app.py

import streamlit as st
import tensorflow as tf
import numpy as np
from PIL import Image
import request
import url


st.title("Assembly AI HACKATON")

from PIL import Image
image = Image.open('descarga.png')
st.image(image, caption='Assembly AI')


def main():
    st.image(image, caption='Assembly AI')

if __name__ == '__main__':
    main()
    

st.set_option('deprecation.showfileUploaderEncoding', False)
@st.cache(allow_output_mutation=True)
def load_model():
	model = tf.keras.models.load_model('./my_model.h5')
	return model


def predict_class(image, model):

	image = tf.cast(image, tf.float32)
	image = tf.image.resize(image, [256, 256])

	image = np.expand_dims(image, axis = 0)

	prediction = model.predict(image)

	return prediction


model = load_model()


st.title('Leaf Classifier detection using Convulutional Neural Networks')

file = st.file_uploader("Upload an image ", type=["jpg", "png"])


if file is None:
	st.text('Waiting for upload....')

else:
	slot = st.empty()
	slot.text('Running inference....')

	test_image = Image.open(file)

	st.image(test_image, caption="Input Image", width = 400)

	pred = predict_class(np.asarray(test_image), model)

	class_names = ['Potato___Early_blight', 'Potato___Late_blight', 'Potato___healthy']

	result = class_names[np.argmax(pred)]

	output = 'The model predicts that the image that that you upload is ' + result

	slot.text('Done')

	st.success(output)



st.info('The model is a CNN doing a classification task ', icon="ℹ️")
st.success('The model is running in a inference successfully !', icon="✅")