| ''' |
| Author : Rupesh Garsondiya |
| github : @Rupeshgarsondiya |
| Organization : L.J University |
| ''' |
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| import http |
| import streamlit as st |
| from PIL import Image |
| from train import * |
| from test import * |
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| |
| class Main: |
| def __init__(self) -> None: |
| pass |
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| def run(self): |
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| st.markdown( |
| """ |
| <style> |
| .img-rounded { |
| border-radius: 15px; |
| width: 400px; /* Adjust the width as needed */ |
| } |
| |
| </style> |
| <div style="text-align: center;"> |
| <img src="https://files.oaiusercontent.com/file-j5d0noa13fNCzctwMzacm7k9?se=2024-10-15T17%3A31%3A03Z&sp=r&sv=2024-08-04&sr=b&rscc=max-age%3D604800%2C%20immutable%2C%20private&rscd=attachment%3B%20filename%3D393b3cd9-2488-4f60-a1d7-52fe26428c8f.webp&sig=JSGn0KwxflHJ9MPWGzjpDn%2BzUTJxnMrSN2%2BrrTMhoLg%3D" class="img-rounded",><br> |
| |
| """, |
| unsafe_allow_html=True |
| ) |
| st.write() |
| st.write() |
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| t = test() |
| t.predict_data() |
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| st.markdown("[GitHub](https://github.com/Rupeshgarsondiya/Project-1) | <a href='https://www.linkedin.com/in/rupesh-garsondiya-919817275/' target='_blank'>Linkedin</a>",unsafe_allow_html=True) |
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| st.markdown("", unsafe_allow_html=True) |
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| st.markdown("<hr>", unsafe_allow_html=True) |
| st.markdown( |
| "<b><p style='text-align: center; font-size: 12px;'>© 2024 Rupesh Garsondiya. All Rights Reserved.</p>", |
| unsafe_allow_html=True |
| ) |
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| |
| if __name__ == "__main__": |
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| st.markdown("<h1 style='text-align: center;'><b>User behaviour classification on user Behaviour Dataset</b></h1>", unsafe_allow_html=True) |
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| st.markdown("<p ><b> - About this project :</b></p>",unsafe_allow_html=True) |
| st.write(' - This project is a simple web application that uses a machine learning model to classify user behavior into different categories.') |
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| st.write(' - The model is trained on a dataset of user behavior and can be used to predict the behavior of a new user based on their mobile data.') |
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| run_obj = Main() |
| run_obj.run() |
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| else : |
| print("This is a Streamlit app. Please run it using `streamlit run app.py `") |