Update app.py

NER for Boycott dataset

app.py

@@ -1,38 +1,26 @@
 import streamlit as st
-import spacy
-from spacy import displacy
-from collections import Counter
+import json
 
-# Load the English language model
-nlp = spacy.load("en_core_web_sm")
+# Load the data from the JSON file
+with open('data.json', 'r') as f:
+    data = json.load(f)
 
-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 display_brand_info(parent_company):
+    brand_info = ''
+    for brand_id, brand in data['brands'].items():
+        if parent_company.lower() in brand['description'].lower():
+            brand_info += f"# {brand['name']}\n{brand['description']}\n---\n"
+    return brand_info
 
 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.")
+    st.set_page_config(page_title="Brand Information")
+    st.title("Brand Information")
 
-    text = st.text_area("Text to Process", height=200, placeholder="Enter the text you want to analyze")
+    parent_company = st.text_input("Enter Parent Company")
 
-    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 parent_company:
+        brand_info = display_brand_info(parent_company)
+        st.markdown(brand_info)
 
 if __name__ == "__main__":
     main()