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import streamlit as st
def home_ui():
# Streamlit app title
st.title("Transformer-Based Text Classification Project 🌟")
# Objective section
st.header("🔍 Objective")
st.write("""
The primary objective of this project is to classify text into positive or negative sentiment using a
**Transformer-based pre-trained model**. This model helps in understanding the sentiment of user-provided text,
which can be useful in applications like customer feedback analysis, review classification, and more.
""")
# Tools used
st.header("🛠️ Tools Used")
st.write("""
This project leverages the following tools and technologies:
- **Python**: For data preprocessing and backend logic.
- **Hugging Face Transformers**: For leveraging pre-trained Transformer models.
- **PyTorch**: For model operations and predictions.
- **Docker** (optional): To containerize the application for deployment.
- **Mlflow** : For model tracking and version control.
- **Git**: For version control and collaboration.
- **Streamlit**: To create an interactive and user-friendly UI.
""")
# Architecture section
st.header("🏗️ Project Architecture")
st.write("""
The architecture of this project can be summarized in the following flow:
""")
# Display architecture image
architecture_image_path = "project_flow.svg" # Replace with your image path
st.image(architecture_image_path, caption="Project Architecture", use_column_width=True)
# Footer or additional information
st.write("---")
st.write("""
💡 This application is designed to showcase the integration of **NLP** and **Machine Learning** with
an easy-to-use web interface. The predictions are generated in real-time, providing insights into text sentiments.
""")
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