import streamlit as st

st.sidebar.markdown('''
# Sections
- [How to use](#how-to-use)
''', unsafe_allow_html=True)

st.markdown('''
# ProtHGT User Guide
''')

import streamlit as st

st.markdown("""
ProtHGT is a web-based tool for **automated protein function prediction** using heterogeneous graph transformers and knowledge graphs. Follow the steps below to generate predictions for your proteins.
""")

st.subheader("1. Select Proteins")
st.markdown("""
In the **sidebar**, choose how to input your proteins:
- **Search Proteins**: Select or search UniProt IDs from the available dataset.
- **Upload a File**: Upload a text file (.txt) containing UniProt IDs (one per line, max 100).    
""")
st.warning("⚠️ Currently, our system can only generate predictions for proteins that are already included in our knowledge graph. Real-time retrieval of relationship data from external source databases is not yet supported. We are actively working on integrating this capability in future updates. Stay tuned!")
st.info("📥 Selected proteins can be downloaded as a txt file.")


st.subheader("2. Choose Gene Ontology (GO) Category")
st.markdown("""
Select which **Gene Ontology (GO) sub-ontology** to use for function prediction:
- **Molecular Function (MF)** – Biochemical activity of the protein
- **Biological Process (BP)** – Biological roles and pathways
- **Cellular Component (CC)** – Location within the cell
- **All Categories** – Runs predictions for all three categories
""")

st.subheader("3. Generate Predictions")
st.markdown("""
Click **"Generate Predictions"** to start the analysis. The model will process the selected proteins and return predicted functional annotations.

🔄 **Processing time**: A few minutes (depending on input size).
""")

st.subheader("4. View and Filter Results")
st.markdown("""
Once predictions are generated, use the filter options to refine the output:
- **Filter by Protein** (UniProt ID)
- **Filter by GO Category**
- **Set Probability Range** (Adjust prediction confidence thresholds)

Results are displayed in a sortable table, with **probabilities** indicating prediction confidence.
""")

st.info("📥 Filtered predictions can be downloaded as a CSV file.")

st.subheader("5. Start a New Query")
st.markdown("""
After generating predictions, you can start a new query by selecting different options from the sidebar.
""")

st.subheader("🚀 Running Locally?")
st.markdown("""
For **larger datasets** or **custom analyses**, you can run ProtHGT locally using our [**GitHub repository**](https://github.com/HUBioDataLab/ProtHGT).
""")