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import pickle
import streamlit as st
import pickle
import streamlit as st
import tensorflow as tf
import numpy as np
from PIL import Image
import joblib
# with open("Trained_model.sav","rb") as a:
# loaded_model=pickle.load(a)
loaded_model=joblib.load("Trained_model.sav")
def app():
def pred_and_plot(model, filename):
# Make a prediction
pred = model.predict(filename)
return pred
st.markdown('''<p style="font-family:sans-serif; color:white; font-size: 42px"> <b>**Get cyclone intensity with the click of a button.**</b></p>''',unsafe_allow_html=True)
st.markdown('''<p style="font-family:sans-serif; color:white; font-size: 20px;">Sample image 👇</p>''',unsafe_allow_html=True)
sample_img="30.jpg"
st.image(
sample_img,
caption=f"This is a sample image which you feed in this app and calculate the intensity :)",
use_column_width=True,
)
st.markdown('''<p style="font-family:sans-serif; color:white; font-size: 20px;">Upload an image 👇</p>''',unsafe_allow_html=True)
file = st.file_uploader("Image",type=["png", "jpg", "jpeg"])
if file is not None:
image = Image.open(file)
st.image(
image,
caption=f"You amazing image has shape",
use_column_width=True,
)
img_array = np.array(image)
img = tf.image.resize(img_array, size=(256,256))
img = tf.expand_dims(img, axis=0)
img=img/255.
if st.button('Compute Intensity'):
intensity=pred_and_plot(loaded_model,img)
st.markdown("The intensity of your image in KNOTS is 👇")
st.success(intensity)
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