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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+brain_tumor_vgg16_model.keras filter=lfs diff=lfs merge=lfs -text

Dockerfile

@@ -0,0 +1,23 @@
+# Use a minimal base image with Python 3.9 installed
+FROM python:3.9
+
+# Set the working directory inside the container to /app
+WORKDIR /app
+
+# Copy all files from the current directory on the host to the container's /app directory
+COPY . .
+
+# Install Python dependencies listed in requirements.txt
+RUN pip3 install -r requirements.txt
+
+RUN useradd -m -u 1000 user
+USER user
+ENV HOME=/home/user \
+    PATH=/home/user/.local/bin:$PATH
+
+WORKDIR $HOME/app
+
+COPY --chown=user . $HOME/app
+
+# Define the command to run the Streamlit app on port "7860" and make it accessible externally
+CMD ["streamlit", "run", "app.py", "--server.port=7860", "--server.address=0.0.0.0", "--server.enableXsrfProtection=false"]

app.py

@@ -0,0 +1,72 @@
+import streamlit as st
+import numpy as np
+from PIL import Image
+from tensorflow.keras.models import load_model
+import os
+import time
+import requests
+from io import BytesIO
+
+# Function to load the model (cached for efficiency)
+@st.cache_resource
+def load_vgg_model():
+    return load_model("brain_tumor_vgg16_model.keras")
+
+# Function to run the prediction and show progress
+def run_prediction(image_path, model, img_size):
+    # Create a progress bar and status text
+    progress_bar = st.progress(0)
+    status_text = st.empty()
+
+    for i in range(100):
+        progress_bar.progress(i + 1)
+        status_text.text(f"Processing... {i+1}%")
+        time.sleep(0.01)
+
+    # Process and predict
+    try:
+        # Check if the input is a file-like object or a string path
+        if isinstance(image_path, str):
+            image = Image.open(image_path).convert("RGB")
+        else:
+            image = Image.open(image_path).convert("RGB")
+
+        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
+
+        prediction = model.predict(img_array)
+        class_predicted = (prediction > 0.5).astype("int32")[0][0]
+
+        # Display the result
+        if class_predicted == 1:
+            st.error(f"Prediction: Tumor Detected (Probability: {100*prediction[0][0]:.2f}%)")
+        else:
+            st.success(f"Prediction: No Tumor Detected (Probability: {100*(1 - prediction[0][0]):.2f}%)")
+    except Exception as e:
+        st.error(f"An error occurred during classification: {e}")
+
+    # Clear the progress bar
+    progress_bar.empty()
+    status_text.empty()
+
+# Streamlit UI
+st.title("Brain Tumor MRI Classification App")
+st.write("Upload your own image to get a prediction.")
+
+# Load the model
+model = load_vgg_model()
+
+# Define image size (must match the model's input size)
+img_size = 150
+
+# --- UI for user image upload ---
+st.subheader("Upload Your Own Image")
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    st.image(uploaded_file, caption='Uploaded MRI Scan', use_container_width=True)
+
+    # Use a button to trigger the classification explicitly
+    if st.button("Check for Brain Tumor"):
+        run_prediction(uploaded_file, model, img_size)

brain_tumor_vgg16_model.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bd437d9296b98e0061c857e29407a19f08404a926f9a15c952e1a86b27bd8673
+size 84226863

requirements.txt

@@ -0,0 +1,5 @@
+numpy==2.0.2
+tensorflow==2.20.0
+streamlit==1.49.1
+pillow==11.3.0
+requests==2.32.5