Update app.py

app.py

@@ -1,5 +1,6 @@
 import streamlit as st
 from PIL import Image
+import spacy
 
 st.set_page_config(page_title="FACTOID: FACtual enTailment fOr hallucInation Detection", layout="wide")
 st.title('Welcome to :blue[FACTOID] ') 
@@ -33,4 +34,50 @@ label_names = ["support", "neutral", "refute"]
 prediction = {name: round(float(pred) * 100, 1) for pred, name in zip(prediction, label_names)}
 print(prediction)
 
+
+from sentence_transformers import CrossEncoder
+model1 = CrossEncoder('cross-encoder/nli-deberta-v3-xsmall')
+scores1 = model.predict([(sentence1, sentence2)])
+
+#Convert scores to labels
+label_mapping = ['contradiction', 'entailment', 'neutral']
+labels = [label_mapping[score_max] for score_max in scores1.argmax(axis=1)]
+labels
+
+
+
+def extract_person_names(sentence):
+    """
+    Extract person names from a sentence using spaCy's named entity recognition.
+    
+    Parameters:
+        sentence (str): Input sentence.
+    
+    Returns:
+        list: List of person names extracted from the sentence.
+    """
+    # Load English language model
+    nlp = spacy.load("en_core_web_sm")
+    
+    # Process the sentence using spaCy
+    doc = nlp(sentence)
+    
+    # Extract person names
+    person_names = [entity.text for entity in doc.ents if entity.label_ == 'PERSON']
+    
+    return person_names[0]
+
+person_name1 = extract_person_names(sentence1)
+person_name2 = extract_person_names(sentence2)
+
 st.write("Result:", prediction)
+
+col1, col2 = st.beta_columns(2)
+
+with col1:
+    st.write("Without Factual Entailment:",prediction)
+
+with col2:
+    st.write("Factual Entailment:",labels)
+    st.write(f"{person_name1}::{person_name2}")
+