Create app.py

app.py

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+import streamlit as st
+import tensorflow as tf
+from PIL import Image
+import numpy as np
+import os
+
+# Load the saved model
+@st.cache_resource
+def load_model():
+    model = tf.keras.models.load_model("pneumonia_cnn_model.keras")  # Use relative path for deployment
+    return model
+
+model = load_model()
+
+st.title("🫁 Pneumonia Detection from Chest X-ray Images")
+
+st.markdown("Upload your own X-ray or try one of the sample images below.")
+
+# === Sample Image Section ===
+sample_images = {
+    "Choose a sample image": None,
+    "🧍 Normal Sample": "normal.jpg",
+    "🤒 Pneumonia Sample": "pneumonia.jpg"
+}
+
+selected_sample = st.selectbox("📂 Select a sample image", list(sample_images.keys()))
+uploaded_file = st.file_uploader("📁 Or upload a chest X-ray image...", type=["jpg", "jpeg", "png"])
+
+# Determine which image to use
+if selected_sample != "Choose a sample image":
+    image_path = sample_images[selected_sample]
+    image = Image.open(image_path).convert("RGB")
+    st.image(image, caption=f'🖼️ {selected_sample}', use_column_width=True)
+elif uploaded_file is not None:
+    image = Image.open(uploaded_file).convert("RGB")
+    st.image(image, caption='🖼️ Uploaded Image', use_column_width=True)
+else:
+    image = None
+
+# === Predict Button ===
+if image and st.button('🔍 Predict'):
+    img = image.resize((150, 150))
+    img_array = np.array(img) / 255.0
+    img_array = np.expand_dims(img_array, axis=0)
+
+    prediction = model.predict(img_array)
+
+    if prediction[0][0] > 0.5:
+        st.error("🩺 **Prediction: Pneumonia Detected**")
+    else:
+        st.success("✅ **Prediction: Normal**")