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from flask import Flask, request, render_template, jsonify
from flask_cors import CORS
import keras
import numpy as np
from keras.preprocessing import image
import io
app = Flask(__name__)
CORS(app)
model = keras.models.load_model('Cats_vs_Dogs.model')
@app.route('/')
def index():
return render_template('index.html', prediction=None)
@app.route('/predict', methods=['POST'])
def predict():
imagefile = request.files['imagefile']
# Read the image file into memory
img_stream = imagefile.read()
# Convert the image to grayscale and resize
img = image.load_img(io.BytesIO(img_stream), color_mode='grayscale', target_size=(60, 60))
img_array = image.img_to_array(img)
img_array = np.expand_dims(img_array, axis=0)
img_array /= 255.0
prediction = model.predict(img_array)
predicted_class = "Dog" if prediction[0][1] > prediction[0][0] else "Cat"
return jsonify({'prediction': predicted_class})
if __name__ == '__main__':
app.run(debug=True)