cp

app.py

@@ -1,17 +1,25 @@
 import streamlit as st
 from transformers import pipeline
 
-# Load your model; this example uses the GPT-2 model for text generation.
-generator = pipeline('text-generation', model='gpt2')
+# Load the SQLCoder model
+sql_generator = pipeline('text-generation', model='defog/sqlcoder-7b-2')
 
-st.title('Hugging Face Model Integration')
+st.title('SQL Table Extractor')
 
-# Text input
-user_input = st.text_input("Type a sentence to complete", "Streamlit is ")
+# Text input for SQL query
+user_sql = st.text_input("Enter your SQL statement", "SELECT * FROM my_table WHERE condition;")
+
+# Button to parse SQL
+if st.button('Extract Tables'):
+    # Generate SQL or parse directly
+    results = sql_generator(user_sql)
+    # Assuming results contain SQL, extract table names (this part may require custom logic based on output)
+    tables = extract_tables_from_sql(results)
+    
+    # Display extracted table names
+    st.write('Extracted Tables:', tables)
+
+def extract_tables_from_sql(sql):
+    # Dummy function: Implement logic to parse table names from SQL
+    return ["my_table"]  # Example output
 
-# Generate text button
-if st.button('Generate'):
-    # Generate text
-    result = generator(user_input, max_length=50, num_return_sequences=1)
-    # Display the generated text
-    st.text_area("Generated Text", result[0]['generated_text'], height=150)