app.py

import streamlit as st
import spacy
from transformers import BertTokenizer, BertModel
from PIL import Image

#LOAD MODEL SECTION#
nlp = spacy.load("./models/en_core_web_sm")

bert_tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
bert_model = BertModel.from_pretrained("bert-base-uncased")
bert_model.eval()


























st.title("SpaGAN Demo")
st.write("Enter a text, and the system will highlight the geo-entities within it.")

# Define a color map and descriptions for different entity types
COLOR_MAP = {
    'FAC': ('red', 'Facilities (e.g., buildings, airports)'),
    'ORG': ('blue', 'Organizations (e.g., companies, institutions)'),
    'LOC': ('purple', 'Locations (e.g., mountain ranges, water bodies)'),
    'GPE': ('green', 'Geopolitical Entities (e.g., countries, cities)')
}

# Display the color key with descriptions
st.write("**Color Key:**")
for label, (color, description) in COLOR_MAP.items():
    st.markdown(f"- **{label}**: <span style='color:{color}'>{color}</span> - {description}", unsafe_allow_html=True)

# Text input
user_input = st.text_area("Input Text", height=200)

# Process the text when the button is clicked
if st.button("Highlight Geo-Entities"):
    if user_input.strip():
        # Process the text using spaCy
        doc = nlp(user_input)

        # Highlight geo-entities with different colors
        highlighted_text = user_input
        for ent in reversed(doc.ents):
            if ent.label_ in COLOR_MAP:
                color = COLOR_MAP[ent.label_][0]
                highlighted_text = (
                    highlighted_text[:ent.start_char] +
                    f"<span style='color:{color}; font-weight:bold'>{ent.text}</span>" + 
                    highlighted_text[ent.end_char:]
                )

        # Display the highlighted text with HTML support
        st.markdown(highlighted_text, unsafe_allow_html=True)
    else:
        st.error("Please enter some text.")