Create .app.py

.app.py

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+import streamlit as st
+import spacy
+from spacy import displacy
+from collections import Counter
+
+# Load the English language model
+nlp = spacy.load("en_core_web_sm")
+
+def process_text(text):
+    # Process the text using the spaCy pipeline
+    doc = nlp(text)
+
+    # Extract the named entities
+    entities = [(ent.text, ent.label_) for ent in doc.ents]
+
+    # Count the occurrences of each entity type
+    entity_counts = Counter([ent[1] for ent in entities])
+
+    # Visualize the named entities using displacy
+    ent_html = displacy.render(doc, style="ent")
+
+    return ent_html, dict(entity_counts)
+
+def main():
+    st.set_page_config(page_title="Named Entity Extraction")
+    st.title("Named Entity Extraction")
+    st.write("This app uses spaCy to extract named entities from the given text and visualize them.")
+
+    text = st.text_area("Text to Process", height=200, placeholder="Enter the text you want to analyze")
+
+    if st.button("Process Text"):
+        ent_html, entity_counts = process_text(text)
+        st.markdown(ent_html, unsafe_allow_html=True)
+        st.subheader("Entity Type Counts")
+        st.dataframe(entity_counts, use_container_width=True)
+
+if __name__ == "__main__":
+    main()