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app.py

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+# app.py
+
+import streamlit as st
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+
+def main():
+    st.title("Hugging Face SQL Generator")
+
+    # Get user input
+    prompt = st.text_area("Enter your prompt:")
+
+    if st.button("Generate SQL"):
+        # Call a function to generate SQL using the Hugging Face model
+        sql_result = generate_sql(prompt)
+
+        # Display the SQL result
+        st.write("Generated SQL:")
+        st.code(sql_result, language="sql")
+
+def generate_sql(prompt):
+    # Load the "NumbersStation/nsql-350M" model
+    model_name = "NumbersStation/nsql-350M"
+    model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+    # Tokenize and generate SQL
+    inputs = tokenizer(prompt, return_tensors="pt")
+    outputs = model(**inputs)
+
+    # Decode the generated SQL
+    sql_query = tokenizer.batch_decode(outputs["output_ids"], skip_special_tokens=True)[0]
+
+    return sql_query
+
+if __name__ == "__main__":
+    main()

requirements.txt

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+#requirements.txt
+
+streamlit
+transformers