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# app.py
import subprocess
# Install dependencies from requirements.txt
subprocess.run(["pip", "install", "-r", "requirements.txt"])
import streamlit as st
from transformers import AutoModelForCausalLM, AutoTokenizer
def main():
st.title("Hugging Face SQL Generator")
# Get user input
prompt = st.text_area("Enter your SQL 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 = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Tokenize and generate SQL
input_ids = tokenizer(prompt, return_tensors="pt").input_ids
generated_ids = model.generate(input_ids, max_length=500)
sql_query = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
return sql_query
if __name__ == "__main__":
main()
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