Create app.py

app.py

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+import streamlit as st
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing import image
+import numpy as np
+
+# Load the model and weights
+model_path = "Pneumonia_detection_using_CNN.h5"
+weights_path = "Pneumonia_detection_using_CNN.weights.h5"
+
+model = load_model(model_path)
+model.load_weights(weights_path)
+
+# Streamlit app
+st.title('Pneumonia Detection App')
+
+# File uploader for image input
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "png"])
+
+if uploaded_file is not None:
+    # Display the uploaded image
+    st.image(uploaded_file, caption='Uploaded Image', use_column_width=True)
+    
+    # Check if predict button is clicked
+    col1, col2, col3 = st.columns([1, 4, 1])  # Adjust column width ratios as needed
+    with col2:
+        if st.button('Predict', key='predict_button'):
+            # Load and preprocess the image
+            img = image.load_img(uploaded_file, target_size=(224, 224))
+            img_array = image.img_to_array(img)
+            img_array = np.expand_dims(img_array, axis=0)
+
+            # Make a prediction
+            prediction = model.predict(img_array)
+
+            # Display the prediction with confidence level in large highlighted text
+            class_names = ['Normal', 'Pneumonia']
+            predicted_class = class_names[np.argmax(prediction)]
+            confidence_level = np.max(prediction) * 100  # Convert probability to percentage
+
+            # Set text color based on prediction
+            if predicted_class == 'Normal':
+                text_color = 'green'
+            else:
+                text_color = 'red'
+
+            # Display prediction and confidence level in large highlighted text
+            st.markdown(f'<p style="font-size:32px; color:{text_color};">Prediction: {predicted_class}</p>', unsafe_allow_html=True)
+            st.markdown(f'<p style="font-size:32px; color:{text_color};">Confidence: {confidence_level:.2f}%</p>', unsafe_allow_html=True)