initial commit

app.py

@@ -1,9 +1,12 @@
 # Created by Hansi at 30/08/2023
+import os
+
 import nltk
 nltk.download('punkt')
 nltk.download('averaged_perceptron_tagger')
 
 import streamlit as st
+from PIL import Image
 from accord_nlp.information_extraction.convertor import entity_pairing, graph_building
 from accord_nlp.information_extraction.ie_pipeline import InformationExtractor
 
@@ -17,6 +20,7 @@ re_args = {
     "labels_list": ["selection", "necessity", "none", "greater", "part-of", "equal", "greater-equal", "less-equal", "not-part-of", "less"],
     "special_tags": ["<e1>", "<e2>"],  # Should be either begin_tag or end_tag
     "use_multiprocessing": False,
+    "process_count": 1
 }
 
 @st.cache_resource
@@ -37,8 +41,12 @@ with st.spinner(text="Initialising..."):
 
 
 def main():
-    st.sidebar.title("ACCORD-NLP")
-    st.sidebar.markdown("Extract information from text")
+    image = Image.open(os.path.join(os.path.dirname(__file__), 'accord_logo.png'))
+    st.sidebar.image(image)
+
+    # st.sidebar.title("ACCORD-NLP")
+    st.sidebar.header("Information Extractor")
+    st.sidebar.markdown("Extract entities and their relations from textual data")
     st.sidebar.markdown(
         "[code](https://github.com/Accord-Project/NLP-Framework)"
     )
@@ -47,39 +55,29 @@ def main():
 
     txt = st.text_area('Sentence')
 
-    # st.write(txt)
-
-    # with st.spinner(text="Processing..."):
-    # graph = ie.sentence_to_graph(txt)
-
     if txt:
         # preprocess
         sentence = ie.preprocess(txt)
-        st.write(sentence)
 
         # NER
         with st.spinner(text="Recognising entities..."):
             ner_predictions, ner_raw_outputs = ie.ner_model.predict([sentence])
 
-        st.write(ner_predictions)
-
         with st.spinner(text="Extracting relations..."):
             # pair entities to predict their relations
             entity_pair_df = entity_pairing(sentence, ner_predictions[0])
-            # st.write('entity paired')
 
             # relation extraction
             re_predictions, re_raw_outputs = ie.re_model.predict(entity_pair_df['output'].tolist())
             entity_pair_df['prediction'] = re_predictions
-            # st.write(re_predictions)
 
         with st.spinner(text="Building graph..."):
             # build graph
             graph = graph_building(entity_pair_df, view=False)
-            # st.success()
 
         st.header('Entity-Relation Representation')
-        st.graphviz_chart(graph)
+        # st.graphviz_chart(graph)
+        st.graphviz_chart(graph, use_container_width=True)
 
 
 if __name__ == '__main__':