Update app.py

app.py

@@ -1,189 +1,113 @@
-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.")
+import streamlit as st
+import pickle
+from PIL import Image
+import numpy as np
+import os
+import numpy as np
+import pandas as pd 
+import random
+import logging
+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**.")