Upload folder using huggingface_hub

.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

.streamlit/config.toml

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+[global]
+dataFrameSerialization = "legacy"

Dockerfile

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+FROM python:3.9-slim
+
+# Create a non-root user
+RUN adduser --disabled-password --gecos '' streamlit-user
+
+WORKDIR /app
+
+COPY . .
+
+# Set Streamlit's config directory to the current working directory
+ENV STREAMLIT_SERVER_CONFIG_FILE=/app/.streamlit/config.toml
+
+# Install dependencies as the non-root user
+USER streamlit-user
+RUN pip3 install --no-cache-dir -r requirements.txt
+
+EXPOSE 8501
+
+CMD ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0"]

app.py

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+import streamlit as st
+import numpy as np
+from PIL import Image
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+
+# Load the trained VGG16 model
+@st.cache_resource
+def load_vgg_model():
+    return load_model("brain_tumor_vgg16_model.keras")
+
+model = load_vgg_model()
+img_size = 150
+
+# Streamlit UI
+st.title("Brain Tumor MRI Classification App")
+st.write("Upload a brain MRI scan to check if it contains a tumor.")
+
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    # Display the uploaded image
+    image = Image.open(uploaded_file).convert("RGB")
+    st.image(image, caption='Uploaded MRI Scan', use_column_width=True)
+    st.write("")
+    st.write("Classifying...")
+
+    # Preprocess the image for the model
+    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  # Rescale to [0, 1]
+
+    # Make prediction
+    prediction = model.predict(img_array)
+    class_predicted = (prediction > 0.5).astype("int32")[0][0]
+
+    # Display the result
+    if class_predicted == 1:
+        st.error("Prediction: Tumor Detected")
+    else:
+        st.success("Prediction: No Tumor Detected")

brain_tumor_vgg16_model.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cad45eb1560e9ffabdba98c067cdc5063779c375cc8d673a7bb3da261734c749
+size 84124079

requirements.txt

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+numpy
+tensorflow
+streamlit
+Pillow
+huggingface_hub