cp

app.py

@@ -23,20 +23,39 @@
 #     # Dummy function: Implement logic to parse table names from SQL
 #     return ["my_table"]  # Example output
 
+# import streamlit as st
+# from transformers import pipeline
+
+# # Load the NER model
+# ner = pipeline("ner", model="dbmdz/bert-large-cased-finetuned-conll03-english", grouped_entities=True)
+
+# st.title('Hello World NER Parser')
+
+# # User input for text
+# user_input = st.text_area("Enter a sentence to parse for named entities:", "John Smith lives in San Francisco.")
+
+# # Parse entities
+# if st.button('Parse'):
+#     entities = ner(user_input)
+#     # Display extracted entities
+#     for entity in entities:
+#         st.write(f"Entity: {entity['word']}, Entity Type: {entity['entity_group']}")
+
+
 import streamlit as st
 from transformers import pipeline
 
-# Load the NER model
-ner = pipeline("ner", model="dbmdz/bert-large-cased-finetuned-conll03-english", grouped_entities=True)
+# Load CodeBERT model as a feature extractor
+# (Note: You may need to adjust the task if using CodeBERT for other specific purposes)
+codebert = pipeline("feature-extraction", model="microsoft/codebert-base")
 
-st.title('Hello World NER Parser')
+st.title('CodeBERT Feature Extractor')
 
 # User input for text
-user_input = st.text_area("Enter a sentence to parse for named entities:", "John Smith lives in San Francisco.")
-
-# Parse entities
-if st.button('Parse'):
-    entities = ner(user_input)
-    # Display extracted entities
-    for entity in entities:
-        st.write(f"Entity: {entity['word']}, Entity Type: {entity['entity_group']}")
+user_input = st.text_area("Enter code or text to extract features:", "SELECT * FROM users;")
+
+# Extract features
+if st.button('Extract Features'):
+    features = codebert(user_input)
+    # Display extracted features (example: show size of feature vector for demonstration)
+    st.write('Number of features extracted:', len(features[0][0]))