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app.py
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app.py
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from flask import Flask, render_template, request
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing import image
import numpy as np
app = Flask(__name__)
dic = {0: 'Thaumatophyllum bipinnatifidum',
1: 'Thaumatophyllum xanadu',
2: 'Hederaceum oxycardium ',
3: 'Hederaceum oxycardium Brazil'}
model = load_model('MobileNetV2-philodendronbaru-94.99.h5')
model.compile() # Compile the model to ensure metrics are built
def predict_label(img_path):
i = image.load_img(img_path, target_size=(224, 224))
i = image.img_to_array(i) / 255.0
i = np.expand_dims(i, axis=0)
p = np.argmax(model.predict(i), axis=-1)
return dic[int(p[0])]
# routes
@app.route("/", methods=['GET', 'POST'])
def main():
return render_template("philoden.html")
@app.route("/classification", methods=['GET', 'POST'])
def classification():
return render_template("classification.html")
@app.route("/submit", methods=['GET', 'POST'])
def get_output():
if request.method == 'POST':
img = request.files['my_image']
img_path = "static/" + img.filename
img.save(img_path)
p = predict_label(img_path)
return render_template("classification.html", prediction=p, img_path=img_path)
if __name__ == '__main__':
app.run(debug=True)