updating app

app.py

@@ -10,6 +10,8 @@ from PIL import Image
 from accord_nlp.information_extraction.convertor import entity_pairing, graph_building
 from accord_nlp.information_extraction.ie_pipeline import InformationExtractor
 
+from trubrics.integrations.streamlit import FeedbackCollector
+
 ner_args = {
     "labels_list": ["O", "B-quality", "B-property", "I-property", "I-quality", "B-object", "I-object", "B-value", "I-value"],
     "use_multiprocessing": False,
@@ -40,6 +42,14 @@ with st.spinner(text="Initialising..."):
     ie = init()
 
 
+collector = FeedbackCollector(
+    # component_name="default",
+    email=st.secrets["TRUBRICS_EMAIL"],
+    password=st.secrets["TRUBRICS_PASSWORD"],
+    project="test"
+)
+
+
 def main():
     image = Image.open(os.path.join(os.path.dirname(__file__), 'accord_logo.png'))
     st.sidebar.image(image)
@@ -48,36 +58,68 @@ def main():
     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)"
+        "[codebase](https://github.com/Accord-Project/NLP-Framework)"
+    )
+    st.sidebar.markdown(
+        "[models](https://huggingface.co/ACCORD-NLP)"
     )
 
+    if 'text' not in st.session_state:
+        st.session_state['text'] = ''
+    if 'graph' not in st.session_state:
+        st.session_state['graph'] = None
+
     st.header("Input a sentence")
 
     txt = st.text_area('Sentence')
 
     if txt:
-        # preprocess
-        sentence = ie.preprocess(txt)
-
-        # NER
-        with st.spinner(text="Recognising entities..."):
-            ner_predictions, ner_raw_outputs = ie.ner_model.predict([sentence])
-
-        with st.spinner(text="Extracting relations..."):
-            # pair entities to predict their relations
-            entity_pair_df = entity_pairing(sentence, ner_predictions[0])
-
-            # relation extraction
-            re_predictions, re_raw_outputs = ie.re_model.predict(entity_pair_df['output'].tolist())
-            entity_pair_df['prediction'] = re_predictions
-
-        with st.spinner(text="Building graph..."):
-            # build graph
-            graph = graph_building(entity_pair_df, view=False)
-
-        st.header('Entity-Relation Representation')
-        # st.graphviz_chart(graph)
-        st.graphviz_chart(graph, use_container_width=True)
+        if txt == st.session_state['text']:
+            st.header('Entity-Relation Representation')
+            st.graphviz_chart(st.session_state['graph'], use_container_width=True)
+
+        else:
+            st.session_state['text'] = txt
+            st.session_state['graph'] = None
+
+            # preprocess
+            sentence = ie.preprocess(txt)
+
+            # NER
+            with st.spinner(text="Recognising entities..."):
+                ner_predictions, ner_raw_outputs = ie.ner_model.predict([sentence])
+
+            with st.spinner(text="Extracting relations..."):
+                # pair entities to predict their relations
+                entity_pair_df = entity_pairing(sentence, ner_predictions[0])
+
+                # relation extraction
+                re_predictions, re_raw_outputs = ie.re_model.predict(entity_pair_df['output'].tolist())
+                entity_pair_df['prediction'] = re_predictions
+
+            with st.spinner(text="Building graph..."):
+                # build graph
+                graph = graph_building(entity_pair_df, view=False)
+
+            st.header('Entity-Relation Representation')
+            # st.graphviz_chart(graph)
+            st.graphviz_chart(graph, use_container_width=True)
+
+            st.session_state['graph'] = graph
+
+        st.divider()
+        st.write("Does this prediction look correct?")
+        collector.st_feedback(
+            component="default",
+            feedback_type="thumbs",
+            model="accord-nlp-ie",
+            align="flex-start",
+            metadata={
+                "sentence": txt
+            },
+            open_feedback_label="[Optional] Provide additional feedback",
+            single_submit=False
+        )
 
 
 if __name__ == '__main__':

requirements.txt

@@ -2,4 +2,5 @@
 --find-links https://download.pytorch.org/whl/torch_stable.html
 torch==2.0.1+cpu
 streamlit==1.26.0
-accord-nlp==0.1.8
+accord-nlp==0.1.8
+trubrics[streamlit]==1.5.1