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