Cleanup

causenv.py

@@ -1,82 +0,0 @@
-import streamlit as st
-import torch
-from transformers import AutoTokenizer, AutoModelForTokenClassification
-
-# 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.title("Attribution of Causality")
-st.write("Tags indicators and causes. GER only (for now)")
-
-# Text input for sentences
-sentences_input = st.text_area("Sentences (one per line)", "\n".join([
-    "Laub könnte verantwortlich für den Klimawandel sein.",
-    "Nach dem Verursachergrundsatz spielt das keine Rolle.",
-    #"Backenzähne verursachen Artensterben.",
-    "Fußball führt zu Waldschäden.",
-    #"Das hängt mit vielen Faktoren zusammen.",
-    "Haustüren tragen zum Betonsterben bei.",
-    #"Autos stehen im verdacht, Bienensterben auszulösen.",
-    #"Lösen Straßen Waldsterben aus?"
-]))
-
-# 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 Sentences"):
-    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
-        reconstructed_tokens = []
-        reconstructed_labels = []
-        for token, label in zip(tokens, predicted_labels):
-            if token in ['[CLS]', '[SEP]']:  # Exclude special tokens
-                continue
-            if token.startswith("##"):
-                reconstructed_tokens[-1] += token[2:]  # Append subword
-            else:
-                reconstructed_tokens.append(token)
-                reconstructed_labels.append(label)
-
-        # Format output with square brackets
-        formatted_output = []
-        for token, label in zip(reconstructed_tokens, reconstructed_labels):
-            if label != "O":
-                # Use square brackets around label names
-                formatted_output.append(f"[{label}] <b>{token}</b> [/{label}]")
-            else:
-                formatted_output.append(token)
-
-        # Join tokens for display
-        output_sentence = " ".join(formatted_output)
-
-        # Display formatted sentence with Streamlit
-        st.write(f"**Original Sentence:** {sentence}")
-        st.markdown(f"**Labeled Output:** {output_sentence}", unsafe_allow_html=True)
-        st.write("---")