Upload 2 files

app.py

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+import streamlit as st
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing import image as keras_image
+import numpy as np
+from PIL import Image
+
+# Load the pre-trained model
+model = load_model('CNN_model.h5')
+img_width, img_height = 256, 256
+
+# from tensorflow.keras.preprocessing import image as keras_image
+
+def preprocess_image(image):
+    # Convert image to RGB if it has fewer than 3 channels
+    if image.mode != 'RGB':
+        image = image.convert('RGB')
+    # Resize image to match model input shape
+    image = image.resize((img_width, img_height))
+    # Convert image to numpy array
+    image = keras_image.img_to_array(image)
+    # Ensure the image has 3 color channels (RGB)
+    if image.shape[-1] != 3:
+        raise ValueError("Image does not have 3 color channels (RGB)")
+    # Reshape image to add batch dimension
+    image = np.expand_dims(image, axis=0)
+    # Normalize pixel values
+    image = image / 255.0
+    return image
+
+
+# Streamlit app
+st.title('🌡️🦠Pneumonia Detection Model')
+
+uploaded_file = st.file_uploader("Choose chest X-ray image...", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    # Display uploaded image
+    image = Image.open(uploaded_file)
+    st.image(image,width=400)
+
+    # Preprocess the image
+    processed_image = preprocess_image(image)
+
+    # Make prediction
+    prediction = model.predict(processed_image)
+
+    # Get class label
+    class_label = np.argmax(prediction)
+    
+    # Display prediction
+    if class_label==1:
+        st.error('Affected  by Pneumonia', icon="🚨")
+    else:
+        st.success('Result is NORMAL !!', icon="😊")
+       

my_pneumonia_detection_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5d9d85f19d75ee0e133367cb82a065462909f271ec0726c727038add1321cb9a
+size 97909472