Spaces:
Sleeping
Sleeping
File size: 1,958 Bytes
db4a5d2 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 |
import streamlit as st
import os
from werkzeug.utils import secure_filename
import cv2
import numpy as np
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing import image
# Load the trained model
model = load_model("Bone_fracture_classifier_model.h5")
# Function to check if the file extension is allowed
def allowed_file(filename):
return '.' in filename and filename.rsplit('.', 1)[1].lower() in {'jpg', 'jpeg', 'png'}
# Function to preprocess the image
def preprocess_image(file_path):
img = image.load_img(file_path, target_size=(200, 200))
img_array = image.img_to_array(img)
img_array = np.expand_dims(img_array, axis=0)
img_array /= 255.0 # Normalize the image
return img_array
def main():
st.title("Bone Fracture Detection App")
uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
if uploaded_file is not None:
# Check if the file extension is allowed
if allowed_file(uploaded_file.name):
# Display the selected image
st.image(uploaded_file, caption="Uploaded Image", use_column_width=True)
# Save the uploaded image temporarily
temp_image_path = "temp_image.jpg"
with open(temp_image_path, "wb") as temp_image:
temp_image.write(uploaded_file.read())
# Preprocess the image
img_array = preprocess_image(temp_image_path)
# Make prediction
prediction = model.predict(img_array)[0, 0]
result = "Broken" if prediction > 0.5 else "Not Broken"
st.write(f"Prediction: {result}")
st.write(f"Confidence: {prediction:.2%}")
# Remove the temporary image file
os.remove(temp_image_path)
else:
st.warning("Invalid file format. Please upload an image with a valid format (jpg, jpeg, or png).")
if __name__ == "__main__":
main()
|