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import streamlit as st
from tensorflow.keras.models import load_model
from tensorflow.keras.utils import CustomObjectScope
from tensorflow.keras.initializers import glorot_uniform
from PIL import Image
import numpy as np
import os
# Define models and their validation accuracies
model_options = {
"Model1": {
"path": "model_name.h5",
"accuracy": 50
},
"Model2": {
"path": "pneu_cnn_model.h5",
"accuracy": 76
}
}
# Load the model with custom objects if necessary
def load_model_with_custom_objects(model_path):
if not os.path.isfile(model_path):
raise FileNotFoundError(f"Model file not found: {model_path}")
custom_objects = {
'GlorotUniform': glorot_uniform
# Add other custom objects if needed
}
try:
with CustomObjectScope(custom_objects):
model = load_model(model_path)
except Exception as e:
st.error(f"Error loading model: {str(e)}")
raise
return model
# Image preprocessing (adjust as needed for your model)
def preprocess_image(image):
# Convert image to grayscale and resize
image = image.convert('L') # Convert to grayscale if necessary
image = image.resize((64, 64)) # Resize to match the model input shape
image_array = np.array(image)
image_array = image_array.astype('float32') / 255.0 # Normalize
image_array = np.expand_dims(image_array, axis=0) # Add batch dimension
image_array = np.expand_dims(image_array, axis=-1) # Add channel dimension if needed
return image_array
def main():
st.title("Pneumonia Detection App")
model_name = st.selectbox("Select a model", list(model_options.keys()))
model_path = model_options[model_name]["path"]
model_accuracy = model_options[model_name]["accuracy"]
# Load the selected model
try:
model = load_model_with_custom_objects(model_path)
except Exception as e:
st.error(f"Failed to load model: {e}")
return
st.write(f"Model: {model_name}")
st.write(f"Validation Accuracy: {model_accuracy}%")
uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
if uploaded_file is not None:
image = Image.open(uploaded_file)
st.image(image, caption="Uploaded Image", use_column_width=True)
# Perform prediction using the model
img_array = preprocess_image(image)
try:
prediction = model.predict(img_array)
predicted_class = "Pneumonia" if prediction[0][0] > 0.5 else "Normal"
st.write(f"Prediction: {predicted_class}")
except Exception as e:
st.error(f"Error during prediction: {str(e)}")
if __name__ == "__main__":
main()
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