Create app.py

app.py

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+import streamlit as st
+import tensorflow as tf
+from tensorflow.keras.preprocessing import image
+import numpy as np
+
+# Load the pre-trained model
+model = tf.keras.models.load_model('model.h5')
+
+# Define class labels
+class_labels = ['Fractured', ' Not Fractured']
+
+# Streamlit app
+st.title('Bone Fracture Detection App')
+
+# Upload an image for prediction
+uploaded_image = st.file_uploader("Choose an image...", type=["jpg", "png", "jpeg"])
+
+if uploaded_image is not None:
+    # Display the uploaded image
+    st.image(uploaded_image, caption='Uploaded Image', use_column_width=True)
+
+    # Preprocess the image for model prediction
+    img = image.load_img(uploaded_image, target_size=(224, 224))
+    img_array = image.img_to_array(img)
+    img_array = np.expand_dims(img_array, axis=0)
+    img_array /= 255.0
+
+    # Make prediction
+    prediction = model.predict(img_array)
+    predicted_class = int(np.round(prediction)[0][0])
+
+    # Display the prediction result
+    st.write(f"Predicted class: {class_labels[predicted_class]}")
+    st.write(f"Confidence: {prediction[0][0] * 100:.2f}%")