Create app.py

app.py

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+import streamlit as st
+import torch
+from safetensors.torch import load_file
+from transformers import AutoConfig, AutoTokenizer, AutoModel
+from ST2ModelV2_6 import ST2ModelV2
+from huggingface_hub import login
+import re
+import copy
+
+hf_token = st.secrets["HUGGINGFACE_TOKEN"]
+login(token=hf_token)
+
+
+
+# Load model & tokenizer once (cached for efficiency)
+@st.cache_resource
+def load_model():
+    
+    model_name = "anamargarida/Final"  
+    
+    config = AutoConfig.from_pretrained(model_name)
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    
+    class Args:
+        def __init__(self):
+            self.model_name = model_name
+            self.dropout = 0.1
+            self.signal_classification = True
+            self.pretrained_signal_detector = False
+
+    args = Args()
+
+    # Load the model directly from Hugging Face
+    model = ST2ModelV2.from_pretrained(model_name, config=config, args=args)
+
+    
+    return tokenizer, model
+
+# Load the model and tokenizer
+tokenizer, model = load_model()
+
+st.write("model_", model)
+st.write("model_weights", model.model)
+st.write("config", model.config)
+st.write("Signal_classifier_weights", model.signal_classifier.weight)
+st.write(model.model.embeddings.LayerNorm.weight)
+#st.write(model.model.encoder.layer.13.attention.self.value.weight)
+roberta_model = AutoModel.from_pretrained("roberta-large")
+st.write(roberta_model.embeddings.LayerNorm.weight)
+
+model.eval()  # Set model to evaluation mode
+def extract_arguments(text, tokenizer, model, beam_search=True):
+     
+    class Args:
+        def __init__(self):
+            self.signal_classification = True
+            self.pretrained_signal_detector = False
+        
+    args = Args()
+    inputs = tokenizer(text, return_tensors="pt")
+    
+    with torch.no_grad():
+        outputs = model(**inputs)
+
+    #st.write("Model output keys:", outputs.keys())
+
+    # Extract logits
+    start_cause_logits = outputs["start_arg0_logits"][0]
+    end_cause_logits = outputs["end_arg0_logits"][0]
+    start_effect_logits = outputs["start_arg1_logits"][0]
+    end_effect_logits = outputs["end_arg1_logits"][0]
+    start_signal_logits = outputs["start_sig_logits"][0]
+    end_signal_logits = outputs["end_sig_logits"][0]
+
+    #st.write("start_cause_logits", start_cause_logits)
+    #st.write("end_cause_logits", end_cause_logits)
+    #st.write("start_effect_logits", start_effect_logits)
+    #st.write("end_effect_logits", end_effect_logits)
+    #st.write("start_signal_logits", start_signal_logits)
+    #st.write("end_signal_logits", end_signal_logits)
+
+
+    # Set the first and last token logits to a very low value to ignore them
+    start_cause_logits[0] = -1e-4
+    end_cause_logits[0] = -1e-4
+    start_effect_logits[0] = -1e-4
+    end_effect_logits[0] = -1e-4
+    start_cause_logits[len(inputs["input_ids"][0]) - 1] = -1e-4
+    end_cause_logits[len(inputs["input_ids"][0]) - 1] = -1e-4
+    start_effect_logits[len(inputs["input_ids"][0]) - 1] = -1e-4
+    end_effect_logits[len(inputs["input_ids"][0]) - 1] = -1e-4
+
+    st.write("start_cause_logits", start_cause_logits)
+    st.write("end_cause_logits", end_cause_logits)
+    st.write("start_effect_logits", start_effect_logits)
+    st.write("end_effect_logits", end_effect_logits)
+    st.write("start_signal_logits", start_signal_logits)
+    st.write("end_signal_logits", end_signal_logits)
+    
+    # Beam Search for position selection
+    if beam_search:
+        indices1, indices2, _, _, _ = model.beam_search_position_selector(
+            start_cause_logits=start_cause_logits,
+            end_cause_logits=end_cause_logits,
+            start_effect_logits=start_effect_logits,
+            end_effect_logits=end_effect_logits,
+            topk=5
+        )
+        start_cause1, end_cause1, start_effect1, end_effect1 = indices1
+        start_cause2, end_cause2, start_effect2, end_effect2 = indices2
+    else:
+        start_cause1 = start_cause_logits.argmax().item()
+        end_cause1 = end_cause_logits.argmax().item()
+        start_effect1 = start_effect_logits.argmax().item()
+        end_effect1 = end_effect_logits.argmax().item()
+
+        start_cause2, end_cause2, start_effect2, end_effect2 = None, None, None, None
+
+    
+    has_signal = 1
+    if args.signal_classification:
+        if not args.pretrained_signal_detector:
+            has_signal = outputs["signal_classification_logits"].argmax().item()
+        else:
+            has_signal = signal_detector.predict(text=batch["text"])
+
+    if has_signal:
+        start_signal_logits[0] = -1e-4
+        end_signal_logits[0] = -1e-4
+    
+        start_signal_logits[len(inputs["input_ids"][0]) - 1] = -1e-4
+        end_signal_logits[len(inputs["input_ids"][0]) - 1] = -1e-4
+       
+        start_signal = start_signal_logits.argmax().item()
+        end_signal_logits[:start_signal] = -1e4
+        end_signal_logits[start_signal + 5:] = -1e4
+        end_signal = end_signal_logits.argmax().item()
+
+    if not has_signal:
+        start_signal = 'NA'
+        end_signal = 'NA'
+        
+
+    tokens = tokenizer.convert_ids_to_tokens(inputs["input_ids"][0])
+    token_ids = inputs["input_ids"][0]
+
+    #st.write("Token Positions, IDs, and Corresponding Tokens:")
+    #for position, (token_id, token) in enumerate(zip(token_ids, tokens)):
+        #st.write(f"Position: {position}, ID: {token_id}, Token: {token}")
+
+    st.write(f"Start Cause 1: {start_cause1}, End Cause: {end_cause1}")
+    st.write(f"Start Effect 1: {start_effect1}, End Cause: {end_effect1}")
+    st.write(f"Start Signal: {start_signal}, End Signal: {end_signal}")
+
+    def extract_span(start, end):
+        return tokenizer.convert_tokens_to_string(tokens[start:end+1]) if start is not None and end is not None else ""
+
+    cause1 = extract_span(start_cause1, end_cause1)
+    cause2 = extract_span(start_cause2, end_cause2)
+    effect1 = extract_span(start_effect1, end_effect1)
+    effect2 = extract_span(start_effect2, end_effect2)
+    if has_signal:
+        signal = extract_span(start_signal, end_signal)
+    if not has_signal:
+        signal = 'NA'
+    list1 = [start_cause1, end_cause1, start_effect1, end_effect1, start_signal, end_signal]
+    list2 = [start_cause2, end_cause2, start_effect2, end_effect2, start_signal, end_signal]
+    return cause1, cause2, effect1, effect2, signal, list1, list2
+
+def mark_text(original_text, span, color):
+    """Replace extracted span with a colored background marker."""
+    if span:
+        return re.sub(re.escape(span), f"<mark style='background-color:{color}; padding:2px; border-radius:4px;'>{span}</mark>", original_text, flags=re.IGNORECASE)
+    return original_text  # Return unchanged text if no span is found
+
+st.title("Causal Relation Extraction")
+input_text = st.text_area("Enter your text here:", height=300)
+beam_search = st.radio("Enable Beam Search?", ('No', 'Yes')) == 'Yes'
+
+
+if st.button("Extract1"):
+    if input_text:
+        cause1, cause2, effect1, effect2, signal, list1, list2 = extract_arguments(input_text, tokenizer, model, beam_search=beam_search)
+
+        cause_text1 = mark_text(input_text, cause1, "#FFD700")  # Gold for cause
+        effect_text1 = mark_text(input_text, effect1, "#90EE90")  # Light green for effect
+        signal_text = mark_text(input_text, signal, "#FF6347")  # Tomato red for signal
+
+        st.markdown(f"<span style='font-size: 24px;'><strong>Relation 1:</strong></span>", unsafe_allow_html=True)
+        st.markdown(f"**Cause:**<br>{cause_text1}", unsafe_allow_html=True)
+        st.markdown(f"**Effect:**<br>{effect_text1}", unsafe_allow_html=True)
+        st.markdown(f"**Signal:**<br>{signal_text}", unsafe_allow_html=True)
+
+        #st.write("List 1:", list1)
+
+        if beam_search:
+
+            cause_text2 = mark_text(input_text, cause2, "#FFD700")  # Gold for cause
+            effect_text2 = mark_text(input_text, effect2, "#90EE90")  # Light green for effect
+            signal_text = mark_text(input_text, signal, "#FF6347")  # Tomato red for signal
+    
+            st.markdown(f"<span style='font-size: 24px;'><strong>Relation 2:</strong></span>", unsafe_allow_html=True)
+            st.markdown(f"**Cause:**<br>{cause_text2}", unsafe_allow_html=True)
+            st.markdown(f"**Effect:**<br>{effect_text2}", unsafe_allow_html=True)
+            st.markdown(f"**Signal:**<br>{signal_text}", unsafe_allow_html=True)
+
+            #st.write("List 2:", list2)
+    else:
+        st.warning("Please enter some text before extracting.")