app.py

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}%")