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import streamlit as st
import torch
from transformers import AutoTokenizer, AutoModelForTokenClassification
from annotated_text import annotated_text
# Load the trained model and tokenizer
model_directory = "norygano/causalBERT"
tokenizer = AutoTokenizer.from_pretrained(model_directory, add_prefix_space=True)
model = AutoModelForTokenClassification.from_pretrained(model_directory)
# Set model to evaluation mode
model.eval()
# Define the label map
label_map = {0: "O", 1: "B-INDICATOR", 2: "I-INDICATOR", 3: "B-CAUSE", 4: "I-CAUSE"}
# Streamlit App
st.markdown(
"""
<div style="display: flex; align-items: center; justify-content: left; font-size: 60px; font-weight: bold;">
<span>CAUSEN</span>
<span style="transform: rotate(270deg); display: inline-block; margin-left: 5px;">V</span>
</div>
""",
unsafe_allow_html=True
)
# Add a description with a link to the model
st.write("Tags indicators and causes in explicit attribution of causality. GER only (atm)")
# Text input for sentences with italic placeholder text
sentences_input = st.text_area("*Sentences (one per line)*", "\n".join([
"Laub könnte verantwortlich für den Klimawandel sein.",
"Fußball führt zu Waldschäden.",
"Haustüren tragen zum Betonsterben bei.",
])
, placeholder="Your Sentences here.")
st.markdown("[Model](https://huggingface.co/norygano/causalBERT)")
# Split the input text into individual sentences
sentences = [sentence.strip() for sentence in sentences_input.splitlines() if sentence.strip()]
# Button to run the model
if st.button("Analyze"):
for sentence in sentences:
# Tokenize the sentence
inputs = tokenizer(sentence, return_tensors="pt", truncation=True, padding=True)
# Run inference
with torch.no_grad():
outputs = model(**inputs)
# Get the logits and predicted label IDs
logits = outputs.logits
predicted_label_ids = torch.argmax(logits, dim=2)
# Convert token IDs back to tokens
tokens = tokenizer.convert_ids_to_tokens(inputs['input_ids'][0])
# Map label IDs to human-readable labels
predicted_labels = [label_map[label_id.item()] for label_id in predicted_label_ids[0]]
# Reconstruct words from subwords and prepare for annotated_text
annotations = []
current_word = ""
current_label = "O"
for token, label in zip(tokens, predicted_labels):
if token in ['[CLS]', '[SEP]']: # Exclude special tokens
continue
if token.startswith("##"):
# Append subword without "##" prefix to the current word
current_word += token[2:]
else:
# If we have accumulated a word, add it to annotations with a space
if current_word:
if current_label != "O":
annotations.append((current_word, current_label))
else:
annotations.append(current_word)
annotations.append(" ") # Add a space between words
# Start a new word
current_word = token
current_label = label
# Add the last accumulated word
if current_word:
if current_label != "O":
annotations.append((current_word, current_label))
else:
annotations.append(current_word)
# Display annotated text
st.write(f"**Sentence:** {sentence}")
annotated_text(*annotations)
st.write("---")
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