Upload app.py

app.py

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+import streamlit as st
+import spacy
+from spacy import displacy
+
+# Load spaCy model
+nlp = spacy.load("en_core_web_sm")
+
+def visualize_entities(doc):
+    # Create a list of (start, end, label) tuples for the named entities
+    entities = [(ent.start_char, ent.end_char, ent.label_) for ent in doc.ents]
+
+    # Highlight named entities in the input text
+    html = displacy.render(doc, style="ent", options={"ents": entities})
+    st.markdown(html, unsafe_allow_html=True)
+
+def filter_entities(entities, selected_labels):
+    if not selected_labels:
+        return entities
+
+    filtered_entities = []
+    for entity, label in entities:
+        if label in selected_labels:
+            filtered_entities.append((entity, label))
+    return filtered_entities
+
+def main():
+    st.title("Named Entity Recognition App")
+    st.write("Enter a text and get named entities!")
+
+    user_input = st.text_area("Enter text:", height=200)
+    visualize = st.checkbox("Visualize Entities")
+    filter_labels = st.multiselect("Filter Entities by Label:", options=["PERSON", "ORG", "GPE", "DATE"])
+
+    if st.button("Analyze"):
+        doc = nlp(user_input)
+        entities = [(ent.text, ent.label_) for ent in doc.ents]
+
+        if visualize:
+            visualize_entities(doc)
+
+        filtered_entities = filter_entities(entities, filter_labels)
+
+        st.write("Named Entities:")
+        for entity, label in filtered_entities:
+            st.write(f"Text: {entity}, Entity: {label}")
+
+if __name__ == "__main__":
+    main()