpret the result
+#     if result == 0:
+#         st.write("Prediction: The image is not infected.")
+#     else:
+#         st.write("Prediction: The image is infected.")

best_weights.hdf5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:bb57db1720a217e18b32be5f4b354c3c322f918c6ba7f12d6ade9311b60e0b71
+size 78473152

requirements.txt

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+streamlit
+pillow
+numpy
+tensorflow==2.13.0
+keras