app.py

import streamlit as st
import spacy
import torch
from transformers import BertTokenizer, BertModel
from transformers.models.bert.modeling_bert import BertForMaskedLM

from models.spabert.models.spatial_bert_model import SpatialBertConfig, SpatialBertForMaskedLM, SpatialBertModel
from models.spabert.utils.common_utils import load_spatial_bert_pretrained_weights
from models.spabert.datasets.osm_sample_loader import PbfMapDataset
from torch.utils.data import DataLoader

from PIL import Image

device = torch.device('cpu')

#Spacy Initialization Section
nlp = spacy.load("./models/en_core_web_sm")

#BERT Initialization Section
bert_tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
bert_model = BertModel.from_pretrained("bert-base-uncased")
bert_model.to(device)
bert_model.eval()

#SpaBERT Initialization Section
data_file_path = 'models/spabert/datasets/SPABERT_finetuning_data_combined.json'
pretrained_model_path = 'models/spabert/datasets/fine-spabert-base-uncased-finetuned-osm-mn.pth'

config = SpatialBertConfig()
config.output_hidden_states = True
spaBERT_model = SpatialBertForMaskedLM(config)

pre_trained_model = torch.load(pretrained_model_path, map_location=torch.device('cpu'))
spaBERT_model.load_state_dict(bert_model.state_dict(), strict = False)
spaBERT_model.load_state_dict(pre_trained_model, strict=False)

spaBERT_model.to(device)
spaBERT_model.eval()

#Load data using SpatialDataset
spatialDataset = PbfMapDataset(data_file_path = data_file_path,
                                        tokenizer = bert_tokenizer,
                                        max_token_len = 256,              #Originally 300
                                        #max_token_len = max_seq_length,              #Originally 300
                                        distance_norm_factor = 0.0001,
                                        spatial_dist_fill = 20,
                                        with_type = False,
                                        sep_between_neighbors = True,    #Initially false, play around with this potentially?
                                        label_encoder = None,             #Initially None, potentially change this because we do have real/fake reviews.
                                        mode = None)                      #If set to None it will use the full dataset for mlm

data_loader = DataLoader(spatialDataset, batch_size=1, num_workers=0, shuffle=False, pin_memory=False, drop_last=False) #issue needs to be fixed with num_workers not stopping after finished

#Pre-aquire the SpaBERT embeddings for all geo-entities within our dataset
def process_entity(batch, model, device):
    input_ids = batch['masked_input'].to(device)
    attention_mask = batch['attention_mask'].to(device)
    position_list_x = batch['norm_lng_list'].to(device)
    position_list_y = batch['norm_lat_list'].to(device)
    sent_position_ids = batch['sent_position_ids'].to(device)
    pseudo_sentence = batch['pseudo_sentence'].to(device)

    # Convert tensor to list of token IDs, and decode them into a readable sentence
    pseudo_sentence_decoded = tokenizer.decode(pseudo_sentence[0].tolist(), skip_special_tokens=False)

    with torch.no_grad():
        outputs = spaBERT_model(#input_ids=input_ids,
                        input_ids=pseudo_sentence,
                        attention_mask=attention_mask,
                        sent_position_ids=sent_position_ids,
                        position_list_x=position_list_x,
                        position_list_y=position_list_y)
                        #NOTE: we are ommitting the pseudo_sentence here. Verify that this is correct

    embeddings = outputs.hidden_states[-1].to(device)

    # Extract the [CLS] token embedding (first token)
    embedding = embeddings[:, 0, :].detach()  # [batch_size, hidden_size]

    #pivot_token_len = batch['pivot_token_len'].item()
    #pivot_embeddings = embeddings[:, :pivot_token_len, :]

    #return pivot_embeddings.cpu().numpy(), input_ids.cpu().numpy()
    return embedding.cpu().numpy(), input_ids.cpu().numpy()

all_embeddings = []
for batch in (data_loader):
  embeddings, input_ids = process_entity(batch, model, device)
  all_embeddings.append(embeddings)

st.write("SpaBERT Embedding shape:", all_embeddings[0].shape)
st.write("SpaBERT Embedding:", all_embeddings[0])



#Get BERT Embedding for review
def get_bert_embedding(review_text):
    #tokenize review
    inputs = bert_tokenizer(review_text, return_tensors='pt', padding=True, truncation=True).to(device)
    
    # Forward pass through the BERT model
    with torch.no_grad():
        outputs = bert_model(**inputs)

    # Extract embeddings from the last hidden state
    embeddings = outputs.last_hidden_state[:, 0, :].detach()     #CLS Token
    return embeddings





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
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)

# Define example reviews for testing
example_reviews = {
    "Review 1": "I visited the Empire State Building in New York last summer, and it was amazing!",
    "Review 2": "Google, headquartered in Mountain View, is a leading tech company in the United States.",
}

# Dropdown for selecting an example review
user_input = st.selectbox("Select an example review", options=list(example_reviews.keys()))

# Get the selected review text
selected_review = example_reviews[user_input]

# Process the text when the button is clicked
if st.button("Highlight Geo-Entities"):
    if selected_review.strip():
        bert_embedding = get_bert_embedding(selected_review)
        # Debug: Print the shape of the embeddings
        st.write("Embedding Shape:", bert_embedding.shape)
        
        # Debug: Print the embeddings themselves (optional)
        st.write("Embeddings:", bert_embedding)
        
        # Process the text using spaCy
        doc = nlp(selected_review)
        
        # Highlight geo-entities with different colors
        highlighted_text = selected_review
        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 select a review.")