Upload 4 files

.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
+best_model_.keras filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,51 @@
+import streamlit as st
+import tensorflow as tf
+from tensorflow.keras.preprocessing import image
+import numpy as np
+
+# Function to preprocess the image
+def preprocess_image(img_path, img_height, img_width):
+    # Load the image in grayscale mode
+    img = image.load_img(img_path, target_size=(img_height, img_width), color_mode='grayscale')
+    # Convert the image to a numpy array
+    img_array = image.img_to_array(img)
+    # Expand dimensions to match the model's input shape
+    img_array = np.expand_dims(img_array, axis=0)
+    # Normalize the image
+    img_array = img_array / 255.0  
+    return img_array
+
+# Load the best model
+@st.cache_resource
+def load_model():
+    return tf.keras.models.load_model('best_model_.keras')
+
+model = load_model()
+
+# Streamlit UI
+st.title("X-ray Image Classification")
+st.write("Upload an X-ray image to classify it as Normal or Pneumonia.")
+
+# File uploader for image
+uploaded_file = st.file_uploader("Choose an X-ray image...", type="jpeg")
+
+if uploaded_file is not None:
+    # Save the uploaded file to a temporary location
+    with open("temp.jpeg", "wb") as f:
+        f.write(uploaded_file.getbuffer())
+    
+    # Preprocess the image
+    img_height, img_width = 224, 224  # Use the same dimensions as used during training
+    preprocessed_img = preprocess_image("temp.jpeg", img_height, img_width)
+    
+    # Display the uploaded image
+    st.image(uploaded_file, caption="Uploaded X-ray Image", use_column_width=True)
+    
+    # Make predictions
+    prediction = model.predict(preprocessed_img)
+    
+    # Output the prediction
+    if prediction[0] > 0.5:
+        st.write("Prediction: Pneumonia")
+    else:
+        st.write("Prediction: Normal")

best_model_.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:86a7ca2de31117d4cfb752441c31ea3a85f04049562281c01f8010af6e0df1c9
+size 1148000380

dockerfile

@@ -0,0 +1,20 @@
+# Use an official Python runtime as a parent image
+FROM python:3.9-slim
+
+# Set the working directory in the container
+WORKDIR /app
+
+# Copy the requirements file into the container
+COPY requirements.txt ./
+
+# Install any necessary dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy the rest of the application code into the container
+COPY . .
+
+# Expose the port the app runs on
+EXPOSE 8501
+
+# Command to run the Streamlit app
+CMD ["streamlit", "run", "app.py"]

requirements.txt

@@ -0,0 +1,9 @@
+tensorflow 
+opencv-python 
+matplotlib
+streamlit
+numpy
+scipy
+scikit-learn
+keras_tuner
+scikeras