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@@ -11,4 +11,4 @@ COPY . .
 RUN pip install --no-cache-dir -r requirements.txt
 
 # Run the Streamlit app
-CMD ["streamlit", "run", "app.py"]
+CMD ["streamlit", "run", "app.py", "--server.address=0.0.0.0"]

app.py

@@ -3,7 +3,7 @@ import numpy as np
 from PIL import Image
 from tensorflow.keras.models import load_model
 
-# Load the trained VGG16 model
+# Load the trained VGG16 model once
 @st.cache_resource
 def load_vgg_model():
     return load_model("brain_tumor_vgg16_model.keras")
@@ -12,27 +12,25 @@ def load_vgg_model():
 st.title("Brain Tumor MRI Classification App")
 st.write("Upload a brain MRI scan to check if it contains a tumor.")
 
-# Load the model outside the prediction block to prevent reloading on every interaction
+# Load the model
 model = load_vgg_model()
 
 uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
 
 if uploaded_file is not None:
-    # Display the uploaded image
+    # Preprocess and predict
     image = Image.open(uploaded_file).convert("RGB")
     st.image(image, caption='Uploaded MRI Scan', use_column_width=True)
 
-    # Preprocess the image for the model
     img_size = 150
     img_array = np.array(image.resize((img_size, img_size)))
     img_array = np.expand_dims(img_array, axis=0)
     img_array = img_array / 255.0
 
-    # Make prediction
     prediction = model.predict(img_array)
     class_predicted = (prediction > 0.5).astype("int32")[0][0]
 
-    # Display the result
+    # Display result
     if class_predicted == 1:
         st.error("Prediction: Tumor Detected")
     else: