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import streamlit as st
import base64
# Setting page config
st.set_page_config(page_title="Home", page_icon=":house:", layout="wide")
#-------------------------------- Background and custom CSS -------------------------------------#
#impliment background formating
def set_bg_hack(main_bg):
# set bg name
main_bg_ext = "jpg"
st.markdown(
f"""
<style>
.stApp {{
background: url(data:image/{main_bg_ext};base64,{base64.b64encode(open(main_bg, "rb").read()).decode()});
background-repeat: no-repeat;
background-position: right 50% bottom 95% ;
background-size: cover;
background-attachment: scroll;
}}
</style>
""",
unsafe_allow_html=True,
)
set_bg_hack("images/dark_bg_home.jpg")
# Setting custom css
css = f"""
<style>
[data-testid="stHeader"] {{
background: rgba(0,0,0,0);
}}
</style>
"""
st.markdown(css, unsafe_allow_html=True)
#-------------------------------- Sidebar Modification -------------------------------------#
# Setting logo on sidebar
st.sidebar.image("images/logo.png", caption="About this app")
st.sidebar.markdown("##")
st.sidebar.markdown("##")
st.sidebar.markdown("##")
st.sidebar.markdown("[](https://github.com/prithush92)")
st.sidebar.markdown("[](https://www.linkedin.com/in/prithush92/)")
# Page title
st.title("Airline Passenger Feedback Portal βοΈ")
# Description
st.markdown("""
Welcome to the Airline Passenger Feedback Portal! This app analyzes passenger ratings and reviews and predicts how likely is a passenger to recommend the airline. \n
""")
# Features
st.header("π© Features", divider="red")
st.markdown("""
- **Analyze Passenger Ratings**: A Machine Learning model is deployed to predict customer recommendation status based on Ratings.
- **Review Sentiment Analysis**: Customer Reviews are deeply analyzed and sentiment analysis is performed to determine the sentiment of the review.
- **Overall Recommendation Prediction**: Finally using both the Ratings and Review Sentiment, Overall Recommendation Status of the user is predicted.
- **Airline Reviews Dashboard**: For the convenience of Airline Companies, a dashboard is designed to easily visualize Passenger Ratings
and Important Keywords in both Positive and Negative Reviews.
""")
# Technologies Used
st.header("π Technologies Used", divider="blue")
st.markdown("""
- **SQLite**: Database management system for storing and retrieving passenger feedback data.
- **Pandas**: Data manipulation and analysis library for handling datasets.
- **NumPy**: Numerical computing library for performing mathematical operations.
- **Matplotlib**: Visualization library for creating insightful plots and charts.
- **WordCloud**: Visualization tool for generating word clouds from textual data.
- **TensorFlow**: Deep learning framework for building and training machine learning models.
""")
# About the Developer
st.header("π¨π»βπ» About the Developer", divider="green")
st.write("""
This app is developed by **Prithu Sharma**.
""")
st.markdown(
"""
[](https://github.com/prithush92)
[](https://www.linkedin.com/in/prithush92/)
"""
)
# Footer
st.markdown("---")
st.write("Explore the app and make informed decisions based on passenger feedback!") |