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Update app.py
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app.py
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<h2>Project Overview</h2>
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<p>Detailed explanation of the project, its goals, and key functionalities.</p>
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<h2>Installation</h2>
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<pre><code>
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# Step-by-step instructions on how to install your project.
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pip install your-project
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</code></pre>
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<h2>Usage</h2>
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<pre><code>
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# Instructions on how to use your project with code snippets.
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from your_project import YourModel
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model = YourModel()
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result = model.predict("Sample input")
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print(result)
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</code></pre>
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<h2>Dataset and Model Information</h2>
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<p>Information about the dataset and model architecture.</p>
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<h2>Results and Performance</h2>
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<p>Metrics, visualizations, and comparisons showcasing the performance of your model.</p>
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<h2>How to Contribute</h2>
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<p>Guidelines for contributing to your project.</p>
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<h2>License</h2>
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<p>The license for your project.</p>
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<h2>Citation</h2>
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<p>How to cite your work if applicable.</p>
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</body>
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</html>
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"""
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@app.route('/')
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def home():
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return render_template_string(intro_content)
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if __name__ == '__main__':
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app.run(debug=True)
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# import streamlit as st
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# import pandas as pd
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# import joblib
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# import streamlit as st
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# import eda
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# import models
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# page = st.sidebar.selectbox(label='Select Page:', options=['Home Page', 'Exploration Data Analysis', 'Model Prediksi'])
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# if page == 'Home Page':
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# st.header('Welcome Page')
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# st.write('')
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# st.write('Milestone 2')
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# st.write('Nama : Ryan Trisnadi')
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# st.write('Batch : ')
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# st.write('Tujuan Milestone : Prediksi harga rumah di Melbourne')
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# st.write('')
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# st.caption('Silahkan pilih menu lain di Select Box pada sebelah kiri layar anda untuk memulai!')
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# st.write('')
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# st.write('')
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# with st.expander("Latar Belakang"):
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# st.caption('Proyek ini dibuat untuk mengilustrasikan dan memproyek kedepan harga rumah di kota Melbourne, Australia. Kita akan menguji dengan Linear regression
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# dengan beberapa test algorithma seperti SVM, Decision Tree, Random Forest, dan Boosting. Oleh karena itu, kita akan memilih variable "price" atau harga
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# rumah sebagai target. Dengan mengunakan Pipeline di setiap proses dari Feature Engineering, kita bisa membuat proses lebih efisien dan mencegah data leak.
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# Dataset tersebut memiliki beberapa faktor seperti alamat, kode pos, harga rumah, lokasi lingkungan, kamar, kamar mandi, garasi, besar tanah, umur dan
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# lain-lain'.)
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# elif page == 'Exploration Data Analysis':
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# eda.run()
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# else:
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# models.run()
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import streamlit as st
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import pandas as pd
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import joblib
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import streamlit as st
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import eda
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import models
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page = st.sidebar.selectbox(label='Select Page:', options=['Home Page', 'Exploration Data Analysis', 'Model Prediksi'])
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if page == 'Home Page':
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st.header('Welcome Page')
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st.write('')
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st.write('GC7')
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st.write('Nama : Ryan Trisnadi')
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st.write('Batch : ')
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st.write('Tujuan GC&: Prediksi harga sentiment review di IMDB')
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st.write('')
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st.caption('Proyek ini dibuat untuk mengevaluasi konsep Natural Language Processing (NLP) dengan dataset dari Kaggle tentang IMDB movie ratings.
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Kaggle dataset tersebut memiliki 2 kolom: "sentiment" dan "review" yang terdiri dari banyak string values yang perlu di data clean dan
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dinterpretasi jadi data berguna. Model LSTM dan GRU akan gunakan untuk prediksi kata-kata apa saja yang akan tertampil terbanyak!')
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st.write('')
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