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LegendaryToe
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523a420
1
Parent(s):
9753a3a
cp
Browse files
app.py
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import streamlit as st
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from transformers import pipeline
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# Load the SQLCoder model
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sql_generator = pipeline('text-generation', model='defog/sqlcoder')
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st.title('SQL Table Extractor')
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# Text input for SQL query
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user_sql = st.text_input("Enter your SQL statement", "SELECT * FROM my_table WHERE condition;")
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# Button to parse SQL
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if st.button('Extract Tables'):
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return ["my_table"] # Example output
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# import streamlit as st
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# from transformers import pipeline
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# # Load the SQLCoder model
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# sql_generator = pipeline('text-generation', model='defog/sqlcoder')
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# st.title('SQL Table Extractor')
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# # Text input for SQL query
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# user_sql = st.text_input("Enter your SQL statement", "SELECT * FROM my_table WHERE condition;")
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# # Button to parse SQL
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# if st.button('Extract Tables'):
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# # Generate SQL or parse directly
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# results = sql_generator(user_sql)
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# # Assuming results contain SQL, extract table names (this part may require custom logic based on output)
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# tables = extract_tables_from_sql(results)
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# # Display extracted table names
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# st.write('Extracted Tables:', tables)
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# def extract_tables_from_sql(sql):
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# # Dummy function: Implement logic to parse table names from SQL
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# return ["my_table"] # Example output
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import streamlit as st
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from transformers import pipeline
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# Load the NER model
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ner = pipeline("ner", model="dbmdz/bert-large-cased-finetuned-conll03-english", grouped_entities=True)
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st.title('Hello World NER Parser')
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# User input for text
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user_input = st.text_area("Enter a sentence to parse for named entities:", "John Smith lives in San Francisco.")
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# Parse entities
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if st.button('Parse'):
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entities = ner(user_input)
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# Display extracted entities
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for entity in entities:
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st.write(f"Entity: {entity['word']}, Entity Type: {entity['entity_group']}")
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