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import os
from flask import Flask, request, render_template
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing import image
import numpy as np
app = Flask(__name__)
# Load the trained model
model = load_model('bone_fracture/bone_model.h5') # Update with your model's file path
# Define class labels
class_labels = ['Not Fractured', 'Fractured']
@app.route('/', methods=['GET', 'POST'])
def index():
if request.method == 'POST':
# Get the uploaded file from the form
file = request.files['file']
if file:
# Save the file temporarily
temp_path = 'temp.jpg'
file.save(temp_path)
# Load and preprocess the image
img = image.load_img(temp_path, 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 a prediction
prediction = model.predict(img_array)
predicted_class = int(np.round(prediction)[0][0])
predicted_label = class_labels[predicted_class]
# Delete the temporary file
os.remove(temp_path)
return render_template('result.html', prediction=predicted_label)
return render_template('index.html')
if __name__ == '__main__':
app.run(debug=True)
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