Create app.py

app.py

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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("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()