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bady_fat_calculator / app.py

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Update app.py

06381b2 verified 22 days ago

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	from flask import Flask, request, jsonify
	from tensorflow.keras.preprocessing import image
	import numpy as np
	from tensorflow.keras.models import load_model
	from PIL import Image
	from io import BytesIO

	app = Flask(__name__)

	# Load the trained models
	# model_female_leg = load_model('trained_model_female_leg.h5')
	# model_female_arm = load_model('trained_model_female_arm.h5')
	# model_male_arm = load_model('trained_model_male_arm.h5')
	#model_male_leg = load_model('YourModelName.h5')
	#model_male_arm = load_model('YourModelName.h5')
	#model_female_arm = load_model('YourModelName.h5')
	#model_female_leg = load_model('model.h5')
	#model_male_leg = load_model('trained_model_male_leg.h5')


	model_male_leg = load_model('model.h5')
	model_male_arm = load_model('model.h5')
	model_female_leg = load_model('YourModelName.h5')
	model_female_arm = load_model('model.h5')

	# Define class labels for each model
	class_label_male_leg = ['High', 'Moderate', 'Low']
	class_label_male_arm = ['High', 'Moderate', 'Low']
	class_labels_female_leg = ['High', 'Moderate', 'Low']
	class_labels_female_arm = ['High', 'Moderate', 'Low']

	# Define route for model prediction for model 1
	@app.route('/predict_model_male_leg', methods=['POST'])
	def predict_model1():
	return predict(request.files['file'], model_male_leg, class_label_male_leg)

	# Define route for model prediction for model 2
	@app.route('/predict_model_male_arm', methods=['POST'])
	def predict_model2():
	return predict(request.files['file'], model_male_arm, class_label_male_arm)

	# Define route for model prediction for model 3
	@app.route('/predict_model_female_leg', methods=['POST'])
	def predict_model3():
	return predict(request.files['file'], model_female_leg, class_labels_female_leg)

	# Define route for model prediction for model 4
	@app.route('/predict_model_female_arm', methods=['POST'])
	def predict_model4():
	return predict(request.files['file'], model_female_arm, class_labels_female_arm)

	# Define route for ping
	@app.route('/', methods=['GET'])
	def ping():
	return jsonify({'PING': 'Success!'})

	def predict(file, model, class_labels):
	# Check if file is provided
	if not file:
	return jsonify({'error': 'No file provided'})

	# Load and preprocess the image
	img = Image.open(BytesIO(file.read())) # Convert FileStorage to io.BytesIO
	img = img.resize((150, 150)) # Resize image to match model's input shape
	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)

	# Interpret the result
	predicted_class = np.argmax(prediction)
	predicted_label = class_labels[predicted_class]

	return jsonify( predicted_label)

	# if __name__ == '__main__':
	# app.run(debug=True)