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import os
os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0'
import streamlit as st
from keras.models import load_model
from keras.preprocessing.image import load_img, img_to_array
from keras.applications.vgg19 import preprocess_input
import numpy as np
from transformers import pipeline
# Load the Keras model
model = load_model("/home/user/app/Tumour_Model(V19).h5")
# Define the class reference dictionary
ref = {0: 'Glioma', 1: 'Meningioma', 2: 'No Tumor', 3: 'Pituitary'}
# Define function to preprocess the image
def preprocess_image(image_path):
img = load_img(image_path, target_size=(256, 256))
img_array = img_to_array(img)
img_array = preprocess_input(img_array)
img_array = np.expand_dims(img_array, axis=0)
return img_array
# Streamlit app
def main():
st.title('Brain Tumor Classification')
# Upload image
uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
if uploaded_file is not None:
# Preprocess the image
image = preprocess_image(uploaded_file)
# Make prediction
predictions = model.predict(image)
predicted_class = np.argmax(predictions)
predicted_class_name = ref[predicted_class]
probabilities = predictions.tolist()[0]
# Display prediction
st.success(f"Predicted class: {predicted_class_name}")
st.write("Probabilities:")
for i, prob in enumerate(probabilities):
st.write(f"{ref[i]}: {prob}")
# Hugging Face component
#st.title("Hugging Face Model")
#model_name = "mrm8488/distill-bert-base-spanish-wwm-cased-finetuned-spa-squad2-es"
#st.huggingface_component(model_name)
if __name__ == '__main__':
main()
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